Knowledge in Data Science Symposium (KIDS25)

Sharing data science knowledge to advance cures for childhood disease

Video Archive

Keynote addresses

Accelerating science and medicine with AI physician-scientists
Vivek Natarajan

Accelerating science and medicine with AI physician-scientists
Faisal Mahmood, PhD

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
Children’s Hospital Benjamin Bloom and Fundación Ayúdame a Vivir, San Salvador, El Salvador

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

Deep Learning Meets High-Throughput CRISPR Editing: Utilizing Novel CHANCE-net and CHANCE-seq platforms for Safe Lead Target Identification
Jacqueline Chyr. PhD

AI-driven mRNA optimization for cell-type-specific protein expression
Steve Mathew D A, Mtech

Proteome-wide computational analyses reveal links between protein condensate formation and RNA biology
Swarnendu Tripathi, PhD

From Literature to Structured Data: Hybrid Manual–AI Curation of the Human Kinome
Katalin Parej, PhD

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