duke math phd students

Duke Math PhD Student

Introduction

Hello! I am a first-year mathematics PhD student at Duke University . I recently graduated from the University of California, Berkeley with a BA in mathematics.

Probability theory is my current academic interest, which I approach equipped with both coursework in theoretical and applied analysis and research experience in numerical analysis and computational probability.

Teaching the next generation of mathematics consistently excites me. I have taught discussion sections of linear algebra, differential equations, and discrete mathematics, and I am currently working as a drop-in tutor ready to tutor for any math class for any student.

To learn more about me, click the links in the menu at the top of this site.

Contact Info:

• Email: scott [dot] mcintyre [at] duke [dot] edu
• LinkedIn: https://www.linkedin.com/in/scott-mcintyre-10/

G-MATH-PHD - Mathematics - PhD

Download as pdf, degree designation.

The Department of Mathematics offers graduate work leading to the PhD in mathematics.

The department offers research training in both pure and applied mathematics. Major areas of research specialization include algebra and algebraic geometry, number theory, geometry, topology, differential geometry and mathematical physics, analysis and partial differential equations, probability and stochastic processes, applied mathematics and mathematical modeling, mathematical biology, scientific computing, numerical analysis, data science and machine learning.

Further details concerning the department, the graduate program, admissions, facilities, the faculty and their research, and financial support may be obtained from the department’s website math.duke.edu . For inquiries, email the director of graduate studies at [email protected] .

Bulletin Links

• Divinity School
• Fuqua School of Business
• Graduate School
• School of Law
• School of Medicine
• Nicholas School of the Environment
• School of Nursing
• Pratt School of Engineering
• Sanford School of Public Policy
• Undergraduate Instruction
• Archived Bulletins

Hi, my name is Kairi Black (formerly Kyrie Johnson).

I am a fifth-year PhD student at Duke University , advised by Samit Dasgupta. I completed my undergraduate at the University of Utah .

From Fall 2022 through Spring 2024, I co-organised (with Rena Chu ) a graduate student learning seminar at Duke. The seminar continues under different organization; for details, see the seminar webpage .

When I'm not doing math, I enjoy rock climbing, reading, and sewing.

Pronouns: she/her, they/them Email: Office: Gross Hall 304B

• the special values of various L-functions (both p-adic and complex);
• explicit class field theory and Hilbert's 12th Problem; and
• the relationship between short character sums and Dirichlet L-functions.

The best way to learn mathematics is to do mathematics. So I don't just present concepts to students; I work to build each student's confidence in their personal problem-solving ability. That self-confidence inspires them to continue working on difficult exercises as well as independently seek out math which interests them. Similarly, students are more successful when they have role models with whom they can identify. So in my classes I highlight the accomplishments of diverse mathematicians, use language which makes no assumptions about the look or level of a mathematician, and think critically about how my identity informs my mathematical life. Courses whose instruction I have participated in are tabulated below.

I aspire that my teaching is consistent with Federico Ardila's axioms in Todos Cuentan: Cultivating Diversity in Combinatorics , which together constitute a "pressing call to action" for math educators:

Mathematical talent is distributed equally among different groups, irrespective of geographic, demographic, and economic boundaries.

Everyone can have joyful, meaningful, and empowering mathematical experiences.

Mathematics is a powerful, malleable tool that can be shaped and used differently by various communities to serve their needs.

Every student deserves to be treated with dignity and respect.

Expository Writing

Below are expository notes I wrote to organize my understanding at the time. I collect them here so others may find them useful.

The Circle Method for Algebraic Number Fields pdf abstract

Motivation for the Étale Fundamental Group pdf abstract

\(\ell\)-adic Galois Representations pdf abstract

Modular Forms, Elliptic Curves, and their Connection to Fermat's Last Theorem (Undergraduate Thesis) pdf abstract

As an undergraduate, I wrote two other short articles about graph theory which I share here as resources for others: Constructing Graceful Graphs by Extending Paths from Graceful Graphs and Graph Theory and Matrices .

Lastly, for a completely general audience (no math background needed!), I've also written three articles on the mysterious "perfect numbers": A strange definition of perfect , Perfectly even , and Perfectly odd .

•    Home |
• Graduate Program |
• Undergraduate Program |

The Graduate Program

What is mathematical biology?

• Training in mathematics
• Training in biology
• Responsibilities of RTG students
• Training in teaching and presentation skills
• Mentoring and advising
• Financial support

How do I apply?

• Current graduate students interested in mathematical biology

Training in Mathematics.

