phd research proposal artificial intelligence

Artificial Intelligence Research Proposal

Artificial intelligence or AI is one of the latest technologies being used in integration with machine learning, deep learning, and deep reinforcement learning. Developers and software designers craft solutions to some of the important problems in AI . Data or information in the form of digital satellite images, visual data, structured unstructured, and text data. Artificial intelligence research proposal writing needs expert advice since the field is extensively growing in research and development.

  Robust data and proper algorithms to detect patterns are essential for the effective functioning of artificial intelligence systems . This article provides a complete picture of artificial intelligence projects where will start by defining the basics

What is Artificial intelligence?

• The aim of developing intelligent machines is the motive behind artificial intelligence
• It becomes one of the inherent and the most necessary parts of many sectors such as real-time applications (industry 4.0, smart city, and robotics)

Due to its growing importance, the research in artificial intelligence is increasing at large. With the help of our highly specialized and technically well-versed group of experts , you can surely produce the best artificial intelligence research proposal. One of the underlying problems in AI research respect the following characteristics of programming.

• Knowledge and perception
• Learning, planning, and reasoning
• Problem solving
• Manipulation capacity and object motion

Get in contact with us if you want to learn about the strategies used by our specialists to solve artificial intelligence research issues . The important technicalities will then be simply shared with you.

Types of Artificial Intelligence

The following are the most important types of artificial intelligence

• It is developed because of the demand from a large number of users and regulators
• The system is developed in such a way to learn and get trained from critical data sets while protecting the user privacy
• NLP strengths are incorporated or utilized by collaborative AI for superior and advanced manipulation
• Diverse testbed is also it’s characteristic
• This model of artificial intelligence helps in improving its adoption and deployment
• It is built over the trusted brand of Singapore
• Incremental learning of artificial intelligence systems automatically is a characteristic feature of this model
• Artificial general intelligence can in turn be enhanced using this model
• The datasets used in this kind of AI is highly qualitative
• Here nearly a small data set comparable to a small country is utilized

We will do a comparative analysis of all of these topic areas so that you can easily identify the subject study that best meets your demands. We support the wide interpretation of liberty of researchers , which we believe will lead to many of the improvements that society requires.

As a result, we encourage our clients to do in-depth research on any topic on their own and act as a facilitator to them. Then, if necessary, we will examine their thoughts and assist them in selecting the most interesting study topic or writing a artificial intelligence research proposal . Let us now look into the applications of AI below

Artificial Intelligence applications

AI has a lot of applications in diverse fields. It is the technology of the present that has huge potential to become the only technology of the future. In this regard, we have a look into the applications of AI below.

• AI is well known for its use in smart cities and other smart applications like finance, health, transport, and justice delivery

With its capacity to supplement human intelligence and capacities , AI intelligence and human abilities are now getting integrated to produce greater results

• Artificial intelligence is integrated into light detection and ranging systems called LIDAR (combines radar and light) for advanced details during navigation and avoiding Collision

For further explanation on all such technicalities, you can contact us. We can also assist you with any research needs you may very well have. We believe that present techniques should be questioned. This is because we think that only by asking questions can we have a better knowledge of what we’re talking about, and only then can we create optimal technologies .

Latest Research Topics in Artificial Intelligence

The following are some of the important and recent artificial intelligence research topics

• Pattern recognition and expert systems
• Artificial neural networks and natural language processing
• Robotics and genetic algorithm
• Machine learning and computer vision
• Automated reasoning and complex systems
• Intelligent search engine, control, and data mining

We intended to influence society by guiding prospective research at a fair cost in all the above topics . As a result, we supply you with a variety of additional services that you will require during your study. Artificial intelligence Dissertation , as you may know, is made feasible by mathematical operations conducted on digital forms of signals utilizing complex algorithms.

Artificial Intelligence Technologies List

• Deep learning image recognition and computer vision skills are utilised by the vehicles for self-driving
• It can intelligently avoid Collisions and unexpected obstacles and it can also pilot a given vehicle by staying in a particular lane
• Robotics engineering field whose primary aim is to manufacture and develop advanced robots
• Automation is involved in developing autonomous mechanisms and systems
• Machine vision is the technology that allows the computers and other devices to have vision
• Machine learning enables a computer to work on its own without getting programmed for each and every aspect
• By NLP you can process the human words and languages using computers

All these AI-based methods and systems are built on the foundations of coding and mathematics . The programming frameworks and simulation techniques related to AI ought to have been obvious to you. You can also feel free to get in touch with our experts at any time concerning these methodologies. Let us now look into the research proposal format.

Format of a research proposal

The following are the important aspects of a research proposal,

• A suitable and unique title to a topic can attract the reader’s attention
• You need to highlight the important points with regard to the background of the topic and the field development timeline
• Research has to be well establish in depth investigation for providing evidence
• The proposed methods and techniques have to be clearly mentioned along with their merits and demerits of the existing works.
• A detailed working plan along with the timeline has to be developed and your research must be scheduled in line with it
• Sources used in proposal writing must be properly acknowledged in bibliography
• You can also include a reference section in place of bibliography

It is now really important that you have a clear and expert view of the various aspects of artificial intelligence proposal in great detail. Because attempting to write a research proposal by knowing all its necessities in prior can help you get the best outcome. So latest now have a look into every aspect of artificial intelligence research proposal in great detail in the following sections

Conduct Preliminary research

To choose one of the best topics, you need to have preliminary research. Make sure that you look into all the aspects of atopic and choose the most specific issue in place. This helps you to focus your research proposal on the right track. You can get all the books, benchmark references, journals, and authentic websites for collecting information regarding your research objective from us. Make sure to look into both pros and cons of your topic. Consider the following points during preliminary research

• Points that are overlooked by the readers in your research sources
• Potential debatable topics to be addressed
• Your stance over the topic
• Recent breakthroughs in your field

You must include explanations from reliable sources on all these points in your proposal. We provide you with the essential support and motivation to conduct research and complete your artificial intelligence research proposal successfully. We are well versed in the proposal format of all the universities of the world.

To formulate research questions, you can use the phrase ‘I want (or attempted) to know what (why or how) of the problem’ so that it looks standard. Let us now have some more ideas on the topic being selected.

Discovering, narrowing and focusing a researchable topic

• The most interesting topic according to you have to be selected
• Then you need to attempt to write all about the topic in your way
• Have interactions with your peer groups and course instructor
• Finally given up your topic in the form of a question which you should proclaim to address in the proposal

In addition, a topic with potential and reliable reference sources can help you to a greater extent. Here we assist you in fetching advanced research materials and data for any novel topic of your interest. As we have established associations with the world’s top researchers and experts , we can bring any kind of materials for your research at your disposal . Reach out to us for all such most needed research assistance. Let us now look into source selection,

Finding, selecting and reading sources  

As you start looking for the standard sources for your artificial intelligence research proposal we insist you give priority to the following sources

• Standard primary and secondary sources of references
• Limitations, research gaps, and drawbacks of existing methods

You can get the necessary practical explanations along with the massive reliable data from our research guidance facility. With world-class certified developers, writers, and engineers you can get a greater insight into all aspects of computer simulation and artificial intelligence from us. Let us now look into the ways of documenting the collected information.

Grouping, sequencing and documenting information

When you are working to present and document the data collected you must make proper grouping and sequencing of them.

For all formatting and editing guidance, you can check out our website. We are offering one of the best artificial intelligence research proposal writing guidance with highly qualified and experienced writers of the world. We ensure to offer customized online research support 24×7 . Let us now see about writing an outline and a prospectus.

Writing an Outline of Research Proposal

The following are the important questions to be dealt with in your research proposal

• The topic to be dealt
• Significance of the topic
• Relevant background knowledge and material
• Problem statement along with its purpose
• Plan of the organization to support the statement to its best

By ensuring multiple grammatical checks and confidential research support we become the most reputed and trustworthy research guidance providers across all the countries. Also, you can expect complete support from our side concerning assignment writing, paper publication , and survey and conference paper writing , and so on. Let us now talk about writing an introduction,

How to write the introduction section?

The following aspects have to be included with huge importance in your research proposal introduction

• All the important points concerning background and context materials
• Necessary terms and concepts definition
• Proper explanations on the focus and purpose of the research proposal
• Plan of organization has to be revealed perfectly

To better understand the style of writing, you can look into the standard examples of the best and successful research proposals that we guided. We have more than two decades of experience in artificial intelligence research. So our experts are capable of solving all the research issues, problems, and concerns of it . Let us now talk about writing the body of the proposal.

Writing the body

The following are all the important points to be remembered while writing the body of a research proposal

• Develop your proposal in and around your topic
• The sources should not direct your proposal whereas the search of sources have to be in line with your objective
• Integration of the sources and discussion must be given prime importance
• Summarising, analyzing, explaining, and evaluating the published work is more important than making a report of it
• Make sure to include the generalized and specific points about the topic

To include the authentic research data in the body of your artificial intelligence research proposal , readily contact our technical experts. We also provide all necessary help in the successful implementation of accurate codes and writing respective algorithms . Let us now talk about the research proposal conclusion

Writing the conclusion

• In case of complexities in the proposal you are expected to provide a summary
• The importance of findings and observations has to be recorded even before the conclusion part. In cases when such points are missed out, you can add and explain their significance at the end
• In the finishing stage, from being more specific you need to shift towards a generalized approach in line with the introduction
• At last, the scope for further research in the future gives a good frame to your proposal

Get to read the best conclusions from our website. An artificial intelligence research proposal is one of our major services through which we have delivered more than 300+ Artificial Intelligence Projects in the field. With the highly experienced technical team and engineers, we are providing experimentation and Research support to our customers. We will now discuss the important aspects of the experimentation section

Experimental section

• The simulation tools being used have to be introduced and explained properly
• Proper configuration details of the software and hardware are essential
• Description of the data sets have to include their links, attributes, and analysis
• Latest years of papers from authentic sources like Elsevier, Springer, and IEEE
• At least from 50+ papers, doing the literature works
• Clear definition of the performance parameters and metrics can fetch you more credibility
• Graphical and tabular comparative analysis attach the visualization aspect to your study
• Summarization of the result has the potential to retain your study in the mind of the reader

As we mentioned earlier, having a better idea of the simulation tools, techniques, platforms, and software becomes highly significant to conduct the best research. Our experts update themselves regularly to provide advanced technical assistance to you. Let us now see the criteria for writing the best thesis

What are the important criteria for the best proposal?

