Rice Ken Kennedy Institute
The Ken Kennedy Institute fosters world-class foundational research in AI and Computing and enables their transformative use across diverse disciplines to solve global challenges, adhering to the highest ethical principles and positioning Rice University at the forefront of Responsible AI.
The institute—formerly known as CITI—was founded in 1986, and today brings together over 250 faculty and senior research members spanning multiple departments and schools to promote interdisciplinary collaboration. The institute also organizes key events such as the AI in Health Conference and the Energy HPC & AI Conference. By supporting research and responsible AI development, the Ken Kennedy Institute aims to create a positive societal impact and advance Rice's leadership in the field.
"Pan-genomic Advances for Fighting Reference Bias" with Ben Langmead (Johns Hopkins University)
Adam Wilcox - Breaking the Data Barrier: Accelerating Healthcare AI with Privacy-Preserving
Ramya Elangovan/Kavin Elangovan - Simple Mobile AI Retina Tracker, SMART, for Equitable Eyecare and
Jansi Sethuraj/Elangovan Krishnan - A Novel AI Powered HEART: Hypertension Eye Assessment and Risk
Ivana Parker - Predictive Modeling for Bacterial Vaginosis in a Tanzanian Cohort of Women Living
Juan Cata - Prediction of Intraoperative Hypotension: Generalization of a Machine Learning Approach
Timon Merk - Brain-Signal Decoding for Adaptive Invasive Neuromodulation in Neurological and
Narein Rao - Intratumor Heterogeneity Through the Lens of Gene Regulatory Networks
Xinyu Wang - Automated Clinical Frailty Scale Scoring with Large Language Models: Feasibility and
Jeff Dominitz - Comprehensive OOS Evaluation of Machine Learning Predictive Algorithms for Medical
Kyle Longhurst - LLM Medication-Reconciliation: From Box-Checking to Measurable Safety
Puyao Ge - A Machine Learning Approach for Adaptive Prediction of Postoperative Length of Stay
Jia Zeng - Matching Alterations to Therapies and Clinical trials with Human-AI Integration
Shalabh Srivastava - Disease Progression Modelling for Personalized Risk Stratification and
Marzia Cescon - Gradient-based Bayesian Optimization for Automated Tuning of Predictive Control in
Andrew Elliott - Probabilistic Detection of Small Tumors using Wavelet Techniques
Haris Shuaib - Federated AI Monitoring Service (FAMOS): An in Silico Feasibility Study
Yiding Han - AI-Based Synthetic Simulation CT Generation from Diagnostic CT for Simulation-Free
Gowrishankar Palaniswamy/Kavin Elangovan - COLAID: A Novel AI-Application for Accurate Detection of
Xinru Chen - AI-Powered Cardiotoxicity Risk Management in Lung Cancer Radiotherapy: From
Yiran Sun - Hierarchical Feature Guided Conditional Diffusion for PET Image Reconstruction
Physical AI and Medicine Panel
AI for Modern Therapeutics Panel
Industry Perspective Panel
David S. Jones, MD, PhD - Health AI and the Health Humanities
Peizhu (Pam) Qian, PhD - Creating User-Centered, Interactive Robotic Tutors for Clinical Nursing
Kristin Kostick-Quenet, PhD, MFA - Ethical Considerations for Agentic Health AI
George Demiris, PhD, FACMI - AI in Gerontology Research and Practice: Opportunities for AI tools to
Tapomayukh (Tapo) Bhattacharjee, PhD - Physical Intelligence for Physical Care: Towards Stakeholder
S. Craig Watkins, PhD - Generative AI and the Future of Clinical Decision-Making