• Rick Durrett: Probabilistic Methods in Genomics and Cancer Modeling.
• Michael Reed: Mathematical Insights into the Biology of Cells.
• John Harer: Geometric and Topological Methods in Biological Networks.

Financial Support.

Current Graduate Students Interested in Mathematical Biology

Tuition, Fees, and Ph.D. Stipends

NOTE: All numbers below require annual approval by the Board of Trustees

Last updated July 2, 2024

PDF version

View full cost to attend . 

  * - Projected rates for tuition, fees, and stipends are tentative and subject to change.

** - For longer term projections, the tuition remission rate can be assumed to increase 0.7% per year.

*** - Teaching assistant and grader rates are established by the Trinity College of Arts and Sciences.

PhD Program

Take on the big challenges.

Earning a PhD takes courage. There will be obstacles, uncertainty and ambiguity. But it’s the vision of better patient care that sustains you, drives you. That’s what drives us, too.

The Duke Difference

High-impact research.

Field-defining work in imaging, genetic medicine, biomaterials, light-based technologies and more.

Mentoring, from Day One

Be surrounded by a team invested in your success.

Uniquely Interdisciplinary

Duke BME’s superpower. Experience our innovative learning and research ecosystems.

Comprehensive Mentorship & Support

Mentoring, really, even before Day One. During the Duke BME admission process we’ll begin an immersive discussion about your interests. Based on those discussions, once accepted you’ll be admitted directly to a lab. Your Advising Team will including your program director, a team of faculty members and senior PhD students as well as dedicated resources available through the Graduate School at Duke.

Additional High-Value Resources

• Conference and travel support
• Grant supported traineeship programs
• Graduate certificate programs in tissue engineering, nanoscience and photonics

Entrepreneurship: Duke Design Health

Discover. Design. Deploy.

When you join a Duke Design Health team you identify needs that impact human health, and then create effective, equitable solutions.

Doctor of Philosophy in Biomedical Engineering

30 Credits of Coursework

• Life Science course—3 credits
• Advanced Mathematics course—3 credits
• Additional courses—24 credits

2 Semesters of Teaching Assistantship (TA)

Responsible Conduct of Research (RCR)

• Orientation
• 4 RCR forums

Thesis and Defense

Sample Timeline

The minimum required amount of coursework is 30 units.

The program of coursework, including the applicability of any  transfer credits , is determined by the student, their advisor and their committee.

Each committee meeting should include an update on progress towards coursework requirements. The student’s committee retains the power to approve the coursework or request that the student take additional courses.

The advanced math (3 units required) and life science (3 units required) courses, and up to one (1) independent study class may be used toward the 30-credit requirement. See a list of potential life science and advanced math courses . Students are not limited to the courses listed.

Important Notes

• Ungraded seminars do not count toward the 30-unit requirement. Students are encouraged to discuss class selection with their advisor upon matriculation and frequently throughout their course of study
• Students seeking a master’s degree en route to a PhD must satisfy the degree requirements for the master’s degree. These are not necessarily aligned with the PhD coursework requirements, and so special consideration should be taken

Two semesters of BME Seminar are required. New matriculants take BME 702’s (Fall only).

Second-year students take BME 701’s (Spring only).

Teaching Assistantship

Two (2) semesters aare required. Duke BME PhD students typically fulfill their teaching assistant (TA) assignments in years 2 to 5.

Students must:

• Complete a TA training session
• And sign up for a Teaching Assistantship Seminar during the semesters in which they TA

Responsible Conduct of Research

RCR training at Duke challenges students to engage in ethical decision-making through active learning—by using realistic scenarios and current issues.

One (1) orientation session and 4 forums are required.

More about RCR at Duke .

PhD Contacts

Jamel Forbes

Staff Specialist, PhD Program

Danielle Giles

Assistant Director of Graduate Studies

Kathryn Radabaugh Nightingale

Director of Graduate Studies, Theo Pilkington Distinguished Professor of BME

Research Themes

Biomechanics & Mechanobiology, Biomedical Imaging & Biophotonics

Research Interests

Ultrasonic and elasticity imaging; image-guided therapies; biomechanical tissue modeling; ultrasonic bioeffects.

Duke University PhD in Mathematics & Statistics

How much does a doctorate in mathematics & statistics from duke cost, duke graduate tuition and fees.

Does Duke Offer an Online PhD in Mathematics & Statistics?

Duke doctorate student diversity for mathematics & statistics, male-to-female ratio.

About 27.8% of the students who received their PhD in mathematics and statistics in 2019-2020 were women. This is about the same as the countrywide number of 29.0%.

