Duke & Chen Institute Joint Boot Camp for AI & AI Accelerated Medical Research
The Department of Computer Science at Duke University is thrilled to invite Ph.D. and postdoc students to apply for the Duke & Chen Institute Joint Boot Camp for AI & AI Accelerated Medical Research, taking place May 12-16 (Monday-Friday) at Duke University.
Organized with the generous support of the Tianqiao and Chrissy Chen Institute, this intensive program is designed to equip research-ready Ph.D. and postdoc students with a deep understanding of the latest advancements in AI technology and its transformative impact on medicine and healthcare.
The boot camp will offer a highly interactive and immersive learning experience, where emerging research directions will be explored in sessions combining short courses and dynamic brainstorming discussions.
To ensure meaningful engagement and productive discussions, space is limited to 30 participants. Registration is now open to outstanding Ph.D. and postdoc students. Travel & lodging support will be provided to admitted students.
Throughout the boot camp, students will have the opportunity to learn from some of the world’s leading experts in the field of AI-Accelerated Medical Research.
The boot camp is organized into full and half day sessions covering a wide range of topics:
Dr. Monica Agrawal (Duke University) will discuss the paradigms and training behind clinical foundation models, including LLMs, visual language models, and others. It will explore various techniques including fine-tuning and weak supervision, as well describe the evaluation landscape.
Advanced Machine Learning Enabled Imaging-Omics Analytics
Dr Heng Huang (University of Maryland College Park) will demonstrate how to design large-scale nonlinear machine learning models and apply them to analyze multi-modal and longitudinal neuroimaging and genome-wide array data to identify, detect ad characterize biomarkers associated with a wide range of diseases.
Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems
Dr. Bang Liu (Université de Montréal ) will provide an overview of LLM-based intelligent agents, framing them within a modular, brain-inspired architecture that integrates principles from cognitive science, neuroscience, and computational research.
Foundational Models and Knowledge Graphs for Consolidating our Knowledge Regarding the Human Genome
Using previously-built knowledge graphs as a basis, Dr. Jie Liu (University of Michigan) will focus on the scope and applications of knowledge graphs to improve AI transparency, reduce errors in large language models, and aid in scientific discoveries.
Machine Learning for Large-Scale, Multimodal, Biomedical Data
Dr Sriram Sankararaman (University of California, Los Angeles) will describe new approaches to analyzing Biobank data including highly scalable machine learning models that enable the detection of complex associations between genes and diseases, deep-learning based phenotype imputation, and scalable train-free approaches to deal with 3D medical volumes.
Harnessing Real World Evidence for Better Clinical Trials with AI
Dr. Fei Wang (Weill Cornell Medical College) will discuss how AI can revolutionize clinical trials by harnessing real-world evidence from the large-scale data such as electronic health records and pharmaceutical/insurance claims, as well as the potential challenges and future directions associated with this process.
Challenges and Opportunities in Translating AI for Healthcare
Dr May Dongmei Wang (Georgia Tech and Emory University) will discuss new retrieval augmented generation (RAG) solutions that address the needs healthcare LLMs have requiring more domain expertise, logical reasoning to handle complex inferences, and computation and transparency for broad adoption in clinical settings.
Medical Information Retrieval in the Era of Large Language Models (LLMs)
Dr. Wei Wang (University of California Los Angles) will explore how large language models unify data modeling tasks across domains, transform natural language processing in biomedicine, and apply to disparate data types such as molecules and images.
Dr Qiao Liu (Yale University), Dr Huaxiu Yao (University of North Carolina at Chapel Hill), Dr Xin Wang (University at Albany, SUNY) and Dr Hongtu Zhu (University of North Carolina at Chapel Hill) will introduce Causal Generalist Medical AI (Causal GMAI)—a paradigm that integrates causal inference with generalist AI models to enhance interpretability, robustness, and generalizability in medical decision-making.
Learn more about each session, or explore the boot camp’s schedule.