The best proposal is expected to consist of the solutions and answers to all the following questioning aspects

• Ability to arrive at the result at times of less resolution and quality of data
• Comparatively the ability to solve data processing parameter trade-offs efficiently
• Enhancing accuracy when the training videos and proper guidance is not available to carry out the testing
• Proper explanation for scalability of your system
• Proper statistical information at the introduction with real-time examples
• Unique and many advanced features are expected to be a part of the proposal
• The number and quality of testing features under consideration

For proper technical notes and standard reference sources in order to holistically cover all the above aspects, you can talk to our experts. Let us now have a look into scalability and the aspects of data sets below

• Scalability must handle very large datasets to provide greater accuracy and efficiency
• For this purpose, during testing make sure that you use a large number of servers, users, and devices
• The real-time examples, applications, and innovations have to explain in a easy to comprehend manner
• Evaluating the datasets by comparing only two of them might not be sufficient
• Along with artificial datasets evaluation becomes more standardized
• Dimensionality, noise level, outliers, and data size are the important aspects that can potentially impact your proposal
• Computation of all metrics have to be explained properly
• Number of metrics under consideration were taking up more than six metrics and parameters are recommended

By providing multiple revisions and professional proposal writing guidance they have been rendering excellent expert aid in artificial intelligence research proposal . Zero plagiarism and on-time delivery are our mottos. Get in touch with us to get guidance from the world’s best research experts.

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Artificial Intelligence Enabled Healthcare MRes + MPhil/PhD

London, Bloomsbury

Artificial Intelligence (AI) has the potential to transform health and healthcare systems globally, yet few individuals have the required skills and training. To address this challenge, our Centre For Doctoral Training (CDT) in AI-Enabled Healthcare Systems will create a unique interdisciplinary environment to train the brightest and best healthcare artificial intelligence scientists and innovators of the future.

UK tuition fees (2024/25)

Overseas tuition fees (2024/25), programme starts, applications accepted.

Applications closed

The Centre for Doctoral Training recruits in at least two rounds. Applicants are advised to apply early, priority will be given to those who have applied in round one.

• Entry requirements

A minimum of an upper second class honours undergraduate degree, or a Master's degree in a relevant discipline (or equivalent international qualifications or experience). Our preferred subject areas are Physical Sciences (Computer Science, Engineering, Mathematics and Physics) or Clinical / Biomedical Science. Applicants with a clinical background or degree in Biomedical Science must be able to demonstrate strong computational skills. You must be able to demonstrate an interest in creating, developing or evaluating AI-enabled Healthcare systems.

The English language level for this programme is: Level 2

UCL Pre-Master's and Pre-sessional English courses are for international students who are aiming to study for a postgraduate degree at UCL. The courses will develop your academic English and academic skills required to succeed at postgraduate level.

Further information can be found on our English language requirements page.

If you are intending to apply for a time-limited visa to complete your UCL studies (e.g., Student visa, Skilled worker visa, PBS dependant visa etc.) you may be required to obtain ATAS clearance . This will be confirmed to you if you obtain an offer of a place. Please note that ATAS processing times can take up to six months, so we recommend you consider these timelines when submitting your application to UCL.

Equivalent qualifications

Country-specific information, including details of when UCL representatives are visiting your part of the world, can be obtained from the International Students website .

International applicants can find out the equivalent qualification for their country by selecting from the list below. Please note that the equivalency will correspond to the broad UK degree classification stated on this page (e.g. upper second-class). Where a specific overall percentage is required in the UK qualification, the international equivalency will be higher than that stated below. Please contact Graduate Admissions should you require further advice.

About this degree

Every student who is accepted onto the AI-enabled Healthcare Systems Centre for Doctoral Training (CDT) must take the MRes Artificial Intelligence Enabled Healthcare in their first year. This will be followed by a 3 year PhD. Throughout this period the CDT will continue to closely monitor the need for continuing training and support, tailored to each student, and provide ongoing training in research skills. The MRes is not currently available as a stand-alone programme.

The MRes programme covers the core competencies of artificial intelligence and has a central emphasis on how healthcare organisations work. Ethical training for medical artificial intelligence will be explicitly emphasised alongside a broader approach to responsible research, innovation and entrepreneurship.

During the MRes year, students will learn the statistical underpinnings of machine learning theory, get a practical grounding in research software engineering and the principles of healthcare and medical research, as well as a thorough treatment of topics in machine learning, advanced statistics and principles of data science.

As part of the MRes, alongside the core and elective modules, you will complete a substantial Masters-level project of your choice, working with a supervisory team that will normally include a clinician and an academic. The project you work on during your MRes normally leads to the chosen PhD research topic.

The remaining years will be more like a traditional PhD, which leads to the presentation of a PhD thesis at the end of the fourth year. During your PhD you will remain involved in CDT activities and will continue to work closely with relevant health professionals and clinical teams through our NHS partners and leading academics at UCL.

As a cohort based PhD programme, students will also have the opportunity to participate in a range of seminars, training programmes, placements and other activities, including UCL's Doctoral Skills Development Programme.

Training Opportunities The CDT programme consists of a range of activities and events including:

• A Mini-MD programme where trainees undertake an immersive clinical experience within an NHS setting
• Annual CDT Conference
• Seminar series
• PPI Training
• Responsible Research & Innovation
• Communication Skills
• Entrepreneurship
• Ethical Training
• The opportunity to attend training programmes offered by the Alan Turing Institute
• Opportunities for internships and placements with industry partners

More information can be found on the CDT Website .

Who this course is for

The Centre for Doctoral Training programme is for students with an interest in creating and developing AI solutions aimed to transform and solve healthcare challenges. The CDT programme is embedded within a NHS setting, and should appeal to students keen to develop clinical knowledge and algorithmic/ programming expertise.

What this course will give you

• Benefit from UCL's excellence both in computational science and biomedical research innovating in AI;
• Be supervised by world-leading clinicians and AI researchers in areas related to your research;
• Work within a real-world setting, embedded within hospitals, allowing you to gain a practical understanding of the value and limitations of the datasets and the translational skills required to put systems into practice;
• Have the opportunity to not only apply AI to healthcare but to apply healthcare to AI, generating novel large-scale open datasets driving methodological innovation in AI;
• Become a future leader in solving the most pressing healthcare challenges with the most innovative AI solutions;
• Study at UCL, which is rated No.1 for research power and impact in medicine, health and life sciences (REF 2021) and 9th in the world as a university (QS World Rankings 2024).

The foundation of your career

We do not yet have any graduates from the four-year programme, our first cohort of students will be graduating over the next few months. We expect them to stay within the field of AI and healthcare, and much like previous graduates from our experienced CDT supervisors, they will go onto successful careers in academia and industry. 

Employability

The distinctive characteristics of our programme allow us to produce graduates who are prepared to:

• engineer adaptive and responsive solutions that use AI to deal with complexity;
• innovate across all levels of care, from community services to specialist hospitals;
• be comfortable working with patients and professionals, and responding to their input;
• appreciate the importance of addressing health needs rather than creating new demand.

The Institute's research departments collaborate with third-sector and governmental organisations, as well as members of the media, both nationally and internationally to ensure the highest possible impact of their work beyond the academic community. Students are encouraged to do internships with relevant organisations where funding permits. Members of staff also collaborate closely with academics from leading institutions globally.

Teaching and learning

Various teaching and learning methods are employed to facilitate effective learning and cater to different learning styles. Below are some common types of teaching methods that may be used across the programme:

Interdisciplinary Teaching: Interdisciplinary teaching involves integrating knowledge and skills from multiple disciplines or subject areas to provide a comprehensive understanding of a topic, particularly AI and healthcare. This approach encourages students to make connections between different subjects and fosters critical thinking and problem-solving abilities.

Lecture-Based Teaching: Lecture-based teaching is a traditional method where the instructor presents information to students through spoken words. It involves the teacher sharing knowledge, concepts, and theories, while students take notes and listen actively. This method is effective for conveying large amounts of information and providing foundational knowledge.

Practical Coding Sessions: Practical coding sessions are hands-on learning experiences where students actively engage in coding exercises, programming tasks, and problem-solving activities. These sessions are essential for AI and programming-related subjects (machine learning, etc) as they allow students to apply theoretical knowledge to real-world scenarios.

Interactive Teaching: Interactive teaching methods encourage active participation and engagement from students. These methods can include discussions, debates, group activities, and case studies, in particular in several modules such as Journal Club. Interactive teaching fosters collaboration, communication skills, and a deeper understanding of the subject matter.

Project-Based Learning: Project-Based Learning involves assigning students long-term projects that require them to investigate and address real-world problems or challenges (such as AI & healthcare group project). It enhances critical thinking, research skills, and creativity while promoting independent learning and teamwork.

Collaborative Learning: Collaborative learning involves students working together in small groups or pairs to solve problems, discuss ideas, and complete tasks. This method promotes teamwork, communication, and the exchange of diverse perspectives.

The use of these teaching/learning methods can vary depending on the subject matter, the goals of the programme, and the preferences of the instructors in the MRes year. Our educational programme incorporates a mix of these methods to cater to the diverse needs of learners and create a well-rounded learning experience.  

Compulsory Modules:

CHME0033 Dissertation in Artificial Intelligence Enabled Healthcare

CHME0032 Healthcare Artificial Intelligence Journal Club

Optional Modules

CHME0012 Principles of Health Data Science

CHME0013 Data Methods for Health Research

CHME0015 Advanced Statistics for Records Research

CHME0016 Machine Learning in Healthcare and Biomedicine

CHME0031 Programming with Python for Health Research

CHME0034 Computational Genetics of Healthcare

CHME0035 Advanced Machine Learning for Healthcare

CHME0039 Artificial Intelligence in Healthcare Group Project

COMP0084 Information Retrieval and Data Mining

Please note that the list of modules given here is indicative. This information is published a long time in advance of enrolment and module content and availability is subject to change.

Assessment methods are crucial components of an educational programme, as they evaluate students' understanding, knowledge, skills, and application of concepts. Here are various types of assessment methods that may be used across the programme:

Exams: Traditional exams are a common assessment method that tests students' knowledge and understanding of the course material. These exams typically involve a time-bound written assessment, where students respond to questions related to the subject matter.

Open-Book Exam: In an open-book exam, students are allowed to refer to their textbooks, notes, or other resources during the assessment. The questions in these exams are often designed to test higher-order thinking and problem-solving abilities, as students have access to reference materials.

Coursework: Coursework assessments involve various assignments, essays, reports, or projects that students complete throughout the course. These assessments may cover specific topics or practical applications and help to assess students' comprehension and critical thinking skills.

Coding Exam: A coding exam is specifically designed for courses related to computer science, software development, or programming. Students are given coding challenges or programming tasks that assess their coding proficiency and problem-solving abilities.

Collaborative Project: In a collaborative project assessment, students work in groups to tackle a complex problem or complete a substantial task. This assessment measures teamwork, communication, time management, and the ability to achieve shared goals.

Presentation and Q&A: Presentations require students to deliver a talk on a given topic or project. The presentation assesses their ability to communicate effectively, organize information, and present ideas coherently. Often, a question and answer (Q&A) session follows the presentation to delve deeper into the topic.