Racial-Ethnic Diversity

Of those graduates who received a doctor’s degree in mathematics and statistics at Duke in 2019-2020, 5.6% were racial-ethnic minorities*. This is lower than the nationwide number of 11%.

PhD in Mathematics & Statistics Focus Areas at Duke

Popular Reports

Compare your school options.

Secondary Menu

High number of ph.d. offers for mss students, february 21, 2024.

The MSS program takes pride in preparing our students for whatever comes after graduation, whether it’s a high-paying industry job or admission to a prestigious Ph.D. program. Our program allows students to delve into the very core of statistical theory, methods, computation, and their real-world applications. Over the last few years, we’ve seen an increased number of students apply (and be accepted to) respected Ph.D. programs all over the world – and this year is no exception. Our MSS students have already received offers from almost two dozen Ph.D. programs (and they’re still going):

Brown University Cambridge University Carnegie Mellon University Duke University Johns Hopkins University New York University North Caroline State University Penn State University Purdue University Rice University Texas A&M University The Ohio State University University of British Columbia University of California Irvine University of California Santa Cruz University of Chicago University of Georgia University of Minnesota University of Pennsylvania University of Pittsburgh University of Texas at Austin University of Washington

Congratulations to all our brilliant, hardworking students on their admissions offers!

Related Articles

• Our Mission
• Diversity, Equity, and Inclusion
• International Recognition
• Department History
• Past Recipients
• Considering a Statistical Science major at Duke?
• Careers for Statisticians
• Typical Pathways
• Applied Electives for BS
• Interdepartmental Majors
• Minor in Statistical Science
• Getting Started with Statistics
• Student Learning Outcomes
• Study Abroad
• Course Help & Tutoring
• Past Theses
• Research Teams
• Independent Study
• Transfer Credit
• Conference Funding for Research
• Statistical Science Majors Union
• Duke Actuarial Society
• Duke Sports Analytics Club
• Trinity Ambassadors
• Frequently Asked Questions
• Summer Session Courses
• How to Apply
• Financial Support
• Graduate Placements
• Living in Durham
• Preliminary Examination
• Dissertation
• English Language Requirement
• TA Guidelines
• Progress Toward Completion
• Ph.D. Committees
• Terminal MS Degree
• Student Governance
• Program Requirements
• PhD / Research
• Data Science & Analytics
• Health Data Science
• Finance & Economics
• Marketing Research & Business Analytics
• Social Science & Policy
• Admission Statistics
• Master's Thesis
• Portfolio of Work
• Capstone Project
• Statistical Science Proseminar
• Primary Faculty
• Secondary Faculty
• Visiting Faculty
• Postdoctoral Fellows
• Ph.D. Students
• M.S. Students
• Theory, Methods, and Computation
• Interdisciplinary Collaborations
• Statistical Consulting Center
• Alumni Profiles
• For Current Students
• Assisting Duke Students
• StatSci Alumni Network
• Ph.D. Student - Alumni Fund
• Our Ph.D. Alums
• Our M.S. Alums
• Our Undergrad Alums
• Our Postdoc Alums
• Alumni Research Symposium

Following the people and events that make up the research community at Duke

Student Wealth and Poverty Across Durham Public Schools, Mapped

By Robin Smith

On August 20, 2024

In Data , Students , Visualization

New maps of Durham released by students in Duke’s Data+ research program show the Bull City as a patchwork of red, white and pink. But what looks like a haphazardly assembled quilt is actually a picture of the socioeconomic realities facing Durham’s 32,000-plus public school students.

The color patches represent the home values across Durham, showing roughly where more and less affluent students live. The darker the red, the higher-priced their housing.

Like cities and neighborhoods, schools face economic disparities too. Research shows that school segregation by race and class in North Carolina has gotten steadily worse over the last three decades.

A 2024 study by North Carolina State University revealed that the typical low-income student attends schools where more than 70% of their classmates are low-income too — a trend that worsens the achievement gap between the richest and poorest children.

A new student assignment plan that Durham Public Schools is rolling out this year aims to combat that trend by redrawing district boundary lines — the thick black lines on the map — to make schools more diverse and equitable.

But if schools are to tackle economic segregation, they’ll need accurate ways to measure it as Durham continues to grow and change.

That was the challenge facing a team in Duke’s Data+ program this summer. For 10 weeks, Duke students Alex Barroso and Dhaval Potdar collaborated with school planners at Durham Public Schools to look at how family wealth and poverty are distributed across the school system.

“Socioeconomic status is a complicated thing,” said Barroso , a Duke junior majoring in statistical science.

