9:00 AM – 12:00 PM, February 25, CSL B02
As the volume of biological and clinical data continues to expand, the significance of statistical and computational tools in life sciences and healthcare is growing at an unprecedented pace. These tools encompass a wide array of applications, ranging from signal processing and machine learning for predictive modelling to clinical decision support systems and intelligent monitoring. They increasingly draw on rich, multimodal health data, including medical images, longitudinal electronic health records, continuous wearable and sensor streams, and molecular and omics profiles. By integrating these diverse data sources, advanced algorithms and computing systems enable more accurate, data-driven decision-making in health and disease management. This session aims to serve as a converging point for researchers and students, providing a platform to discuss the newest breakthroughs and trends in the fields of computational biology, healthcare AI, and digital health technologies, spanning areas such as biomedical informatics, medical imaging, and AI-powered wearables.
Keynote Speaker – Prof. Chenyang Lu, Washington University
Time: 9:00 – 10:00 AM
“AI for Health: Translating Data into Impact”
Abstract: Health care is one of the most complex and high-stakes domains for AI. This talk highlights recent advances in AI for health, focusing on how progress in machine learning, foundation models, and multimodal learning can be translated into real-world impact. Through examples spanning clinical care and public health, the talk examines the technical challenges and solutions that arise when AI meets messy, diverse, real-world data. It concludes by outlining interdisciplinary research opportunities where advanced AI can meaningfully enhance human health.
Biography: Chenyang Lu is the Fullgraf Professor of Computer Science & Engineering at Washington University in St. Louis, with joint appointments in Anesthesiology, Medicine, Neurosurgery, and Public Health. As the founding director of the AI for Health Institute, he leads a multidisciplinary initiative uniting AI researchers and health professionals to address critical health challenges through innovative, data-driven approaches. His current research focuses on developing machine learning models to predict health outcomes using multimodal data, advancing both precision medicine and public health. A Fellow of the ACM and IEEE, Dr. Lu received the 2022 Outstanding Technical Achievement and Leadership Award from the IEEE Technical Community on Real-Time Systems. He also serves as the editor-in-chief of ACM Transactions on Cyber-Physical Systems.
Invited Student Speaker – Yuexing Hao, MIT
Time: 10:00 – 10:45 AM
“Objective Approaches in a Subjective Medical World”
Abstract: Large Language Models (LLM) emerge to become a powerful approach in the medical field. From summarizing clinical notes to making personalized predictions in clinical outcomes, LLM shows its strong capabilities in assisting human-centered clinical journeys. Meanwhile, LLM integrations in clinics reveal vulnerabilities, including the potential for harmful and biased responses, hallucinations, unexplainable content generation, and a lack of comprehensive measurements to validate the accuracy and reliability of the generated content. My dissertation focuses on designing and measuring how these LLM approaches can address multiple facets of challenges in human-centered healthcare delivery: what is the current landscape in LLM integrations in medical decision-making? How to integrate and deploy LLM for a patient-centered shared decision-making process? How to align these LLM prediction processes with the medical domain experts?
Biography: Yuexing Hao is a MIT EECS PostDoc in the Healthy ML Group. She holds Computer Science degrees from Rutgers University (B.A.), Tufts University (M.S.), and Cornell University (Ph.D.). Her research focuses on AI for Healthcare and Human-Computer Interaction, with an emphasis on data-driven approaches to clinical decision-making and patient-centered technologies. Yuexing has been awarded over $152,000 in competitive funding as a principal investigator during her doctoral studies. This includes the APF K. Anders Ericsson Dissertation Grant, the PCCW Frank H.T. Rhodes Leadership and Mission Grants, 2024 North America Women in Tech Most Disruptive Award (powered by Amazon), and the NCWIT AIC Collegiate Award (Honorable Mention). She interned in Google Research, Mayo Clinic, and Scale AI. Her work has been published at Nature Digital Medicine, CHI, AAAI, Bioinformatics, and the Intelligent Systems Conference. She actively serves the research community as Registration Co-Chair for ACM FAccT and Associate Chair for CSCW and CHI.
Student Presentations
Time: 11:00 AM – 12:00 PM
Sourya Sengupta: “An Input-Dependent Fisher Information-Guided Task-Sensitive Image Decomposition for Interpreting Classification Network”
Anirudh Choudhary: “Texture-guided masked image modeling for cancer detection in breast optical coherence tomography images”
Rita Huan-Ting Peng: “AI-guided multimodal analysis of neural rehabilitation for precision healthcare”
Yu-Hsiang Wang: “WaveletDiff: Multilevel Wavelet Diffusion for Time Series Generation”