New York Scientific Data Summit Opens in Manhattan With Focus on AI’s Role in Science and Society

The annual summit, held September 28–29 at the SUNY Global Center in New York City, is co-hosted by Brookhaven National Laboratory, Stony Brook University’s Institute for Advanced Computational Science and the university’s AI Innovation Institute. This year’s theme, “Transformational AI from Science to Society,” places particular emphasis on moving artificial intelligence beyond general-purpose applications and into specialized scientific research.

The event brings together participants from universities, national laboratories and industry, reflecting New York’s role as a hub for scientific research and technology development. The program covers subjects including health and medicine, scientific computing, materials research, climate, energy, robotics and the infrastructure required to operate increasingly sophisticated AI systems.

Health and Medicine Lead the Opening Program

Monday’s program began with a session devoted to AI for scientific discovery in health care and medicine. Girish Nadkarni of the Icahn School of Medicine at Mount Sinai delivered a keynote titled “AI for Human Health: From Scientific Discovery to Clinical Impact.”

The morning also featured researchers from Weill Cornell Medicine, Stony Brook University and the Massachusetts Institute of Technology. Scheduled presentations examined subjects ranging from the use of sound as scientific data to methods for evaluating the effects of AI models on psychological, physical and societal well-being.

The prominence of health research in the opening session illustrates the increasingly interdisciplinary nature of artificial intelligence. Rather than treating AI solely as a branch of computer science, the summit is examining how computational tools can assist researchers working with complex scientific and medical datasets.

AI and machine learning are increasingly being applied to computationally demanding simulations and the analysis of large scientific datasets. The summit provides a forum for researchers to discuss those applications alongside the technical and scientific challenges involved.

From Data Infrastructure to Materials Science

Another major part of Monday’s agenda focused on the infrastructure needed to support AI-driven research.

Kelly Rose of the U.S. Department of Energy was scheduled to discuss large-scale data infrastructure, access and services for research and development. Other presentations addressed inference infrastructure for agent-based scientific systems, atmospheric research and physics-aware artificial intelligence for materials engineering.

The afternoon broadened the discussion further with an “AI for Science” session. Researchers from Argonne National Laboratory, Brookhaven National Laboratory, Stony Brook University, Yale University and the New Jersey Institute of Technology were scheduled to discuss opportunities and challenges for AI in science, interpretation of X-ray absorption data, foundation models for molecules and materials, and reproducibility in computational optimization.

Monday’s agenda also included discussions involving climate applications and emerging AI research. Lightning talks covered topics including materials discovery, electronic health records and scientific AI systems, demonstrating the wide range of fields now experimenting with machine-learning techniques.

Why the Summit Matters for New York

The conference has particular relevance for New York because several of the institutions organizing or participating in the event are major components of the state’s research ecosystem.

Brookhaven National Laboratory is based on Long Island, while Stony Brook University is one of New York’s major public research universities. Manhattan institutions represented in the program include Columbia University, Mount Sinai and Weill Cornell, bringing together researchers from across the New York metropolitan region.

That concentration of institutions makes New York a natural setting for conversations about how artificial intelligence can move from theoretical research into practical scientific applications.

The summit continues Tuesday with sessions examining AI for energy, science and education, as well as robotics and embodied AI. Scheduled subjects include electric-grid applications, multimodal reasoning systems, automated experimentation and real-world robot dexterity.

For New Yorkers, the broader takeaway is that artificial intelligence research is increasingly intersecting with fields far beyond conventional software development. From medicine and climate science to materials engineering, energy and robotics, the technologies being discussed in Manhattan this week illustrate how AI is becoming part of the infrastructure of modern scientific discovery.

The New York Scientific Data Summit offers a snapshot of that transition and of the role New York research institutions are playing as scientists explore how increasingly powerful computational tools can be applied to complex real-world problems.