For years, the standard way to identify children in need was using free and reduced-price lunch statistics from the National School Lunch Program, along with published income data from the U.S. Census Bureau.

But those numbers can be unreliable, Barroso said.

Changing state and federal policies mean that more districts — including Durham Public Schools — are providing free meals to all students, regardless of their family income. But as a result, schools no longer have an exact count of how many students qualify.

And Census estimates are based on geographic boundaries that can mask important variation in the data when we look more closely.

At a symposium in Gross Hall in July, Barroso pointed to several dark red patches (i.e., more expensive housing) bordering white ones (i.e., more affordable) on one of the team’s maps.

In some parts of the city, homes worth upwards of a million dollars abut modest apartments worth a fraction of that, “which can skew the data,” he said.

The problem with Census estimates “is that everyone who lives in that area is reported as having the same average income,” said team lead Vitaly Radsky , a PhD student at UNC’s School of Education and school planner with Durham Public Schools.

So they took a different approach: using homes as a proxy for socioeconomic status.

Research has confirmed that students from higher-value homes perform better in school as measured by standardized math tests.

The team created a custom script that fetches publicly available data on every home in Durham from sources such as Durham Open Data and the Census, and then automatically exports it to a dashboard that shows the data on a map.

“Every single house is accounted for within this project,” Barroso said.

They ran into challenges. For example, Census data are tied to tracts that don’t necessarily align with the district boundaries used by schools, said Dhaval Potdar , a graduate student in Duke’s Master in Interdisciplinary Data Science.

One takeaway from their analysis, Potdar said, is no one yardstick sums up the economic well-being of every student.

In Durham, the typical public school student lives in a home valued at about $300,000.

But the picture varies widely when you zoom in on different geographic scales and footprints.

It’s also a different story if you account for the significant fraction of Durham families who live among neighbors in a larger building such as an apartment, townhouse or condominium, instead of a single-family home.

Considering a home’s age can change the picture too.

Generally speaking, students who live in more expensive homes come from more affluent families. But in many parts of the U.S., home prices have far outpaced paychecks . That means a home that has soared in value in the years since it was purchased may not reflect a family’s true economic situation today, particularly if their income remained flat.

The team’s data visualizations aim to let school planners look at all those factors.

There are still issues to be ironed out. For example, there’s some work to be done before planners can make apples-to-apples comparisons between a student whose family owns their home versus renting a similar property, Barroso said.

“No data source is perfect,” but the research offers another way of anticipating the shifting needs of Durham students, Radsky said.

“The traditional metrics really aren’t getting at the granular fabric of the Durham community,” said Mathew Palmer, the district’s senior executive director of school planning and operational services.

Research like this helps address questions like, “are we putting our resources where the kids need them the most? And are schools equitable?”

“This analysis gives schools more tools moving forward,” Palmer said.

Meet Maggie Heraty, Duke Forest Senior Program Coordinator

Powered by WordPress & Theme by Anders Norén

Meet Our New PhD Students!

We’ll be featuring mini-profiles of our new PhD students over the next few weeks. We look forward to welcoming them into our community!

Hello! My name is Madeleine (or Maddy) Carbonneau. I graduated from Harvard in 2020 with my undergraduate degree in applied math. After graduating, I worked for a couple years at Roivant Sciences, a biotech/ pharmatech company in New York. For the last two years, I have been a post- baccalaureate fellow at the Framingham (MA) Heart Study with the National Heart, Lung, and Blood Institute asking questions about epigenetics, lifestyle factors, and biological aging.

Although I left college thinking I wanted to work in business development in the biotech industry, I quickly learned after starting my first job that I did not like being so far away from the research activities of the company. Luckily, my employer gave me the option to transition to more research-oriented roles. Here, I was able to work with powerful datasets to answer interesting questions and I felt much more motivated by my projects.

I started my position at the Framingham Heart Study to do some research in a more-academic environment and prepare for a doctoral program. In my current research, I work with a lot of genetic/multi- omics data and I’m very motivated to research the questions that arise from these data. I am particularly interested in using analytical structures that allow us to understand how genes work in synchrony. Although my research interests remain quite broad, in graduate school, I would like to explore how we can use statistics to better quantify and describe the relationships between genes and how their interrelated (dys)function can cause disease.

Outside of my research, I aim to surround myself with as many dogs as possible. My partner and I have two dogs of our own, Ginger and Sophie, and have fostered several other dogs. When that is not enough dog for me (it rarely is), I volunteer at an animal shelter where I walk and hang out with more dogs. I also enjoy spending time with my family, who lives in the Boston area. Most of all, I love hanging out with their dogs.

