Program Format

The 5-day class will provide lectures on the mathematics and statistics at the heart of machine learning, plus hands-on training on implementing machine learning tools with the PyTorch software platform, and case studies of the methods applied to specific application areas.

Day 1: Basics of Generative Models with David Carlson, PhD and Helen Li, PhD

Day 2: GenAI for Text with Larry Carin, PhD and Bhuwan Dhingra, PhD

Day 3: GenAI for Images with Ricardo Henao, PhD and Tim Dunn, PhD

Day 4: GenAI for Following Instructions with Monica Agrawal, PhD and Shuyan Zhou, PhD

Day 5: GenAI for Biological Sequences with Rohit Singh, PhD

Each day of the MLSS will be arranged as follows (Eastern Time):

• 9:00-10:15am, Lecture 1: Mathematically-light introduction to the focus of the day
• 10:45am-noon, Lecture 2: Mathematically rigorous discussion of the focus of the day
• Afternoons beginning at 1:00pm: Coding sessions and case studies

Presenter Bios

Monica Agrawal, PhD, is an Assistant Professor with joint appointments in the Department of Biostatistics and Bioinformatics at the Duke University School of Medicine and the Department of Computer Science. Her research focuses on developing artificial intelligence tools to enhance clinical decision support and improve healthcare accessibility for patients. Dr. Agrawal earned her Ph.D. in Computer Science from the Massachusetts Institute of Technology and holds a B.S. in Computer Science from Stanford University. Before joining Duke, she worked at Flatiron Health, where she contributed to real-world evidence generation and data science in oncology. For more information, please visit his faculty profile: Monica Agrawal | Duke Biomedical Engineering

Lawrence Carin, PhD, is a Professor in the Department of Electrical and Computer Engineering at Duke University. His research focuses on machine learning (ML) and artificial intelligence (AI), with applications in medicine, security, and scientific discovery. Dr. Carin earned his B.S., M.S., and Ph.D. in electrical engineering from the University of Maryland, College Park. He began his academic career at Brooklyn Polytechnic Institute (now part of NYU) before joining Duke in 1995. He has served as ECE Department Chair, Vice Provost for Research, and most recently as Provost at King Abdullah University of Science & Technology (KAUST) from 2020 to 2023. He has held the William H. Younger and James L. Meriam Distinguished Professorships and is an IEEE Fellow. Dr. Carin has co-founded two AI companies—Signal Innovations Group and Infinia ML—and publishes widely in top ML/AI forums. For more information, please visit his faculty profile: Lawrence Carin | Duke Electrical & Computer Engineering

David Carlson, PhD, is an Associate Professor in the Department of Civil and Environmental Engineering at Duke University. His research focuses on developing novel machine learning and artificial intelligence techniques to accelerate scientific discovery. He applies these methods to various domains, including environmental health, mental health, and neuroscience. Dr. Carlson’s interdisciplinary approach integrates predictive modeling, health data science, and statistical neuroscience, aiming to advance both theoretical frameworks and practical applications in engineering and health sciences. For more information, please visit his faculty profile: David Carlson | Duke Civil & Environmental Engineering

Ricardo Henao, PhD, is an Associate Professor in the Department of Biostatistics and Bioinformatics at the Duke University School of Medicine, with secondary appointments in Surgery and Electrical and Computer Engineering. He also serves as Associate Director of Clinical Trials AI at the Duke Clinical Research Institute. His research focuses on statistical and machine learning methods for biomedical data, particularly probabilistic modeling and predictive analytics. Dr. Henao develops models to analyze complex, high-dimensional data such as gene expression, proteomics, clinical narratives, imaging, and electronic health records. His recent work emphasizes deep learning and probabilistic approaches to support clinical decision-making and improve health outcomes. For more information, please visit his faculty profile: Ricardo Henao | Duke Electrical & Computer Engineering

Rohit Singh, PhD, is an Assistant Professor in the Departments of Biostatistics & Bioinformatics and Cell Biology at Duke University. His research focuses on computational biology, particularly using machine learning to improve the efficiency of drug discovery. He is currently exploring how single-cell genomics and large language models can help decode disease mechanisms and identify new therapeutic targets and drugs. Dr. Singh is the recipient of the Test of Time Award at RECOMB, MIT’s George M. Sprowls Award for his Ph.D. thesis in Computer Science, and Stanford’s Christopher Stephenson Memorial Award for Masters Research. In addition to his academic work, he has industry experience. For more information, please visit his faculty profile: Rohit Singh | Duke Biostatistics & Bioinformatics

Program Details: Location, Registration and Cost

Location: In Person at Duke University, Schiciano Auditorium

The registration fee is $400, with a discounted rate for members of nonprofits of $150, and a discounted student rate of $50 payable through the registration site. Registration is free for Duke students, faculty, and staff. All fees are non-refundable.

Once we reach maximum registration, we will maintain a waitlist, and will contact those on the waitlist as spots become available. 

We also have a small number of scholarships available for those who would be otherwise unable to join.

Who Should Attend

The MLSS-GenAI is particularly well-suited to members of academia and industry, including students and trainees, who seek a thorough introduction to the methods of generative AI, including interpretation and commentary by respected leaders in the field.

The MLSS-GenAI is meant to provide value to students at multiple levels of mathematical sophistication (including with limited such background). On each day, an initial emphasis will be placed on presenting the concepts as intuitively as possible, with minimum math and technical details. As the concepts are developed further, more math will be introduced, but only the minimum necessary to explain the concepts. Then case studies will show how the technology is used in practical generative AI applications, and these discussions should be accessible to most students (concepts emphasized over detailed math). Strength in mathematics and statistics is a significant plus and will make all MLSS-GenAI material more accessible; however, it is not required to benefit from much of the program. Finally, the class will also introduce participants to the coding software used to make such technology work in practice.