Research Proposal: A research proposal is a preliminary plan for a research project that students submit to demonstrate their research capabilities. It outlines the research question, objectives, methodology, and potential outcomes of the study.

Dissertation Writing: Dissertation writing is typically reserved for higher education levels, such as undergraduate and postgraduate studies. It involves an extended research project on a specific subject, allowing students to demonstrate research, analytical, and academic writing skills.

Online Quizzes and Tests: Online quizzes and tests are digital assessments that may be used for formative or summative purposes. They are often employed in blended or online learning environments.

The use of assessment methods will vary based on the nature of the programme, the subject matter throughout the MRes year. A well-balanced combination of assessment types ensures that students' diverse abilities and learning styles are appropriately evaluated while providing a comprehensive understanding of their progress and achievements.

During the MRes 4 hours of a student's time is spent in tutorials per week and/or, 6-8 hours in lectures per week, and a further 20-24 hours in independent study per week.

Research areas and structure

• AI-enabled diagnostics or prognostics
• AI-enabled operations
• AI-enabled therapeutics
• Public Health Data Science
• Machine Learning in Health Care
• Public Health informatics
• Learning health systems
• Electronic health records and clinical knowledge management
• e-health and m-health
• Clinical Decision Support Systems

Research environment

Our research environment offers a unique degree programme that stands out among competitors. We provide students with the exceptional opportunity to explore the cutting-edge intersection of AI technology and healthcare applications. Our curriculum emphasizes research and innovation skills, empowering students to become independent researchers and adept problem solvers. A key difference is our close collaboration with clinicians and front-line practitioners. This interaction fosters a holistic understanding of healthcare challenges and real-world applications, ensuring that our graduates are equipped with practical knowledge and solutions. Our programme is inclusive, welcoming students from both computational and clinical backgrounds, creating a diverse and dynamic learning environment.

Students studying the programme full-time will be expected to complete 180 credits during the academic year. 

Students studying the programme part-time will be expected to complete 180 credits across two academic years. 

Accessibility

Details of the accessibility of UCL buildings can be obtained from AccessAble accessable.co.uk . Further information can also be obtained from the UCL Student Support and Wellbeing team .

Fees and funding

Fees for this course.

Fee description	Full-time	Part-time
Tuition fees (2024/25)	£6,035	£3,015
Tuition fees (2024/25)	£31,100	£15,550

The tuition fees shown are for the year indicated above. Fees for subsequent years may increase or otherwise vary. Where the programme is offered on a flexible/modular basis, fees are charged pro-rata to the appropriate full-time Master's fee taken in an academic session. Further information on fee status, fee increases and the fee schedule can be viewed on the UCL Students website: ucl.ac.uk/students/fees .

Additional costs

All studentships include a research training support grant, which covers additional research costs throughout students' time on the programme.

For more information on additional costs for prospective students please go to our estimated cost of essential expenditure at Accommodation and living costs .

Funding your studies

Please visit the CDT website for funding information.

For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarships and Funding website .

Note for applicants: When applying on UCL Select, please select MRes Artificial Intelligence enabled healthcare to apply for programme.

Please note that you may submit applications for a maximum of two graduate programmes (or one application for the Law LLM) in any application cycle.

Got questions? Get in touch

Institute of Health Informatics

[email protected]

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• Corpus ID: 209436745

PHD PROPOSAL IN ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

• Published 2019
• Computer Science

25 References

Large-scale machine learning and applications, a survey on transfer learning, distributed gaussian processes, deterministic execution on gpu architectures, exact gaussian processes on a million data points, a loewner-based approach for the approximation of engagement-related neurophysiological features, the worst-case execution-time problem—overview of methods and survey of tools, electrocardiogram generation with a bidirectional lstm-cnn generative adversarial network, pgans: personalized generative adversarial networks for ecg synthesis to improve patient-specific deep ecg classification, supplementary for: deep learning with convolutional neural networks for eeg decoding and visualization, related papers.

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PhD Research Proposal Artificial Intelligence

One of the most important subject areas of computer science is Artificial Intelligence. It provides a wide platform for building a machine with learning capabilities . Artificial intelligence makes machines think and react similarly to humans in uncertain situations. In other words, this machine intelligence to behave artificially like human intelligence is known as artificial intelligence. This page is intended to present you useful information on PhD Research Proposal Artificial Intelligence along with the latest research areas, technologies, challenges, trends, techniques, and ideas !!!

Assume that there is a situation in which a human is performing a particular task by learning and understanding the event to solve associated problems. This human task is performed by machine artificial learning abilities are known as artificial intelligence.

For instance: a self-driving car without a driver. In this, the vehicle monitors the environment and takes effective decisions for secure destination attainment.  

What are the requirements for good research proposal writing? 

We believe that we made you are clear with the exact purpose and importance of artificial intelligence at this moment. Now, we can see about the requirements of good PhD Research Proposal Artificial Intelligence . Basically, the writing of PhD proposal needs more concern and study to create a qualified proposal. Since it is the reflection of your research activities and efforts in the form of valuable words. Here, we have given you a few important tips to prepare good proposal writing.

• Need to be adaptable to access required information and resources
• Need to be meet the expected standard and enhance interest to read
• Need to be original to create a new contribution to the handpicked research area
• Need to be related with your degree and present research areas of artificial intelligence

In general, the PhD research proposal has a standard format to write. As well, it is composed of different components such as title, abstract, introduction, literature study, methodologies, conclusion, and references. In fact, we have a native writer team to give complete assistance in perfect proposal writing. Further, we also help you in literature review writing, paper writing, and thesis writing. Here, we have given you a few important things that need to be focused on while writing PhD Research Proposal Artificial Intelligence.   

What are the Components of a Good Research Proposal? 

• Give a short and crisp title for your research proposal
• Choose a title that addresses your research problem and proposed solutions
• Provide a summary of your research work
• Act as detailed synopsis that answers why, how, and what questions of your research
• Present your selected research area and research problem(s)
• Highlight the significance of your study
• Provide sufficient hypothesis of research
• Mention the methodologies that going to be used as solutions
• Talk about the review of secondary research materials
• Address the identified research gaps in previous related studies
• Do a comparison of techniques and arguments in existing researches
• Describe the contribution and findings of the previous research
• List the merits and demerits of existing research works
• Present system architectural design
• Give a detailed explanation on used research tools and techniques/methodologies
• Speak about the need and importance of choosing those methodologies
• Explain the numerical formulas and used algorithms
• Give justification for your proposed research methodologies
• Mention in what way your research methodologies solve your research problem
• Again give an overview of your research
• Point out the objectives and importance of your research
• Encapsulate all highlights of your research in brief
• A present unique point of your study
• Overall, write nearly two paragraphs
• Provide citation of your referred research websites and books
• Implicitly these references mention your supportive hypothesis
• Narrow down your wide research sources
• Smart picking of research materials will impress the research committee

We hope that you are clear with the fundamentals of writing a good PhD research proposal artificial intelligence . Now, we can see about the three primary research terms of artificial intelligence. Since these terms are most widely used in many research areas of artificial intelligence.  As well, it is categorized into three classifications such as, 

• Exploration Areas
• Real-Time Applications

Our researchers are good at proposing modern research work in upcoming research areas for smart applications . If you are interested to know more research ideas from the following classifications, then make an online or offline connection with us.   

What are three important terminologies in Artificial Intelligence? 

• Genetic Evolutionary
• Logical Rationalism
• Molecular Biological
• Statistical Empiricism
• Neural Connectionism
• Smart System Design
• Learning Approaches
• Inference Mechanism
• Knowledge Representation
• Expert System
• Electronic Commerce
• Bioinformatics
• Intelligent Robots
• Natural Language Processing
• Information Retrieval
• Data Mining

In addition, we have also given you some significant research areas of artificial intelligence . We assure you that all these areas are recognized in current AI research topics and ideas. 

Moreover, we also support you in other important research ideas to support you in all aspects of artificial intelligence . By the by, our first and foremost task in AI research is identifying your interesting research area. Then, we provide you list of the latest research notions and phd topics in artificial intelligence .

Research Areas for PhD Research Proposal Artificial Intelligence

• Reinforcement Learning
• Supervised Learning
• Unsupervised Learning
• Dialogue Systems
• Natural Language
• Understanding
• Recognition
• Classification

Furthermore, we have given you a few important supporting AI technologies. Due to the beneficial impact of AI, it is employed and demanding in several research fields (i.e., other technologies). For your information, here we have given you only a few of them. Once you connect with us, we let you know more about up-to-date research topics of your selected technologies . Specifically, these technologies are currently successful in creating real-time AI applications for the development of a smart society.

Converging Technologies of AI 

• Internet of Things
• Big Data Analytics
• Blockchain Technology
• Lightweight Cryptography
• Cloud Computing
• Software-Defined Networking
• Fog Computing
• 6G Networks
• Industry 4.0
• UAV Communication
• Autonomous Vehicles
• Edge Networks

As a point of fact, AI is treated as the shared technology which used to solve different problems in different technologies. So, it can be recognized in many real-time applications and services. Although this field has so many developments in real-time applications, it has some technical issues that arise in the time of development and deployment . For your reference, here we have listed a few important technical issues of AI in recent research.      

Artificial Intelligence Research Problems 

• Optimized Modern Parameters
• Non-linearity from learning to compensate
• Hard-to-Model Issues
• Knowledge and Learning Representation
• Solution for Computational Infeasibility
• Computationally Understanding Solutions
• Training Policies

Already, we have seen converging technologies of artificial intelligence in an earlier section. To the continuation, now we can see about the current trends of AI. In order to identify these trends, our research team has studied the present and past 2-3 years’ research articles and magazines. Through this review, we analyzed and identified

1) Research gaps that need to address

2) Problems that need enhanced solutions than existing one

From this collection, we have listed only a few of them for your reference. Further, we are also ready to share more trends that are sought by active research scholars in the field of artificial intelligence.    

Artificial Intelligence Current Trends

• Mainly in sustainable developments, energy usage has a key player role
• Provides productive communication plans for improving energy-efficiency
• Support significant services in 6G communication
• Human-sensed data are composed with 5D services to enhance the holographic communication
• Assure high QoS, precision, deterministic in 6G communication
• Need tremendous data rates like Tb/s
• Currently, manufacturing industries are moving towards automation technologies and precision communication
• In this, 6G is assured to give ultra-low delay and ultra-high reliability
• For real-cases, the general data transmission need industrial networks for low latency jitters
• For achieving a secure environ, wireless technologies, IoT and fog-cloud computing are advancing over global sustainability and QoS
• Presently, the 6G network understands 3D communication to enhance several applications like smart transportation, smart cities, smart healthcare, etc.
• For instance – Self-driving vehicles delay < 1ms and reliability > 99.999% for fast decisions over sudden accidents

Now, we can see emerging techniques that play a major role in bringing effective research solutions for different current research problems. As a matter of fact, our developers are proficient-enough to identify the best-fitting research techniques and algorithms for any sort of research problem .