Hello! My name is Armelle Duston. I am of French origin but I grew up in the US in Lynchburg, Virginia. This past May, I graduated with a BS in Applied Math and Statistics from Colorado School of Mines.

At Mines, I first came across the field of mathematical biology which would eventually lead me to my interest in biostatistics. From the beginning, I was interested in using mathematical tools to better understand and improve human health.

My main research experience in undergrad was in investigating the circadian rhythms of adolescents under the mentorship of Dr. Cecilia Diniz Behn. This work involved some mathematical modeling work with systems of differential equations, but my main project was a statistical analysis of combined datasets leading to a finding about sex differences in the circadian rhythms of adolescents. Outside of research, I also participated in a summer institute in biostatistics (SIBS) at NC State and Duke. From taking a course on spatial statistics, I developed an interest in statistical methods in epidemiology and particularly environmental health.

In my free time, I love to read, travel, and do a variety of outdoor sports including hiking, mountain biking, and rock climbing. I’m excited to see what Boston has to offer, and I am looking forward to meeting everyone!

News from the School

Red meat and diabetes

How for-profit medicine is harming health care

A tradition of mentoring

Promising HIV treatment

PhD Program

Accelerate progress.

Adapting to rapid change requires unwavering conviction. And that goes double for creating it. Make a global impact and leave the world a better place than you found it. A PhD can get you there.

The Duke Difference

World-class research.

Cultivate new possibilities in computer engineering, engineering physics and microelectronics.

Mentoring, from Day One

An early introduction to research with a team that’s dedicated to your success.

Interdisciplinary Environment

Cross-disciplinary approaches foster innovation. Experience our unique learning and research ecosystems.

Comprehensive Mentorship & Support

Comprehensive mentoring is a cornerstone of the Duke ECE PhD experience. Once admitted, we help you assemble your Advising Team. Your team will include your research adviser, your departmental adviser, the director of graduate studies, a five-member dissertation committee, and the department chair.

Additional High-Value Resources

• Conference and travel support
• Grant supported traineeship programs
• Graduate certificate programs in tissue engineering, nanoscience and photonics

Certificates & Training Programs

Certificate in photonics.

Offered through the Duke Fitzpatrick Institute of Photonics

Certificate in Nanoscience

Offered through the Duke Graduate School

AI for Understanding and Designing Materials

Traineeship for the Advancement of Surgical Technology

Doctor of Philosophy in Electrical and Computer Engineering

The information below is a summary of the formal degree requirements for the PhD in ECE.

Requirements Overview

• Complete approved courses for PhD degree
• Complete  Responsible Conduct of Research  (RCR) training
• Complete the Qualifying Examination (QE)
• Establish and meet with a Supervisory Committee
• Complete the Preliminary Examination
• Complete two Teaching Assistantship assignments
• Prepare and defend a dissertation [ dissertation guidelines ]
• Complete the  Final Examination

For students  matriculating with a bachelor’s degree , a minimum of 10 courses are required, as follows:

• Six graduate-level courses in ECE (500-level or higher)
• Two approved graduate-level technical electives (500-level or higher, technical in nature, and chosen to provide a coherent program of study)
• Two approved electives (chosen to provide a coherent program of study)

For students  matriculating with a master’s degree from another institution , a minimum of five (5) courses are required, as follows:

• Three graduate-level courses in ECE (500-level or higher)
• One approved graduate-level technical elective (500-level or higher, technical in nature, and chosen to provide a coherent program of study)
• One approved elective (chosen to provide a coherent program of study)

A program of study detailing the planned/completed coursework must be approved at the Qualifying Exam (bring to exam with advisor’s signature) and Preliminary Exam stages of the PhD.

Access the  ECE PhD Program of Study

Important Notes:

• Courses must be worth 3 (or more) graduate semester hours
• Courses must be graded (Credit/No Credit or audited courses may not count toward the Program of Study)
• ECE 899 Independent Study can be used to satisfy only the Approved Elective requirement
• Undergraduate Courses (numbered 499 or lower) require DGS and Graduate School permission for enrollment and may have special restrictions
• Overall Program of Study must indicate adequate breadth, including some courses distinctly outside student’s main curricular area and research topic
• Course selection must be formally approved by a student’s adviser and the DGS through the submission and approval of a Program of Study (Qualifying Exam committee approves the first draft version as part of the exam process)
• Student must maintain a 3.0 GPA in order to remain in good standing and to graduate

