Name: New York Scientific Data Summit 2026
Start: 2026-09-28T08:00:00-04:00
End: 2026-09-29T17:00:00-04:00
Location: SUNY Global Center, New York City

New York Scientific Data Summit 2026

from Monday 28 September 2026 (08:00) to Tuesday 29 September 2026 (17:00)

Monday 28 September 2026

08:00 Registration & Continental Breakfast
Registration & Continental Breakfast
08:00 - 08:45
Room: Global Classroom (Lower Level)

08:45

08:45 - 09:00
Room: Global Classroom (Lower Level)
09:00
09:00 - 10:30
Room: Global Classroom (Lower Level)
Contributions

09:00 Keynote: AI for Human Health: From Scientific Discovery to Clinical Impact - Girish Nadkarni (Icahn School of Medicine at Mount Sinai)

09:30 TBD - Olivier Elemento (Weill Cornell)

09:50 Sound as Data: What Listening Reveals That Looking Doesn't - Margaret Schedel (Stony Brook University)

10:10 Simulating and Benchmarking AI Model Impacts on Human Psychological, Physical, and Societal Well-Being - Pat Pataranutaporn (Massachusetts Institute of Technology)
10:30 Morning Coffee Break
Morning Coffee Break
10:30 - 11:00
Room: Global Classroom (Lower Level)
11:00
11:00 - 12:30
Room: Global Classroom (Lower Level)
Contributions

11:00 Keynote: Fueling the AI & R&D Lifecycle: Developing Data Infrastructure, Access, and Services at Scale for the Genesis Mission - Kelly Rose (Department of Energy)

11:30 Beyond the Model: Building the Inference Infrastructure for Agentic Science - Prasanna Balaprakash (PrimaLabs)

11:50 What Can Self-Driving Cars Teach Us About Clouds? - Arthur Sedlacek (Brookhaven National Laboratory)

12:10 Physics-Aware AI to Engineer Materials: From Atoms to Technological Applications - Michele Simoncelli (Columbia University)
12:30 Lunch Break
Lunch Break
12:30 - 13:30
Room: Cafeteria
13:30
13:30 - 15:00
Room: Global Classroom (Lower Level)
Contributions

13:30 Keynote: AI for Science – Opportunities and Challenges - Salman Habib (Argonne National Laboratory)

14:00 Physics-Informed AI Pipeline for Real-Time Interpretation of X-ray Absorption Spectra - Deyu Lu (Brookhaven National Laboratory & Stony Brook University)

14:20 Toward Foundation Models for Electronic Structure of Molecules and Materials - Tianyu Zhu (Yale University)

14:40 Stochastic Gradient Descent and Reproducibility in Stochastic Optimization - David Shirokoff (New Jersey Institute of Technology)
15:00 Afternoon Coffee Break
Afternoon Coffee Break
15:00 - 15:30
Room: Global Classroom (Lower Level)
15:30
15:30 - 16:30
Room: Global Classroom (Lower Level)
Contributions

15:30 AI for Climate: From Prediction to Impact - Kevin Reed (Stony Brook University and the NY Climate Exchange)

15:50 Algorithmic Trading with Large-Scale Deep Learning - Atlas Wang (University of Texas Austin & XTX)

16:10 TBD - Anil Chinnan (Lockheed Martin)
16:30
16:30 - 17:20
Room: Global Classroom (Lower Level)
Contributions

16:30 Raven: Trustworthy Agentic AI for Closed-Loop Materials Discovery - Renjie Li (University of Illinois Urbana-Champaign / Synthera Labs)

16:40 Interpretable Multimodal AI Integrating Structured Electronic Health Records and Large Language Models for Postoperative Infection Surveillance - Yaxu Zhuang (University of Colorado Anschutz Medical Campus)

16:50 SAFE: Structured Attribute-Factorized Embeddings for Scalable and Trustworthy Intersectional Fairness - Adam Ge (MIT Primes Researcher)

17:00 From Orchestration to Capability: Building Framework-Free Scientific Agent Systems with Portable Units of Knowledge - Shray Mathur (Brookhaven National Laboratory)

17:10 Temporal Phenotyping of Breast Cancer Care Trajectories from Longitudinal EHR Data Using Unsupervised Machine Learning - Amal Bilal (Columbia University)
17:20 Pre-Reception Break
Pre-Reception Break
17:20 - 17:30
Room: Global Classroom (Lower Level)

17:30

17:30 - 19:30
Room: Cafeteria & Multipurpose Room
Tuesday 29 September 2026

08:00 Registration & Continental Breakfast
Registration & Continental Breakfast
08:00 - 09:00
Room: Global Classroom (Lower Level)
09:00
09:00 - 10:30
Room: Global Classroom (Lower Level)
Contributions

09:00 Keynote: AI Foundations for the Electric Grid - Hendrik Hamann (Brookhaven National Laboratory & Stony Brook University)

09:30 AI for AI: Optimizing Additional Infrastructure Build-out to Power Artificial Intelligence Data Centers - Ronnie Sircar (Princeton University)

09:50 Data-Driven Design of Stellarators - Misha Padidar (Flatiron Institute)

10:10 Generative Artificial Intelligence for Uncertainty Quantification - Guannan Zhang (Oak Ridge National Laboratory)
10:30 Morning Coffee Break
Morning Coffee Break
10:30 - 11:00
Room: Global Classroom (Lower Level)
11:00
11:00 - 12:00
Room: Global Classroom (Lower Level)
Contributions

11:00 Disrupt Innovation: Protocols and Quantitative Methods - Sorin Adam Matei (Purdue University)

11:20 Multimodal Reasoning Models for Science - Sandeep Madireddy (Argonne National Laboratory)

11:40 AI-guided Drug Discovery with Automated Experimentation - Emma Bouchard (Carnegie Mellon University)
12:00 Lunch Break
Lunch Break
12:00 - 13:00
Room: Cafeteria
13:00
13:00 - 14:50
Room: Global Classroom (Lower Level)
Contributions

13:00 Keynote: Robot Dexterity in the Real World - Nancy Pollard (Carnegie Mellon University)

13:30 Edge AI: Everything, Everywhere, All at Once - Yiran Chen (Duke University)

13:50 Automating discovery: From cognitive robotics to particle physics - Hod Lipson (Columbia University)

14:10 Designing the Body of Intelligence: Generative AI for Robot Mechanism Design - Anurag Purwar (Stony Brook University)

14:30 Removing the Barriers to Simulation Adoption with Automated Environment Construction and Synthetic Data Generation - Ajay Mandlekar (NVIDIA)
14:50 Afternoon Coffee Break
Afternoon Coffee Break
14:50 - 15:20
Room: Global Classroom (Lower Level)
15:20
15:20 - 16:00
Room: Global Classroom (Lower Level)
Contributions

15:20 Local Symmetry Breaking and Correlated Fluctuations in Quantum Materials from Fine-tuned Atomistic Foundation Models - Niraj Aryal (Brookhaven National Laboratory)

15:30 Towards a practical universal approximation theorem for quantum machine learning - John Zweck (New York Institute of Technology)

15:40 Variational Transformer Ansatz for Dissipative Quantum Many-Body Steady States - Lu Wei (Stony Brook University)

15:50 Federated Scientific Intelligence: From Distributed Learning to Distributed Discovery - Ravi Madduri (Argonne National Laboratory)
16:00

16:00 - 16:50
Room: Global Classroom (Lower Level)

16:50

16:50 - 17:00
Room: Global Classroom (Lower Level)