In the case of complications in solving problems, our developers analyze the degree of problem complexity and create hybrid technologies or new algorithms accordingly. Overall, we are good to tackle the problem at any level of complexity in smart ways. Also, we suggest key parameters and development tools that enhance your system performance.   

Latest Techniques in AI 

• Generally, the data are collected from different formats, mode representations and sources
• Merging all these dissimilar data in one place is a tedious task
• For the data fusion, advanced neural networks and bayesian learning is used
• For instance – CNN, RBM, and DBM
• Through sensors, collect raw data and transfer it into high-computational devices for data processing
• This may cause more power usage and high traffic load over the network
• So, it is required to design a system that minimizes load and power usage without losing vital information
• Utilize ANN and perform preprocessing
• Also, network topology and architecture are required to be chosen appropriately for add-on benefits
• Prevent interference for primary user benefits through spectrum sensing
• The significant role of the primary user is to transmit data between secondary users and the succeeding layer
• This process is executed by Cooperative Spectrum Sensing (CSS) with high power usage
• The power usage increases because of report findings and spectrum sensing with respect to a centralized location
• Similarly, Convolutional Neural Network is utilized in Deep Corporate Sensing

Additionally, we have given you some growing ideas about artificial intelligence. These ideas are selected from different trending research areas that gain more attraction from the research community. If you have your own ideas to implement an artificial intelligence project, then we support you to upgrade your idea to match the latest advancements of artificial intelligence. So, create a bond with us, to know new interesting PhD research propsoal artificial intelligence . Overall, we give assistance on not only these ideas but also beyond this list of ideas.   

Emerging Ideas on AI 

• Artificial Intelligence for Internet of Things
• Privacy-Aware AI-assisted Edge System for Trustable Services
• Fast AI Services Migration from Cloud into Edge
• Secure Data Dissemination on AI-assisted Edge Systems
• In-depth Learning Services over Edge Network
• Energy-Aware AI-assisted Edge System for Quality of Services
• Real-time AI-assisted Edge Systems with Optimized Solutions
• Edge-intensive Distributed / Collaborate / Federated Smart Services

On the whole, we are here to update you about the recent research updates of artificial intelligence in every possible area. Particularly, we help you in research problem selection, corresponding solutions selection, PhD Research Proposal Artificial Intelligence Writing, code development, paper writing, paper publication, and thesis writing. So, think smartly and hold your hands with our technical experts to shine your AI research career.

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PhD in AI Curriculum (Jan 2021 onwards)

• MHRD PhD students may decide the supervisor at the time of joining or by the end of first semester. For a research project-funded position, the faculty executing that research project himself/herself will be the guide.
• Phd students have to maintain a minimum CGPA of 7 from their course work.
• PhD studnets are required to give a comprehensive exam within 12 months of joining (maximum 2 chances to pass comprehensive exam).
• After passing the comprehensive exam, the student has to present the research proposal seminar (RPS) within 3 months. RPS for regular and direct PhD students should be within 18 months of registration.
• The guide will constitute a Doctoral Committee (DC), and DC would conduct regular meetings to evaluate the progress of the work.
• Following syllabus holds for part time PhD as well
• Electives not in given lists can be considered with approval of faculty advisor

PhD candidates joining after M.Tech or Equivalent degree

• Need to complete 12 credits of coursework in 1 year with 6 credits of mandatory courses (AI5000 and ID2230/CS6013).
• 6 credits of electives can be any course from the elective basket.

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Curriculum (first year)		Credits
Course Name	Credits
AI5000	Basics/Foundations of Machine Learning	3 (Core)
ID2230/CS6013	Advanced Data Structures and Algorithms	3 (Core)
XXxxxx	Electives (basket below)	6
	Total	12

PhD candidates joining after BTech/BE/MSc/Equivalent Degree in any discipline (aka Direct PhD candidates)

• Need to complete 24 credits of coursework in 1 year with 6 credits of mandatory courses (AI5000 and ID2230/CS6013).

-->

Curriculum (first year)		Credits
Course Name	Credits
AI5000	Basics/Foundations of Machine Learning	3
AI5030	Probability and Stochastic Processes	3
AI5100	Deep Learning	3
EE5609	Matrix Theory	3
ID2230/CS6013	Advanced Data Structures and Algorithms	3
XXxxxx	Electives (baskets below)	10
	Total	24

AI Electives

Course	Credits
Intro to Statistical Learning Theory	1
Kernel Methods	1
Sequence Models	1
Bayesian Data Analysis	1
Non-linear Control Techniques	1
Optimisation Methods in Machine Learning/Convex Optimization	3
Information Theory and Coding	3
Stochastic Processes for Machine Learning	1
Introduction to Submodular Functions	1
Artificial Intelligence	2

Course	Credits
Information Retrieval	3
Natural Language processing	3
Data Mining	3
Text Processing	3
Computer Vision	3
Speech Systems	3
Image and Video Processing	3
Surveillance Video Analytics, Visual Big data analytics, Video content analysis	3
Computer Vision for Autonomous Vehicle Technology	3
Parallel & Concurrent Programming	3
Distributed Computing	3

Recent PhD Topics in Artificial Intelligence 2023

Artificial intelligence (AI) is expanding rapidly, and its applications are becoming more common in various sectors. As a result, researchers are always looking for new methods to improve AI algorithms and implementations. With the emergence of new technology and approaches, academics are researching novel artificial intelligence study subjects to progress the discipline even further.

This blog will look at the latest PhD research topics in artificial intelligence for 2023. These subjects include a combination of practical and theoretical challenges that might help influence the future of artificial intelligence. Let’s delve into AI research, from natural language processing to autonomous robots.

Introduction

What is PhD topic in Artificial Intelligence?

A PhD topic in Artificial Intelligence involves advanced research and exploration within the realm of AI. It encompasses a wide array of subjects, such as machine learning , natural language processing, computer vision, robotics, and neural networks selection of project topic introduction. Doctoral candidates delve into cutting-edge techniques, developing innovative algorithms and seeking novel applications to address complex challenges. These topics push the boundaries of AI, contributing to its growth and impact on various industries. From enhancing decision-making processes to enabling autonomous systems and tackling ethical considerations, AI PhD Topic selection paves the way for groundbreaking advancements shaping technology and society’s future.

However, some potential areas might be of interest and relevance in the field of AI in 2023. Keep in mind that these are speculative and that new trends may have emerged since my last update:

• AI Ethics and Fairness : With the increasing integration of AI in various domains, there’s a growing concern about ethical issues, bias, and fairness. Dissertation topics in English literature might focus on developing AI models that are more transparent, accountable, and unbiased.
• Explainable AI (XAI) : Explainability remains a crucial challenge in AI. Research in this area could explore methods and techniques to make AI models more interpretable and provide understandable explanations for their decisions.
• AI in Healthcare : AI has great potential to revolutionize healthcare . Research might delve into areas like medical image analysis, personalized treatment plans, drug discovery, and AI-assisted diagnostics.
• Natural Language Processing (NLP) : NLP continues to be a significant area of research. The focus could be improving language understanding, machine translation, sentiment analysis, and dialogue systems.
• Reinforcement Learning : Advancements in reinforcement learning have shown promise in various fields, such as robotics and gaming. Dissertation topics could explore more efficient algorithms and real-world applications.
• AI for Sustainability : AI can be critical in addressing environmental and sustainability challenges. PhD research might use interesting artificial intelligence (AI) topics to optimize resource management, climate modelling, and sustainability-driven decision-making.
• AI in Autonomous Systems : The development of autonomous vehicles and drones has accelerated, and research could focus on enhancing their safety, decision-making capabilities, and robustness.
• AI and Creativity : Exploring AI’s potential in creative domains like art, music, and storytelling could be a fascinating area of research.
• AI for Cybersecurity : As cyber threats evolve, AI can be leveraged to detect and mitigate attacks. Research might concentrate on building more robust and adaptive cybersecurity systems.
• AI and Internet of Things (IoT) : The integration of AI with IoT devices is becoming more prevalent. PhD research design might look into AI-enabled IoT applications, security concerns, and optimizing IoT systems using AI.

Remember that these are just general topics, and PhD research requires a more specific and well-defined research question within the chosen domain. To get the most recent and relevant information, I recommend checking the latest academic journals, conference proceedings, and university websites for updates on AI research topics in 2023.

• Check out our Sample Topic selection for the Project to see how the PhD topic selection is constructed.

Top 10 research topics for artificial intelligence in 2023

• Natural Language Processing (NLP)
• Computer Vision
• Deep Learning
• Reinforcement Learning
• Generative Adversarial Networks (GANs)
• Explainable AI (XAI)
• Autonomous Robotics
• Ethics in AI
• Quantum Computing for AI
• Edge Computing for AI
• Check out our study guide to learn more about PhD Topic selection. How do you choose a topic for your PhD research?

Important Points:

• Natural language processing (NLP) studies how computers perceive and interpret human language.
• Computer vision is the process of teaching machines how to recognize and understand pictures and movies.
• Deep learning is a subset of machine learning in which artificial neural networks are trained using massive volumes of data.
• Reinforcement learning is a sort of machine learning in which an agent is trained to make decisions based on incentives and penalties.
• GANs are a neural network that uses existing data to create new data.
• XAI aims to make AI more transparent and understandable to humans.
• Autonomous robotics is the development of robots that can function autonomously without human intervention.
• AI ethics is concerned with the proper development and application of AI technology.
• Quantum computing is an emerging field.

The 2023 PhD topics in Artificial Intelligence highlight the dynamic field’s growth and promise for revolutionizing industries and improving quality of life. The research emphasizes ethical AI, addressing bias, fairness, and transparency. Advancements in natural language processing make AI more accessible and intuitive. AI-driven approaches revolutionize decision-making, data analysis , and predictive modelling in healthcare, finance, and environmental sciences. Novel AI architectures, such as quantum-based and neuro-symbolic systems, demonstrate efficient algorithms and power.

Integrating AI in robotics and autonomous systems redefines machine interaction, with implications for automation, manufacturing, and transportation. Collaboration between academia, industry, and policymakers is crucial for responsible and ethical ai research topics for beginners in technology development.

About PhD Assistance

PhD Assistance , writers and researchers have extensive expertise in selecting the best topic and title for a PhD dissertation based on their specialization and personal interests. Furthermore, our specialists are drawn from international and top-ranked colleges in nations such as the United States, the United Kingdom, and India. Our authors have the expertise and understanding to choose a PhD research subject that is appropriate for your study and a catchy title that surely fits your research aim.