Qualifying Examination

The purpose of the Qualifying Exam is to assess the potential to succeed in the PhD program by having students demonstrate:

• Reading and deeply understanding three selected papers in the field
• Understanding the strengths and shortcomings of the three papers
• Understanding why the particular problem space defined by the three papers is important
• Generating sound research ideas based on the strengths and shortcomings of the three papers
• Writing and presenting information supporting the points above

Qualifying Exam Details

• Qualifying Exam Guidelines
• QE Student Procedural Guidelines (step-by-step how-to document)
• QE Details Approval/Submission Form

Supervisory Committee

The supervisory committee is formed in preparation for the preliminary examination and must consist of at least five members (including the student’s advisor), at least three of which must be graduate ECE faculty members.

In addition, as required by The Graduate School, at least one (1) member of the committee must be from either another department or a clearly separate field of study within the Duke ECE Department. Committees are proposed using the  Committee Approval Form .

Note:  While the Graduate School’s Committee Approval Form lists a minimum of four (4) committee members, the ECE Department requires five (5) committee members.

Teaching Assistantship

All PhD students must complete two semesters of a Teaching Assistantship (TA) prior to graduation. We provide training before you enter an undergraduate classroom for the first time.

The student is expected to complete this requirement sometime during his or her third through the eighth semester. Teaching Assistantships will be assigned by the DGS based on the background and interests of the student and the current department needs.

Teaching Assistantships are expected to require 10 hours per week on average and may involve such activities as organizing and leading discussion sections, grading homework and quizzes, assisting in the development of course materials, supervising laboratory sessions and so forth.

TA training information »

Preliminary Examination

The preliminary examination, which must be completed by the end of academic year three, consists of (1) a written dissertation research proposal and 2) an oral presentation and defense of this proposal to an approved five-member faculty committee.

The written dissertation research proposal should consist of a 10-page (maximum) report plus appendices providing additional supporting information as well as an anticipated timeline for completion of all PhD degree requirements.

The oral presentation, approximately 45 minutes with extra time allotted for questions posed by the committee throughout and after the presentation, should reflect the contents of the report.

• Preliminary Exam Description
• Preliminary Exam Student Procedural Guidelines
• Graduate School PhD Committee Approval Form
• Preliminary Exam Details Form
• Preliminary Exam Outcome Form
• Preliminary Exam Rubric

Final Examination

The student must follow the Graduate School’s guidelines for submitting the dissertation and scheduling the Final Examination, including submitting the departmental defense announcement to the ECE Graduate Office and uploading the dissertation at least two weeks prior to the defense.

• Final Exam Student Procedural Guidelines
• Graduate School Guide for Electronic Submission of Theses and Dissertations
• Graduate School PhD Committee Change Form
• Final Exam Details Form
• Departmental Defense Announcement

Note:  Details concerning important dates and deadlines, filing of intention to graduate, committee approval, and additional details may be found in the  Graduate Bulletin .

PhD Contacts

Angela Chanh, M.Ed.

Assistant Director of Graduate Studies

Michael E. Gehm

Director of Graduate Studies, Professor of ECE

Kevyn Light

Senior Program Coordinator

Graduate Program Coordinator

Duke's Quantum Leap in AI

Duke's quantum leap in ai.

Dustin Tart can measure improvement in treatment of one of the deadliest conditions at Duke University Hospital by a single time marker: Before Sepsis Watch and After Sepsis Watch.

Before Sepsis Watch , an artificial intelligence tool created at Duke Institute for Health Innovation and implemented at Duke University Health System to track and treat the life-threatening response to infection, Tart saw firsthand as a Nursing Program Manager how tiny changes signaling a turn toward sepsis were missed, and treatment was delayed.

"We didn't really have a standardization around sepsis care," Tart said. "It was kind of sporadic."

Sepsis is sometimes colloquially called "blood poisoning," and occurs when an existing infection triggers a chain reaction that can rapidly lead to organ failure and death. It is the No. 1 cause of death in hospitals, according to the Centers for Disease Control and Prevention .

The problem is that many initial symptoms, such as fever and elevated heart rate, mimic other illnesses that even the most experienced clinicians might miss.

"When you're talking about sepsis, it's subtle," said Armando Bedoya , a Duke Assistant Professor of Medicine in Pulmonary, Allergy and Critical Care Medicine who was instrumental in the development of Sepsis Watch, along with Dr. Cara O'Brien .

This is why the idea of developing a deep learning model to provide Duke with an early warning system seemed like an intriguing solution.

