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Keynote Addresses
Accelerating Science and Medicine with AI Physician-Scientists
Vivek Natarajan
AI Researcher
Google DeepMind
Multimodal, Generative, and Agentic AI for Pathology
Faisal Mahmood, PhD
Associate Professor of Pathology
Harvard Medical School
Lunchtime Lightning Talk
Master's of Science in Applied Biomedical Data Sciences
Stanley Pounds, PhD
Oral Session 1A
AI-Driven Imaging and Quantitative Analysis in Cancer and Cell Biology
Quantitative Live-Cell Imaging for Comprehensive Analysis of Phenotypic Heterogeneity
Anand Singh, PhD, and Zeliha Kilic, PhD
Bridging the Gap in Histopathology Image Analysis of Pediatric Solid Tumors with Unsupervised Representation Learning
Ali Foroughi Pour, PhD
Mapping Nuclear Remodeling in Neuronal Differentiation with Multimodal Imaging and Quantitative Analysis
Kirby Campbell, PhD
An AI-Guided Platform for Scalable Expert Annotation of Pediatric Cancer Pathology Images
Haoran Chen, PhD
Oral Session 1B
Mitochondrial Single Cell Omics
Custom Genomic Pipelines Reveal New Insights into Leukemia Mitochondrial Genetics
Mondira Kundu, MD, PhD
Oral Session 1C
Advancing Predictive Medicine Through Longitudinal Clinical Data Analysis
Machine Learning Using High-Dimensional Longitudinal Clinical Data Improves the Prediction of Acute and Chronic Graft-versus-Host Disease after Allogeneic Hematopoietic Cell Transplantation
Dinesh Keerthi
Early-Life Prediction of Hospitalization and Emergency Department Visits in Patients with Sickle Cell Disease Using Laboratory and Social Determinants of Health (SDoH) Data
Chengzhou Wu, PhD
Temporal Deep Learning for Longitudinal Modeling of Fetal Hemoglobin in Sickle Cell Disease
Minzhang Zheng
Time Sensitive-Adherence Tracking Protocol: Lessons Learned
Carmen Salaverría
Oral Session 2A
Building Reliable and Interpretable AI Systems for Medical Image Analysis
Region-Aware Reconstruction Strategy for Pretraining fMRI Foundation Model
Ruthwik Reddy Doodipala B. Tech, MS Candidate
Bayesian Joint Mediation Model to Identify Brain Regions Mediating Negative Effects of Radiation Therapy on Neurocognition in Medulloblastoma Patients
Saikat Nandy, PhD
AI for Automated Body Composition Analysis on Pediatric and Adult CT Images
Rafah Mresh, MD
Resiliency to Suboptimal Training Data in Machine Learning Segmentation Models for Radiology
James Pierce, BS
Oral Session 3A
From Data to Drugs: Accelerating Drug Discovery Through Data-Driven Methods
Using Bespoke Data Analysis to Improve Throughput, Communication, and Accuracy in Basic Lab Science: Don’t Forget the Small Things
Mary Ashley Rimmer, PhD
Harnessing Data Science to Enable N-of-1 Antisense Therapies in Personalized Medicine
Davneet Kaur, PhD
Direct-to-Biology Enabled Molecular Glue Discovery
Uma Neelakantan
Oral Session 3B
AI/ML modeling in computational systems biology
AI-Driven mRNA Optimization for Cell-Type-Specific Protein Expression
Steve Mathew D A, Mtech
From Literature to Structured Data: Hybrid Manual–AI Curation of the Human Kinome
Katalin Parej, PhD
Deep Learning Meets High-Throughput CRISPR Editing: Utilizing Novel CHANCE-net and CHANCE-seq platforms for Safe Lead Target Identification
Jacqueline Chyr, PhD
Proteome-Wide Computational Analyses Reveal Links Between Protein Condensate Formation and RNA Biology
Swarnendu Tripathi, PhD