• Holmes, Wayne, Maya Bialik, and Charles Fadel. “Artificial intelligence in education.” Globethics Publications, 2023. 621-653. Doi: 58863/20.500.12424/4273108
• Bermejo, Belen, and Carlos Juiz. “Improving cloud/edge sustainability through artificial intelligence: A systematic review.”  Journal of Parallel and Distributed Computing (2023). Doi: 1016/j.jpdc.2023.02.006
• Cerchia, Carmen, and Antonio Lavecchia. “New avenues in artificial-intelligence-assisted drug discovery.”  Drug Discovery Today (2023): 103516. Doi: 1016/j.drudis.2023.103516
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PhD on artificial intelligence for renewable energy and sustainability

Fully-funded PhD in the area of artificial intelligence for renewable energy and sustainability.

Application deadline

Funding information.

A stipend of £19,000 for 22/23, which will increase each year in line with the UK Research and Innovation (UKRI) rate, plus Home rate fee allowance of £4,596 (with automatic increase to UKRI rate each year). The studentship is offered for 3.5 years. For exceptional international candidates, there is the possibility of obtaining a scholarship to cover overseas fees.

Supervised by

Dr Erick Sperandio Nascimento

Prof Prashant Kumar

Renewable energy sources have gained increased attention and investments from the industries, governments and society, such as wind, solar, and hydrological sources, to enable a more sustainable and yet economically feasible development. However, the building and operationalization of renewable power plants face a series of challenges that must be tackled in order to improve their adoption. One of the main challenges resides in the ability to accurately predict the meteorological parameters that influence the generation of wind and solar energy from shorter to longer term, which becomes even more challenging in the face of climate change.

Therefore, this project aims at researching, developing and building AI-based solutions that can support the development of more reliable and accurate weather forecasting systems applied to the prediction of solar and wind energy generation, extreme weather events forecasting and their effects, air quality and sustainability. Historical data from publicly available sources will be used, like surface weather stations, GDAS/ECMWF/Era5 and satellite data, among others, along with information about wind turbines and photovoltaic cells.

We seek for exceptional candidates that are willing to develop AI-based clean air solutions by researching and building cutting-edge approaches and techniques in the fields of deep learning, physics-informed and graph neural networks, spatial-temporal modelling, model explainability and interpretability, time series foundation models, physical modelling and data-driven approaches, among others, applied to the challenges related to the fields of renewable energies and sustainability.

The applicant will be directly involved with research activities in the Global Centre for Clean Air Research (GCARE) and the People-Centred AI Institute, both in the University of Surrey, having access to an amazing set of resources, infrastructure and people engaged to deliver world-class researches and technologies with a focus on the well-being of people and on the scientific and technological development of the academia, industry and society.

Related links

Eligibility criteria.

This studentship is open to UK and international candidates.

All applicants should have (or expect to obtain) a first-class degree in a numerate discipline (mathematics, science or engineering) or MSc with distinction (or 70% average) and a strong interest in pursuing research in this field.

Additional experience which is relevant to the area of research is also advantageous.

English language requirements

IELTS minimum 6.5 overall with 6.0 in writing, or equivalent.

How to apply

Applications should be submitted via the PhD Vision, Speech and Signal Processing programme .

In place of a research proposal you should upload a document stating the title of the project that you wish to apply for and the name of the relevant supervisor.

Studentship FAQs

Read our  studentship FAQs  to find out more about applying and funding.

03 March 2023

Contact details

Erick giovani sperandio nascimento.

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Artificial Intelligence and Data Analytics (AIDA) Group

Featured story.

Suggested PhD Projects

Here are some suggested topics for PhD projects from our group members. These projects are merely suggestions and illustrate the broad interests of the research group. Potential research students are encouraged and welcome to produce their own suggestions in these research areas or other research areas in the field. All applicants are invited to contact the academic associated with the project when making an application.

Machine Learning for the Pharmacology of Ageing

Contact:  alex freitas.

Recently, there has been a growing interest in ageing research, since the proportion of elderly people in the world’s population is expected to increase substantially in the next few decades. As people live longer, it becomes increasingly more common for a person to suffer from multiple age-related diseases. Since old age is the ultimate cause or the greatest risk factor for most of these diseases, progress in ageing research has the potential to lead to a more cost-effective treatment of many age-related diseases in a holistic fashion. In this context, researchers have collected a significant amount of data about ageing-related genes and medical drugs affecting an organism’s longevity – mainly about simpler model organisms, rather than humans. This data is often freely available on the web, which has facilitated the application of machine learning methods to the pharmacology or biomedicine of ageing, to try to discover some knowledge or patterns in such datasets. This project will focus on developing machine learning algorithms for analysing data about the pharmacology of ageing, i.e., data about medical drugs or chemical compounds that can be used as an intervention against ageing, mainly in model organisms. The broad type of machine learning method to be developed will be supervised machine learning (mainly classification), but the specific type of algorithm to be developed will be decided later, depending on the student’s interest and suitability to the target datasets. Note that, although this is an interdisciplinary project, this is a project for a PhD in Computer Science, so the student will be expected to develop a novel machine learning method. As examples of interdisciplinary papers on machine learning for ageing research, see e.g. (the first paper is particularly relevant for this project, whilst the second includes a broader discussion about machine learning for ageing research):

Relevant References:

D.G. Barardo, D. Newby, D. Thornton, T. Ghafourian, J.P. de Magalhaes and A.A. Freitas. Machine learning for predicting lifespan-extending chemical compounds. Aging (Albany NY), 9(7), 1721-1737, 2017.

Fabris, J.P. de Magalhaes, A.A. Freitas. A review of supervised machine learning applied to ageing research. Biogerontology, 18(2), 171-188, April 2017.

Machine Learning with Fairness-Aware Classification Algorithms

This project involves the classification task of machine learning, where an algorithm has to predict the class of an object (e.g. a customer or a patient) based on properties of that object (e.g. characteristics of a customer or patient). There are now many types of classification algorithms, and in general these algorithms were designed with the only (or main) goal of maximizing predictive performance. As a result, the application of such algorithms to real-world data about people often leads to predictions which have a good predictive accuracy but are unfair, in the sense of discriminating (being biased) against certain groups or types of people – characterized e.g. by values of attributes like gender or ethnicity. In the last few years, however, there has been a considerable amount of research on fairness-aware classification algorithms, which take into account the trade-off between achieving a high predictive accuracy and a high degree of fairness. The project will develop new classification algorithms to cope with this trade-off, focusing on classification algorithms that produce interpretable predictive models, rather than black box models.

[1] Friedler, A.A., Scheidegger, C., Venkatasubramanian, S., Choudhary, S., Hamilton, E.P. and Roth, D. A comparative study of fairness-enhancing interventions in machine learning. Proc. 2nd ACM Conf. on Fairness, Accountability and Transparency (FAT’19), 329-338. ACM Press, 2019.

[2] Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. A survey on bias and fairness in machine learning. arXiv preprint: arXiv:1908.09635. 2019.

Cognition-enabled lifelong robot learning of behavioural and linguistic experience

Contact:  ioanna giorgi.

Present-day cognitive robotics models draw on a hypothesised developmental paradigm of human cognitive functions to devise low-order skills in robots, such as perception, manipulation, navigation and motor coordination. These methods exploit embodied and situated cognition theories that are rooted in motor behaviour and the environment. In other words, the body of a physical artefact (e.g., a robot) and its interactions with the environment and other organisms in it contribute to the robot’s cognition. However, it is not clear how these models can explain or scale up to the high-level cognitive competence observed in human behaviour (e.g., reasoning, categorisation, abstraction and voluntary control). One approach to model robot learning of behavioural and cognitive skills is in incremental and developmental stages that resemble child development. According to child psychology and behaviour, conceptual development starts from perceptual clustering (e.g., prelinguistic infants grouping objects by colour) and progresses to nontrivial abstract thinking, which requires a fair amount of language . Thus, to solve the problem of modelling high-level cognitive skills in robots, language, in interaction with the robot’s body, becomes inseparable from cognition. This project is aimed at following a cognitive and developmental approach to robot learning that will allow robots to acquire behavioural and linguistic skills at a high level of cognitive competence and adaptation as humans. This learning should be lifelong : humans apply earlier-learned skills to make sense of continuous novel stimuli, which allows them to develop, grow and adjust to more complex practices. One such cognitive robot can be used across various themes: human-robot interaction using theory of mind (ToM) skills for robots, social robots and joint human-robot collaboration.

Note: The Cognitive Robotics and Autonomous Systems (CoRAS) laboratory at the School of Computing has access to several humanoids (NAO) and socially interactive robot platforms (Buddy Pro, Q.BO One, Amy A1), mobile robots (Turtlebot Waffle Pi, Burger), pet-like companion robots and gadgets like AR Epson glasses and Microsoft HoloLens.

Attention model for agent social learning during human-robot interaction.

Successful human-robot interaction requires that robots learn by observing and imitating human behaviour. The theory of learning behaviour through observation is referred to as social learning . Behavioural learning can also be enhanced by the environment itself and through reinforcement (i.e., establishing and encouraging a pattern of behaviour). One important component of such learning is cognitive attention , which deals with the degree to which we notice a behaviour. Cognitive attention renders some inputs more relevant while diminishing others, with the motivation that more focus is needed for the important stimuli in the context of social learning. Attention brings forth positive reinforcement (reward) or negative reinforcement (punishment). If the reward is greater than the punishment, behaviour is more likely to be imitated and reciprocated. In human-robot interaction, attention is crucial for two reasons: 1) to respond or reciprocate the behaviour appropriately during the interaction, and 2) to learn or imitate that behaviour for contingencies. This project is aimed at devising a cognitive attention model of a robot for social learning. The model will include memory, reasoning, language and multi-sensory data processing, i.e., “natural” stimuli during the interaction such as from vision, speech and sensorimotor experience. It can be based on a cognitive architecture approach or alternative computational approaches. The solution should ideally be encompassing multiple aspects of interaction (verbal and non-verbal), but it can also focus on such specific aspects (e.g., visual attention or intention reading).

How can a robot learn skills from a human tutor

Contact:  giovanni masala.

The aim of this project is to enhance robot learning from a human tutor, similar to a child who learns from a human teacher. The agent will develop the ability to communicate through natural language from scratch, by interacting with a tutor, recognising their verbal and non-verbal inputs as well as emotions, and, finally, grounding the word meaning in the external environment. The project will start from an existing neuro-cognitive architecture under development [1], based on a Human-like approach to learning, progressively incrementing knowledge and language capabilities through experience and ample exposure, using a corpus based on early language lexicons (preschool literature). The robot will integrate with visuospatial information-processing mechanisms for embodied language acquisition, exploiting affective mechanisms of emotion detection for learning and cognition. The agent will be embodied into a humanoid robot as opposed to a computer or a virtual assistant, to enable real-world interactions with the humans and the external environment, to learn and refine its natural language understanding abilities guided or depending on the teacher’s emotions and visual input (object associations with the words, facial expression, and gestures). Emotions will influence the cognitive attention of the robotic agent, modulating the selectivity of attention on specific tasks, words, and objects, and motivating actions and behaviour.