Sepsis Watch was trained by inputting data from 42,000 patient encounters with 32 million data points. The artificial intelligence deep learning system helps synthesize all that information and compares it with 86 real-time variables from current patients every five minutes, flagging who might be susceptible to sepsis.

After Sepsis Watch was introduced in 2018, deaths attributed to sepsis dropped 27%, and use of the AI tool has expanded from the emergency department to the entire Duke University Hospital.

"A lot of people develop AI models, but not many are integrating them into clinical practice to improve clinical outcomes," said Suresh Balu , Associate Dean for Innovation and Partnership at Duke School of Medicine and Director for DIHI. "That is a huge differentiator for us at Duke."

Sepsis Watch was one of Duke's earliest successful forays into artificial intelligence use, but the growth and accessibility in the past two years of generative AI – through software such as ChatGPT – has ushered in an explosive use of AI at Duke.

AI is used by a researcher who thinks it can help find a cure for a rare pediatric cancer, by the Nasher Museum of Art to curate an exhibition, and by a Duke faculty member who hopes an AI version of himself will allow for 24/7 office hours.

And that's just skimming the surface of use across Duke, which hosted its first AI summit in April about the wide range of research, teaching and operations activities engaging AI at Duke.

"It's moving quickly toward being a pretty basic skill set," said Evan Levine , Senior Director for IT Services and Support in Duke's Office of Information Technology. "We're going to want to make sure people know how to use it correctly, to know where the pitfalls are."

An Artistic Use of AI

When the Nasher Museum of Art had a hole in its exhibition schedule last year, Chief Curator Marshall Price joked they should ask ChatGPT to plan something to fill it. But the curatorial staff didn't laugh off the suggestion. What if they really did? they wondered.

That's how the Nasher's " Act as if you are a curator : an AI-generated exhibition" came to be. The exhibition opened in September 2023 and ran through February 2024. Creating it wasn't as simple as asking the chatbot to conjure an exhibition from the 14,000 objects in the Nasher's collection. That first query yielded an answer that included works of art "not even in our collection, let alone a real artwork," said Julia McHugh , a Curator and Director of Academic Initiatives.

With help from Mark Olson , Associate Professor of the Practice of Art, Art History & Visual Studies at Duke, a custom interface was built that included the Nasher's full public-facing catalogue. Even then, AI made some interesting choices under the theme it selected for itself, "Dreams of Tomorrow: Utopian and Dystopian Visions."

"Some things we had never seen before – literally, our curatorial staff had never laid eyes on because they were in deep storage," McHugh said. "And some things were seemingly kind of random."

Among the more interesting pieces were two preparatory sketches by artist Peter Reginato, which McHugh hypothesized as an interpretation of "dreams for a future sculpture" to fit the exhibition theme.

More recently in a separate project, the Rubenstein Arts Center featured the exhibition " Rethinking the Past ," the first to use AI to simulate and reconstruct Etruscan and Roman landscapes in 1,300 years of human history.

Duke Professor of Classical Studies, Art, Art History and Visual Studies Maurizio Forte led the exhibition design, which he said had a basis in scientific and paleoenvironmental data collected through archeological analysis. The AI portion of generating images took seconds instead of months with traditional methods, Forte said. An international research team spent months validating its scientific accuracy.

"Duke is doing what Duke does best, which is innovate in all sorts of different areas and spaces. There's a lot of really cool stuff happening in the right ways, and I think we'll learn from that and come together for bigger and better things." Evan Levine Senior Director for IT Services and Support in Duke’s Office of Information Technology

AI created ethereal and dreamlike landscape portraits filled with depictions of lush vegetation.

"It's like you are bringing your camera into a time machine," Forte said, "and as soon as you shoot it, you see something that makes you say, 'Oh!' It's magic."

Curing the Rarest Diseases

The computer screen in Pranam Chatterjee 's lab in the Pratt School of Engineering is filled with neon-green squiggles, thick sections of fusilli-pasta-like springs and narrow lines that curl in every direction. It's a digital model of a protein in the cell of a rare pediatric cancer called alveolar rhabdomyosarcoma, or ARMS .

Chatterjee, an Assistant Professor of Biomedical Engineering, describes it by the very technical term, "spaghetti monster." He and his lab are trying to design something that will attach to the irregular shape, and they've used generative AI to help.

"No one can touch this protein," Chatterjee said. "And that's why this cancer is not cured. But as you can see, our binder is centered right inside that protein. So, it is able to hit it and kind of embed itself within that protein."

Hitting the protein with a binder created with the help of a generative AI algorithm means that protein can be destroyed. If the protein is destroyed, the cell will die.

Chatterjee is fairly certain that AI is going to help cure this cancer.

Chatterjee's lab has trained its custom-built generative AI by feeding it billions of points of data specifically related to protein in cells, so that when they write complex algorithms asking AI ambitious questions, they're confident in the answers.

"That is literally the hardest protein on the planet to bind to and to destroy," Chatterjee said. "If we can get to that protein, I think we can do anything – and this would prove to us that our technology makes a difference."

Chatterjee and his lab collaborators are using AI to examine some of the rarest and most difficult diseases: ARMS and Ewing Sarcoma , two rare pediatric cancers; Huntington's Disease , a fatal neurodegenerative disease; and Alexander Disease , a rare neurological disorder. They develop models for binding to the proteins, and then test them in the lab – a rare one-stop shop.

"Those are our favorite targets because they're just hard," Chatterjee said. "Generative AI is probably one of the coolest ways to design drugs for them."

24/7 Office Hours

Tony O'Driscoll did some quick math recently to illustrate a problem. About 420 students took the Duke Adjunct Professor's classes in both the Fuqua School of Business and Pratt School of Engineering in Spring 2024. It was very common for most of them to ask for at least 15 minutes of his time outside of class.

He offered 90 minutes of office hours per week over the 14-week semester.

That meant 21 available office hours – but a demand for 105 hours. It didn't add up.

"Could I create a digital copy, or clone of myself?" he asked.

O'Driscoll is working on it but inputting your life's work and knowledge – 100-plus articles, two books, more than 30 podcasts, 23 webinars and 20-plus keynote speeches that he was sad to learn totaled just 15 megabytes of JSON files – into a custom-built generative AI system and debugging it takes time.

One lesson he learned quickly is that he needed to divide "ProfTonyAI," as he's dubbed the creation, into three versions of himself: One with specific knowledge based on the class and can serve as a "study buddy" for students; another that answers broader questions about theory and research; and a final one that provides mentorship and career guidance.

"Funnily enough, I'm more pleased with the Conversational Mentor Tony than with the Study Buddy Tony," O'Driscoll said. "Generative AI is a little better at being a bit more freeform and conversational."

"If this technology allows a student to get access to meaningful and useful content at their 'moment of need,' then I think it adds value. That teachable moment could be at 3 in the morning when I'm asleep. My digital version doesn't sleep." Tony O'Driscoll Duke Adjunct Professor in Fuqua School of Business and Pratt School of Engineering

After about a year of working on it, he plans to launch ProfTonyAI this fall.

"If this technology allows a student to get access to meaningful and useful content at their 'moment of need,' then I think it adds value," O'Driscoll said. "That teachable moment could be at 3 in the morning when I'm asleep. My digital version doesn't sleep."

A Copilot for Your Office Work

In every presentation Steve Toback gives on artificial intelligence – and he gives them often because it's one of his passions – he includes a quote that has been attributed to various people across the internet: "AI won't take your job. Someone using AI will."

The underlying message from Toback, a Duke Media Architect and Senior Producer for Academic Media Production in the Office of Information Technology (OIT), is that everyone needs to learn how to use AI responsibly and ethically.

Using Microsoft Copilot is one way. All staff, faculty and students have access to Copilot (formerly Bing Chat) at no charge by using their NetID to log into the platform. Using a Duke login ensures that any data entered remains secure and is not publicly shared.

Microsoft Copilot might be most useful with basic tasks: Summarizing long emails or web pages, finding just the right formula for a difficult Excel task, creating an illustration for a Zoom presentation.

But receiving the best support requires giving Copilot (and other generative AI applications like ChatGPT) clear instructions or questions, Toback said.

"The better-formed your question is, the better response you're going to get," Toback said. "Prompt engineering is everything."

Prompt engineering means giving the chatbot parameters and expectations for the information you're seeking, and includes starting by giving it a role with the prompt, "Act as if you are..."

But even if the best prompt is provided, AI answers are only as good as your own knowledge of a subject – because generative AI answers are often not correct. It's still not an expert on everything but will answer as if it is.

"Broad-use generative tools are kind of good at a lot of things," said Levine, OIT's Senior Director for IT Services and Support.

Advancements in AI are evolving quickly with potential for transformative change.

"Duke is doing what Duke does best, which is innovate in all sorts of different areas and spaces," Levine said. "There's a lot of really cool stuff happening in the right ways, and I think we'll learn from that and come together for bigger and better things."

How are you using AI at work? Let us know by writing [email protected] .

Follow Working@Duke on  X (Twitter) , Facebook and Instagram and subscribe on YouTube .