[1] Golosio B, Cangelosi A, Gamotina O, MASALA GL, A Cognitive Neural Architecture Able to Learn and Communicate through Natural Language. PLoS ONE 10(11): e0140866, 2015.

Note: The Cognitive Robotics and Autonomous Systems (CoRAS) laboratory at the School of Computing has access to several humanoids (NAO) and socially interactive robot platforms (Buddy Pro, Q.BO One, Amy A1), mobile robots (Turtlebot Waffle Pi, Burger), pet-like companion robots and devices like AR Epson glasses and Microsoft HoloLens.

Explainability and Interpretability of Machine/Deep learning techniques in medical imaging

In medicine is very important the acceptance of Machine Learning systems not only in terms of performance but also considering the degree to which a human can understand the cause of a decision. Nowadays, the application of Computer Aided Detection Systems in radiology is often based on Deep Learning Systems thanks to their high performance. In general, more accurate models are less explainable  and there is a scientific interest in the field of Explainable Artificial Intelligence, to develop new methods that explain and interpret ML models. There is not a concrete mathematical definition for interpretability or explainability, nor have they been measured by some metric; however, a number of attempts have been made in order to clarify not only these two terms but also related concepts such as comprehensibility. A possible target (but other medical diseases are allowed) of this research is a model to discover the severity of Breast Arterial Calcifications. Breast arterial calcification (BAC) is calcium deposition in peripheral arterioles. There is increasing evidence that BAC is a good indicator of the risk of cardiovascular disease. The accurate and automated detection of BACs in mammograms remains an unsolved task and the technology is far from clinical deployment. The challenging task is to develop an explainable model applicable to BAC detection, able to discriminate between severe and weak BACs in patients’ images.

Autonomous car makes me sick

Contact:  palaniappan ramaswamy.

With the rapid advancements in autonomous car technology, we will soon see cars driving on their own on the roads. While some may dread this lack of control in fear of safety, generally it is much safe and the real issue lies elsewhere. Do you know that many of us will feel sick – motion sickness will become a huge problem and there is not much ongoing work to mitigate this situation.  In this project, we will explore using transcutaneous auricular vagus nerve stimulation (taVNS) as an intervention technology. VNS is a medically approved technology for conditions such as epilepsy. But here we will study the non-invasive version of VNS in mitigating the effects of motion sickness. Functional near infra-red spectroscopy (fNIRS) will be utilised to assess the effect of the taVNS on motion sickness. Some prior signal processing knowledge will be required but knowledge on VNS and fNIRS can be gained from the project. 

Stress management

The fundamental aspect of human experience is awareness. Combined with the ability to think, imagine and understand it results into the beautiful cosmic play we experience. However, with it comes along a multitude of problems, often illusory in nature – such as stress, anxiety, anger, negativity, etc. It isn’t hard to guess that in such states our behaviour is significantly altered, usually in harmful ways for both – us and the environment. There are techniques such as meditation, music, humour which can help us come back to our “real” senses and feel happier/peaceful again. So the fundamental enquiry would be about what sort of things do help us achieve a happier state, and moreover what’s their impact on both short term and long term brain functioning. This project will study this aim using EEG.

Information Visualisation Directed by Graph Data Mining

Contact:  peter rodgers.

Data visualisation techniques are failing in the face of large data sets. This project attempts to increase the scale of graph data that can be visualised by developing data mining techniques to guide interactive visualisation. This sophisticated combining of information visualisation and data mining promises to greatly improve the size of data understandable by analysts, and will advance the state of the art in both disciplines. On successful completion, publications in high quality venues are envisaged. This project is algorithmically demanding, requiring good coding skills. The implementation language is negotiable, but Java, JavaScript or C++ are all reasonable target languages. Data will be derived from publicly available network intrusion or social network data sets. Tasks in this research project include: (1) implementing graph display software and interface. (2) developing project specific visualisation algorithms. (3) integrating graph pattern matching and other graph data mining systems into the visualisation algorithms.

Visual Analytics for Set Data

Visual Analytics is the process of gaining insights into data through combining AI and information visualization. At present, visual analytics for set based data is largely absent. There are a large number of sources for set based data, including social networks as well as medical and biological information. This project will look at producing set mining algorithms which can then be used to support set visualization methods such as Euler/Venn diagrams or Linear diagrams. Firstly, the use of existing data mining methods will produce useful information about sets and the data instances in them. After this effort, more complex algorithms for subset and set isomorphism will be developed to allow for pattern matching within set data. These set mining methods will be integrated into Euler diagram based exploratory set visualization techniques.

Using Soft Nanomembrane Electronics for Home-based Anxiety Monitoring

Contact:  jim ang.

Sensor-enhanced virtual reality systems for mental health care and rehabilitation. New immersive technologies, such as  virtual reality (VR) and augmented reality (AR) are playing an increasingly important role in the digital health revolution. Significant research has been carried out at University of Kent, in collaboration with medical scientists/practitioners, psychiatrists/psychologists, digital artists and material scientists (for novel sensor design and integration with VR). Such projects include designing VR for dementia care, eating disorder therapy, eye disorder therapy and VR-enabled brain-machine interactions. This PhD research can take on the following directions: (1) Co-design of VR for a specific healthcare domains, involving key stakeholders (e.g. patient representatives, clinicians, etc) to  understand the design and deployment opportunities and challenges in realistic health contexts. (2) Deploy and evaluate VR prototypes to study the impact of the technologies in the target groups. (3) Design and evaluate machine learning algorithms to analyse behavioural and physiological signals for clinical meaningful information, e.g. classification of emotion, detection of eye movement, etc. 

Relevant publications: 

[1] M Mahmood, S Kwon, H Kim, Y Kim, P Siriaraya, J Choi, B Otkhmezuri, K Kang, KJ Yu, YC Jang, CS Ang, W Yeo (2021) Wireless Soft Scalp Electronics and Virtual Reality System for Motor Imagery ‐ Based Brain–Machine Interfaces. Advanced Science. 8(19). 

[2] S Mishra, K Yu, Y Kim, Y Lee, M Mahmood, R Herbert, CS Ang, W Yeo, J Intarasirisawat, Y Kown, H Lim (2020). Soft, wireless periocular wearable electronics for real-time detection of eye vergence in a virtual reality toward mobile eye therapies. Science Advances. 6 (11), eaay1729. 

[3] L Tabbaa, CS Ang, V Rose, P Siriaraya, I Stewart, KG Jenkins, M Matsangidou (2019) Bring the Outside In: Providing Accessible Experiences Through VR for People with Dementia in Locked Psychiatric Hospitals, Proceedings of the CHI 2019 Conference on Human Factors in Computing Systems. 

[4] M Matsangidou, B Otkhmezuri, CS Ang, M Avraamides, G Riva, A Gaggioli, D Iosif, M Karekla (2020). “Now I can see me” designing a multi-user virtual reality remote psychotherapy for body weight and shape concerns. Human–Computer Interaction. 1-27.

Optimisation of Queries over Virtual Knowledge Graphs

Contact:  elena botoeva.

Virtual Knowledge Graphs (also known as Ontology-Based Data Access) provide user-friendly access to Big Data stored in (possibly multiple) data sources, which can be traditional relational ones or more novel ones such as document and triple stores. In this framework an ontology is used as a conceptual representation of the data, and is connected to the data sources by the means of a mapping. User formulates queries over the ontology using a high-level query language like SPARQL; user queries are then automatically translated into queries over the underlying data sources, and the latter are executed by the database engines. Efficiency of the whole approach is highly dependent on optimality of the data source queries. While the technology is quite developed when the underlying data sources are relational, there are still many open problems when it comes to novel data sources, such as MongoDB, graph databases etc. The objective of this PhD project is to design novel techniques for optimising data source queries arising in the context of Virtual Knowledge Graphs.

Heuristics for Scalable Verification of Neural Networks

Due to the success of Deep Learning neural networks are now being employed in a variety of safety-critical applications such as autonomous driving cars and aircraft landing. Despite showing impressive results at various tasks, neural networks are known to be vulnerable (hence, not robust) to adversarial attacks: imperceptible to human eye perturbations to an input can lead to incorrect classification. Robustness verification of neural networks is currently a very hot topic both in academia and industry as neural networks. One of the main challenges in this field is deriving efficient techniques that can verify networks with hundred thousands / millions of neurons in reasonable time, which is not trivial given that exact verification is not tractable (NP- or coNP-complete for ReLU-based neural networks depending on the exact verification problem). Incomplete approaches generally offer better scalability but at the cost of completeness. The aim of the proposed PhD project will be to learn heuristics for efficient verification of neural networks.

Understanding Spiking Neural Networks

Contact:  dominique chu.

Spiking Neural Networks (SNN) are brain-like neural networks. Unlike standard rate coding neural networks, signals are encoded in time. This makes them ideal for processing data that has a temporal component, such as time-series data, video or music. Another advantage of SNNs is that there exists neuromorphic hardware that can efficiently simulate SNNs. SNNs are generally thought to be “more powerful” than standard rate coding networks. However, it is not clear precisely in what sense they are more powerful, or what precisely it is that makes them more powerful. The idea of this project is to investigate this claim using a combination of mathematical and computational methods. As such the project will require an interdisciplinary research methodology at the interface between mathematics, computer science and neuroscience. The project would be suitable for a student who wishes to become and expert in an up-and-coming method in artificial intelligence. It has the scope for both theoretical investigations, but will also require implementing neural networks.

Training algorithms for spiking neural networks

Spiking neural networks encode information through the temporal order of the signals. They are more realistic models of the brain than standard artificial neural networks and they are also more efficient in encoding information. Spiking neural networks are therefore very popular in brain simulations. A disadvantage of spiking neural networks is that there are not many efficient training algorithms available. This project will be about finding novel training algorithms for spiking neural networks and to compare the trained networks with standard artificial neural networks on a number of benchmark AI tasks. An important part of this project will be not only to evaluate how well these spiking neural networks perform in relation to standard networks, but also to understand whether or not they are, as is often claimed, more efficient in the sense that they need smaller networks or fewer computing resources. The main approach of the model will be to gain inspiration from existing theories about how the how the human brain develops and learns. These existing theories will then be adapted so as to develop efficient training algorithms. This project will be primarily within AI, but it will also provide the opportunity to learn and apply techniques and ideas from computational neuroscience.

Machine learning systems to improve medical diagnosis

Contact:  daniel soria.

Research shows that machine learning methods are extremely useful to discover or identify patterns that can help clinicians to tailor treatments. However, the implementation of those data mining procedures may be challenging because of high dimensional data sets, and the choice of proper machine learning methods may be tricky. 

The aim of the research project will be to design and develop new intelligent machine learning systems with high degree of flexibility suitable for disease prediction/diagnosis, that are also easily understandable and explicable to non-experts in the field. Data will be sought from the UK Biobank, to examine whether the selected features are correlated with the occurrence of specific diseases (e.g., breast cancer), whether these relationships persist in the presence of covariates, and the potential role of comorbidities (e.g., obesity, diabetes and cardiovascular diseases) in the assessment of the developed models

How creative are crime-related texts and what does this tell us about cyber crime?

Contact:  shujun li ,   anna jordanous.

The main aim of the PhD project is to investigate if crime-related texts can be evaluated in terms of creativity using automatic metrics. Such a study will help understand how crime-related texts are crafted (by criminals and by automated tools, possibly via a hybrid human-machine teaming approach), how they have evolved over time, how they are perceived by human receivers, and how new methods can be developed to educate people about tactics of cyber criminals. The four tasks of the PhD project will include the following: (1) collecting a large datasets of crime-related texts; (2) developing some objective (automatable) creativity metrics using supervised machine learning, targeted towards evaluating the creativity of crime-related texts (e.g., phishing emails, online hate speech, grooming, cyber bullying, etc.); (3) applying the creativity metrics to the collected data to see how malevolent creativity has evolved over years and for different crimes; (4) exploring the use of generative AI algorithms to create more creative therefore more deceptive crime-related texts.

Computational creativity and automated evaluation

Contact:  anna jordanous.

In exploring how computers can perform creative tasks, computational creativity research has produced many systems that can generate creative products or creative activity. Evaluation, a critical part of the creative process, has not been employed to such a great extent within creative systems. Recent work has concentrated on evaluating the creativity of such computational systems, but there are two issues. Firstly, recent work in evaluation of computational creativity has consisted of the system(s) being evaluated by external evaluators, rather than by the creative system evaluating itself, or evaluation by other creative software agents that may interact with that system. Incorporation of self-evaluation into computational creativity systems *as part of guiding the creative process* is also under explored. In this project the candidate will experiment with incorporating evaluation methods into a creative system and analyse the results to explore how computational creativity systems can incorporate self-evaluation. The creative systems studied could be in the area of musical or linguistic creativity, or in a creative area of the student’s choosing. It is up to the student to decide whether to focus on evaluation methods for evaluating the quality of output from a creative system or the creativity of the system itself (or both). The PhD candidate would be required to propose how they would will explore the above scenarios, for a more specific project. Anna is happy to guide students in this and help them develop their research proposal.

Expressive musical performance software

Traditionally, when computational software performs music the performances can be criticised for being too unnatural, lacking interpretation and, in short, being too mechanical. However much progress has been made within the field of expressive musical performance and musical interpretation expression. Alongside these advances have been interesting findings in musical expectation (i.e. what people expect to hear when listening to a piece of music), as well as work on emotions that are present within music and on how information and meaning are conveyed in music. Each of these advances raises questions of how the relevant aspects could be interpreted by a musical performer. Potential application areas for computer systems that can perform music in an appropriately expressive manner include, for example, improving playback in music notation editors (like Sibelius), or the automated performance of music generated on-the-fly for ‘hold’ music (played when waiting on hold during phone calls). Practical work exploring this could involve writing software that performs existing pieces, or could be to write software that can improvise, interpreting incoming sound/music and generating an appropriate sonic/musical response to it in real time.

Brain-like Computer  

Contact:  frank wang.

The human brain consists of about one billion neurons. Each neuron forms about 1,000 connections to other neurons, amounting to more than a trillion connections. If each neuron could only help store a single memory, running out of space would be a problem. You might have only a few gigabytes of storage space, similar to the space in an iPod or a USB flash drive. Yet neurons combine so that each one helps with many memories at a time, exponentially increasing the brain’s memory storage capacity to something closer to around 2.5 petabytes (or a million gigabytes). The way our brain organizes data may help us manage continuously increasing data, especially in Cloud computing and Big Data. In this project, you are expected to simulate a brain-like computer. Such a computer should be categorised into the unconventional computer group, which is different from traditional Turing machine (with stored programmes) or Von Neumann computer (with an operating system).

My relevant papers: Adaptive Neuromorphic Architecture , Memristor Neural Networks , Grid-Oriented Storage (IEEE Transactions on Computers) .

My relevant keynote talk at Cambridge: Brain and Brain-Inspired Artificial Intelligence )

New Quantum Computer

Contact: frank wang.

Most recently, Frank Wang published an article on Quantum Information Processing (Springer Nature) to report a new quantum computer that can break Landauer’s Bound. Among a number of physical limits to computation, Landauer’s bound limits the minimum amount of energy for a computer to process a bit of information. In light of this study, we may have to presume the demise of this bound despite the many mysteries uncovered with it over the past 60 years.

My relevant papers: Breaking Landauer’s bound in a spin-encoded quantum computer (Springer Nature) , Can We Break the Landauer Bound in Spin-Based Electronics (IEEE Access) .

My relevant keynote talk at Cambridge: A New Quantum Computer Not Bound By Landauer’s Bound )

 RESEARCH PROPOSAL IN ARTIFICIAL INTELLIGENCE

Artificial intelligence (AI) has become one of the most exciting fields to work with. Do you want to gain expertise advice for your research proposal. Our developers and writers give a proper solution to all your problems in AI. At first, we help you to choose the best topics, you need to have for initial research. We work in all the aspects of a topic and pick out the most specific issue in that place. Thus, we keep up our work on the right track. Our researchers go through, benchmark references, journals for collecting information based on your research objective.

Some of the key points we consider for best research proposal are:

• Find a Research Problem:

This is the first step in writing a research proposal here we find a problem of the proposed field of artificial intelligence that you want to address. It can be based on a specified challenge or limitation in AI algorithms, a demand for a more well-organized AI systems, or a longing to reconsider the ethical implications of AI technology. A clear-cut definition of the research problem and explanation why it is important will be mentioned.

• Conduct a Literature Review:

Before going into research, we review the existing literature in that field based on artificial intelligence. So that the current state of knowledge can be understand, existing research gaps can be categorized. The key findings and methodologies of related studies based on your proposal will be analyzed.

• Define Objectives and Research Questions:

Thus, the objectives of the research will be clearly stated out based on the research problem and literature review. What we hope to attain? What are the research questions that we are going to address? These objectives and research questions stands as a guide and provide a clear focus for your research methodology for our proposal.

• Choose a Research Methodology:

The methodology we use to carry your research work will be determined. The question comes as Should we focus on theoretical analysis, experimental studies, or a grouping of both? Being updated on daily basis we will always have the proper resources and feasibility of chosen methodology.

• Develop a Timeline and Budget:

Here we also include a timeline outlining at each step and a budget estimation for a proposal. We will pause our research into controllable tasks and assign time frames at each step. Potential challenges or obstacles that may arise will be studied. More over evaluate the costs for your research, data collection, or software requirements.

• Highlight Expected Results and Impact:

Clear the potential of our research work. What are our expected findings or contributions to the area of artificial intelligence? Will our research produce practical applications or theoretical implications? The meaning of our work will be explained, how it brings into line with current trends in AI.

• Write a Compelling Proposal:

A clear and concise proposal will be written to that communicates our research plan. Formatting, grammar, and style will be considered so our   proposal is professional and easy to read. Headings and subheadings will be used to form your content and make it more manageable. Feedback from leading experts will be got to improve our clarity of our proposal.

In our wide research writing journey, the research proposal is the first step. Constant revisions will be carried on to achieve a tremendous success of your journey. phdprojects.org is a gateway for enriching your academic voyage. Thus, we modify your proposal in such a  way that it is exactly to your research objectives, we assure its credibility, and including relevant data collection methods, techniques analysis and its ethical considerations.

ARTIFICIAL INTELLIGENCE THESIS PROPOSALS

AI thesis proposal are well carved by our professional writers. If you do thesis proposal with us, we assure you that our work, paves the way for your successful and approved research endeavor. Trust on our expertise to support you throughout each phase of your thesis proposal journey. Thus, we assure you to present an outstanding proposal across all sub fields of AI domains by using latest methodologies. We frame our own proposal or we even customize thesis proposal as per your needs.

• Collaborative Feature Maps of Networks and Hosts for AI-driven Intrusion Detection
• Java Programming for AI Applications
• Formal Methods Boost Experimental Performance for Explainable AI
• When Block Chain and AI Integrates
• DaaS: Internet-perception big data systems based on AI
• An Efficient System to Collect Data for AI Training on Multi-Category Object Counting Task
• A WSI architecture for AI semantic networks
• Fast Development of High-performance ICs in AI/IoT Era
• Plenary Talk 2 : Parallel Computing and AI: Impact and Opportunities
• Green AI for IIoT: Energy Efficient Intelligent Edge Computing for Industrial Internet of Things
• Development and establishment in artificial intelligence
• Artificial intelligence: challenges, perspectives and neutrosophy role. (Master Conference)
• Probabilistic machine learning and artificial intelligence
• Artificial intelligence: the next digital frontier?
• Artificial intelligence and computational pathology Artificial Intelligence: Application today and implications tomorrow
• How artificial intelligence is transforming the world
• Machine Learning: An Artificial Intelligence Approach, volume II
• Artificial Intelligence: foundations of computational agents
• Innovation and design in the age of artificial intelligence
• A vision of industry 4.0 from an artificial intelligence point of view
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AI Research at UCF

Unlocking the future of artificial intelligence.

Artificial Intelligence (AI) is transforming the world and everyday lives – from facial recognition on phones to smart home devices to security measures implemented for online banking. By some estimates, the global artificial intelligence market will grow twentyfold by 2030, reaching nearly $2 trillion.

What is AI?

Artificial Intelligence (AI) describes the simulation of human intelligence in machines that are conditioned to think and learn like humans. It is a multidisciplinary discipline that combines computer science, mathematics, psychology, and other areas to develop intelligent systems. AI systems use algorithms, which are sets of rules and instructions, along with large amounts of data to simulate human-like reasoning and behavior. This allows machines to analyze complex data, recognize patterns, and make autonomous decisions, leading to advancements in various fields such as healthcare, finance, transportation, and entertainment. According to Next Move Strategy Consulting, the market for artificial intelligence (AI) is expected to show strong growth in the coming decade. Its value of nearly 100 billion U.S. dollars is expected to grow twentyfold by 2030, up to nearly two trillion U.S. dollars.

Branches of AI

Artificial Intelligence encompasses several branches, each focusing on different aspects of intelligent behavior and problem-solving. Through interdisciplinary collaboration and cutting-edge research, UCF explores the intersection of these branches, unlocking new possibilities and pushing the boundaries of what AI can achieve.

• Computer Vision
• Data Analytics
• Machine Learning
• Natural Language Processing (NLP)

As UCF continues to make strong strides to be the University for the Future, we’re playing an important role in exploring how Artificial Intelligence (AI) technologies can analyze more and deeper data with incredible accuracy, as well as greatly improve efficiency by expediting or even automating certain tasks. AI and its many implications present an enormous opportunity — and responsibility — for purposeful, impactful innovation at UCF.

A Network of AI Researchers

UCF’s Artificial Intelligence Initiative (Aii) aimed at strengthening AI expertise across key industries such as engineering, computer science, medicine, optics, photonics, and business. With plans to onboard nearly 30 new faculty members specializing in AI, this initiative signals UCF’s commitment to driving innovation and progress in AI-related fields.

Through Aii, an interdisciplinary team will harness the power of AI and computer vision to expand into emerging areas such as robotics, natural language processing, speech recognition, and machine learning. By bridging diverse industries, this collaborative effort seeks to pioneer groundbreaking technologies with wide-ranging societal impact.

A Top University for Artificial Intelligence

Articles presented at Computer Vision and Pattern Recognition Conference in 2023

UCF101 dataset is the research benchmark for all papers on human action recognition.

UCF has been the U.S. National Science Foundation REU site in computer vision.

Funded by Intelligence Advanced Research Projects Activity.

Powering AI Innovation from the Heart of Florida

Nestled among Research Park, downtown Orlando, and vibrant research hubs like the Lake Nona Medical City, UCF has a unique advantage in tapping into the diverse resources fueling AI research and development. Orlando’s dynamic tech scene fosters close-knit collaborations with industry partners and embraces cultural diversity, driving interdisciplinary efforts with real-world impact. Whether we’re delving into cutting-edge technologies within our local community or forging connections with global leaders, UCF’s position sets the stage for unparalleled growth in AI, shaping the future of innovation.

Transforming Lives Through AI Research

Much like electricity transformed the 20th century, AI is set to revolutionize the 21st. The adoption of AI isn’t just about technological change; it’s a catalyst for an industrial revolution fundamentally reshaping how we live and thrive.

From making medicine more accessible to building more sustainable cities, AI impacts nearly every aspect of our lives, and UCF’s faculty, students, and alumni are at the heart of it.

Autonomous Vehicles

Self-driving cars were once science fiction fantasy. Today, UCF researchers are making them a reality, promising safer roads, reduced congestion, and increased accessibility, revolutionizing how people and goods are transported.

• Driving the Future
• Developing a Computer Vision-based Navigation System
• AVs and the Future of Transportation

Managing Healthcare

From personalized treatment recommendations to optimizing resource allocation in hospitals, AI-driven solutions enhance efficiency and improve patient outcomes while reducing costs.

• Advanced Medicine
• Expanded Reality in Healthcare
• Using AI in Medicine to Better Predict Disease

Planning Cities and Economies

As smart cities become increasingly popular nationwide, UCF researchers are bringing cutting-edge AI tools and technology to one of the most heavily traveled areas in the state — improving mobility, business and safety for future generations here and across the nation.

• The New Era of Simulation
• Where Artificial Intelligence Meets Urban Planning

Reshaping Education

UCF researchers explore ways to learn from AI chatbots, like ChatGPT, to improve the learning experience for students and faculty. Through innovative approaches, they aim to revolutionize the educational landscape, fostering more interactive and personalized learning experiences.

• Could AI Save Education?
• Using AI to Help Children on the Autism Spectrum

Preserving Cultural Heritage

UCF researchers are leveraging AI to preserve cultural heritage, ensuring the protection of historical sites for future generations.

• Using Satellites to Protect Ancient Sites in Syria, Iraq
• Documenting and Triaging Cultural Heritage (DATCH) Project

Protecting Wildlife

Emphasizing the significance of proactive conservation efforts for future challenges UCF researchers work on the development of effective wildlife management strategies.

• Monitoring Genetic Mutations to Manage Florida Panther

Renewable Energy

UCF researchers are making renewable energy sources like solar and wind power more accessible and reliable, contributing to a greener and more sustainable future.

• The Truth About the Future of Energy
• Developing Floating Offshore Wind Turbine Simulators

A Network of Leading AI Experts

At UCF, our educators and researchers are mentors and leaders, thinkers and doers, big dreamers and problem-solvers. Here, different approaches to exploring and advancing AI also lead to unique collaborations with a variety of industry experts.

• Recognizing Human Action
• Accelerating Drug Development
• Harvesting the Potential of AI

Mubarak Shah Ph.D.

Trustee Chair Professor of Computer Science

The director of UCF’s Center for Research in Computer Vision, Shah also leads the Artificial Intelligence Initiative’s interdisciplinary team in pursuing new AI technologies. Recently, he and a team of UCF researchers received a prestigious prize for their pioneering human action recognition dataset.

Called UCF-101, the dataset includes videos with a range of actions taken with large variations in video characteristics — such as camera motion, object appearance, pose and lighting conditions. This footage provides better examples for computers to train with due to their similarity to how these actions occur in reality.

Find out more about this widely cited dataset

Ozlem Garibay Ph.D.

Assistant Professor of Industrial Engineering and Management Systems

Fusing AI with medicine, Garibay and a team of UCF researchers devised a new, more accurate prediction method that could accelerate the development of life-saving medicines and new treatments for various diseases. Both of which otherwise take decades of time and billions of dollars to produce.

The method models drug and target protein interactions using natural language processing techniques — and the team achieved up to 97% accuracy in identifying promising drug candidates. Garibay says this innovation has the potential to slow down diseases like Alzheimer’s, cancer and the next global virus.

Explore the details on this drug-screening method

Yunjun Xu, Ph.D.

Professor of Mechanical and Aerospace Engineering

By combing nature with technology, Xu and a team of researchers are exploring the use of autonomous robots in agriculture.

Supported by a grant from the U.S. Department of Agriculture’s National Institute of Food and Agriculture, the project will enhance the agricultural applications produced by the AI Institute for Transforming Workforce and Decision Support.

Xu’s team of researchers are applying AI to a variety of concepts to improve mobility, autonomy, precision, and analysis by agricultural robots. Advancing this technology will make farming more efficient, sustainable and cost effective.

Discover how this team will revolutionize agriculture

Artificial Intelligence Degrees and Academic Programs

UCF offers a comprehensive range of degrees related to Artificial Intelligence, including bachelor’s, master’s, doctoral and online programs that equip students with the knowledge and skills needed to excel in the rapidly evolving field of AI.

Bachelor’s Degrees

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MPhil in Human-Inspired Artificial Intelligence

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This exciting MPhil in Human-Inspired Artificial Intelligence is designed to equip the next generation of AI researchers, technologists, and leaders with the skills needed to develop human-centred, human-compatible, responsible and socially and globally beneficial AI technologies.

The course offers a foundational module in human-inspired AI and several elective modules that students can select according to their interests and learning needs. Elective modules include skills modules covering technical and computational skills. These are useful for students with preliminary technical training who wish to consolidate skills. For students with a strong computational background, they can offer the opportunity for more advanced technical and interdisciplinary methods training. Elective modules also include specialist modules that offer learning opportunities in areas such as fundamental human-level AI, social and interactive AI, cognitive AI, creative AI, health and global AI, and responsible AI. The course also includes a period of supervised research where students work individually with supervisors to produce a research dissertation.

The MPhil is directed by the Centre for Human-Inspired Artificial Intelligence (CHIA) within the Institute for Technology and Humanity (ITH). CHIA is dedicated to investigating the innovative ways in which human and machine intelligence can be combined to yield AI which is capable of contributing to social and global progress. It offers an excellent interdisciplinary environment where students can explore technical, human, ethical, applied and industrial aspects of AI.

The course aims to equip students with the skills and knowledge to contribute critically, practically and constructively to interdisciplinary and cross-disciplinary research, scholarship and practice in human-inspired AI. It introduces students from diverse backgrounds to technical and research skills and specialist knowledge of AI applications and issues from a range of academic disciplines and provides them with the opportunity to carry out focused research under close supervision by domain experts at the University. 

The programme will equip the next generation of researchers and leaders in AI by: 

• providing an overview of current interdisciplinary research and challenges in the development of human-inspired AI
• providing the critical tools to engage with the forefront of the academic knowledge, methods and applications in this area.
• developing the skills and abilities to identify, address and approach practical interdisciplinary research challenges.
• supporting students to construct a thorough understanding of the technical, ethical, human and human aspects of AI.  
• Developing the ability and initiative to identify, address and approach relevant challenges across sectors and society 

Learning Outcomes

Knowledge and Understanding

By the end of the course students will have:

• a deep knowledge of the history, methods, and applications of human-inspired AI.
• a practical understanding of the opportunities and challenges of human-inspired AI technologies as applied in research, industries and different areas of society.
• a critical perspective on the governance and ethical challenges that arise from applications of AI and how these sit within and interact with wider society.
• the conceptual understanding and analytic tools to critically evaluate and contribute to debates about the nature, impacts and governance of human-inspired AI.

Skills and other attributes

Graduates of the course will be able to:

• Synthesise and analyse research and advanced scholarship across disciplines.
• Demonstrate competences in practical and technical implementation of AI.
• Put theoretical and academic knowledge into practice.
• Structure extended pieces of written work and present arguments clearly and effectively.
• Plan and implement an independent research project.
• Deal with complex issues both systematically and creatively, and show originality in tackling and solving problems.
• Present their own ideas in a public forum.
• Contribute constructively within an international and cross-disciplinary environment.

Students admitted for the MPhil can apply to continue as PhD students in Human-Inspired Artificial Intelligence within CHIA, or for PhD courses within other relevant departments.

For details of the process for applying to do a PhD, and the standard required, students should consult the CHIA website.

The Centre for Human-Inspired Artificial Intelligence (CHIA) will hold an online webinar 10:00-10:45 am GMT on 4 November 2024.  Please see the  CHIA website  for information on how to register for this event. 

The Cambridge University Postgraduate Virtual Open Days take place at the beginning of  November. They are a great opportunity to ask questions to admissions staff and academics, explore the Colleges virtually, and to find out more about courses, the application process and funding opportunities. Visit the Postgraduate Open Day page for more details.

See further the Postgraduate Admissions Events pages for other events relating to Postgraduate study, including study fairs, visits and international events.

Key Information

9 months full-time, 21 months part-time, study mode : taught, master of philosophy, institute for technology and humanity, course - related enquiries, application - related enquiries, course on department website, dates and deadlines:, michaelmas 2025.

Some courses can close early. See the Deadlines page for guidance on when to apply.

Funding Deadlines

These deadlines apply to applications for courses starting in Michaelmas 2025, Lent 2026 and Easter 2026.

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