AI Innovation Institute

Event Website: bnl.gov/nysds
Dates: September 28-29, 2026
Location: SUNY Global Center, New York, NY
Co-hosts: Brookhaven National Laboratory, the Institute for Advanced Computational Science (IACS), and the AI Innovation Institute at Stony Brook University.

Join us for this premier annual conference that brings together researchers and thought leaders from academia, national labs, and industry to exchange ideas and foster cross-disciplinary collaboration centered on data-driven science and technology.

The theme of NYSDS 2026 is Transformational AI from Science to Society. This year's conference will focus on the ways in which artificial intelligence (AI), machine learning (ML) and robotics are impacting everything from our everyday lives to scientific endeavors. NYSDS2026 will feature the following main tracks:

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  Robotics and Embodied AI: advances in perception, control, learning and interaction for autonomous physical agents in real-world and scientific environments.
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  AI for Science: innovative applications ranging from the physical sciences to biology and medicine, and discussion of important ways in which AI is changing the nature of science.
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  AI for Energy: how energy is discovered and produced, how hazards are mitigated to ensure reliability of the power grid, and our search for new sources of critical minerals and materials.
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  AI for Risk Assessment: inventive uses of large amounts of data and the AI tools to assess and mitigate risks in national security, climate, and financial markets.
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  AI for Education: the ways in which education is being reimagined in the age of AI and the roadblocks to training the next generation of students.

Each track will include invited presentations, contributed talks, posters, and panel discussions.

Engineering Job & Internship Fair hosted by the Career Center Learn more and RSVP on Handshake.

The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2025 will be held from June 11th to June 15th, 2025, at the Music City Center, Nashville, TN. The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers. Register here.

Abstract: The landscape of machine learning evolves rapidly and the complexity of the networks and their architectures defies easy comprehension. AI is touted as the next scientific revolution by allowing the processing and pattern-finding in increasingly massive data sets. One potential end results could be AI enhanced measurement technologies, but what does that mean? This talk will give examples of how classical tools indicate the technical obstacles to this vision in terms of understanding training processes, model comparisons, and feature embeddings. While the results in this talk are largely empirical, they point to interesting directions for (infomation?) theoretical investigation.

Bio: Anand D. Sarwate is an Associate Professor in the Electrical and Computer Engineering Department at Rutgers, The State University of New Jersey. He received B.S. degrees in math and electrical engineering from MIT and a Ph.D. in electrical engineering from UC Berkeley. Prior to joining Rutgers he was a Research Assistant Professor at TTI-Chicago and a postdoc at the ITA Center at UC San Diego. His research interests include information theory, machine learning, signal processing, optimization, and privacy and security.
Location: Light Engineering 250

The Tiger Team open house will be Monday, September 20 at high noon by Zoom:

 https://stonybrook.zoom.us/j/99577242078?pwd=T3IzekV6MkFZQXBrMXBSRDNNZ1lLdz09

The New York Academy of Sciences Presents AI for Materials: From Discovery to Production - A Virtual Symposium

Event Description: This interdisciplinary symposium covers the application of artificial intelligence (AI) throughout the entire life cycle of new materials -- from materials simulations and synthesis to translating research into high-volume industrial production.

Event Link & Registration: nyas.org/AI4Materials2020

Abstract: To be helpful assistants, AI agents must be aware of their own capabilitiesand limitations. This includes knowing when to answer from parametricknowledge versus using tools, when to trust tool outputs, and when toabstain or hedge. Such capabilities are hard to teach through supervisedfine-tuning because they require constructing examples that reflect theagent's specific capabilities. We therefore propose a radically new approachto teaching agents what they know: collaborative self-play. We constructmulti-agent collaborations in which the group is rewarded for collectivelyarriving at correct answers. The desired meta-knowledge emerges fromthe incentives built into the structure of the interaction. We focus on smallsocieties of agents that have access to heterogeneous tools (corpus-specificretrieval), and therefore must collaborate to maximize their success withminimal effort. Experiments show that group-level rewards for multiagent communities can induce policies that transfer to improve tool use andselective prediction in single-agent scenarios. Speaker: Syed Mostofa Monsur Location: Old Computer Science Building - CS2311

Abstract: Molecular learning has become an emerging field of AI, driving breakthroughs in drug discovery, protein design, and materials design. For high-stakes scientific tasks, however, predictive accuracy alone is not sufficient: models must also be interpretable and trustworthy. Our work aims to study molecular learning under a unified explainability perspective across two major model families: Graph Neural Networks (GNNs) and Large Language Models (LLMs).

GNNs are natural choices for molecular graphs and achieve strong performance on many molecular tasks. To enhance explainability, many GNN explanation methods have been proposed and work well for 2D GNNs. However, 3D GNNs introduce two key challenges: producing chemically meaningful substructures and reducing fidelity loss caused by dense geometric graphs. To address these challenges, I present two methods. 3DGraphX decomposes dense 3D graphs into chemically meaningful 3D motifs, enabling compact explanations that align with chemical intuition. EDMA introduces an energy-based discrete mask approximation approach to reduce the discrepancy between the soft mask optimized during training and the hard mask used for explanation, improving explanation fidelity.

LLMs present different characteristics and challenges compared with GNNs. LLMs can provide a certain level of explanation through step-by-step reasoning, and their natural-language outputs are easy for humans to understand and interpret. However, because LLMs are trained for general-purpose tasks, their performance on scientific tasks often lags behind specialized GNNs. To improve performance, existing methods guide LLMs by providing suggestions through brief feedback, retrieval-augmented generation (RAG), or planner agents. However, these approaches face several limitations, such as vague guidance, introduced bias problems, and high computational cost. To fill the gap, I propose RL-Guider, a lightweight reinforcement-learning agent that converts evaluation feedback into input-specific guidance for molecular optimization. RL-Guider improves over time by accumulating historical experience and transfers efficiently across different LLMs while preserving interpretability.

Together, these efforts aim to provide explanations that are scientifically meaningful and faithful, while also preserving or improving performance on molecular tasks to better meet real scientific needs.

Speaker: Xufeng Liu

Location: New Computer Science-1-Room 115

Abstract:
Coarse grained (CG) models alleviate the drawbacks of all-atom simulations. The latter still pose challenges because they are computationally expensive and give access to limited spatiotemporal scales, despite the use of modern high-performance computing clusters. CG models ignore some of the atomistic degrees of freedom, leading to fewer interatomic interactions, hence less computing time. Introducing such models emphasizes the need to properly manage these multiple scales, by carefully deriving potentials and reconstructing conformations from their CG representations, usually with the help of Machine Learning. Following a bottom-up and force matching approach, we train a Physics-Informed Neural Network to extract the CG force field parameters from all-atom simulation data. We verify our approach by applying it to fibrin monomers to study multiple-fibrin polymerization in solution at the microsecond scale, after modifying the force field to incorporate further non-bonded interactions, not present in the training data. Access to these scales will allow us to study the effects of some of the molecules' components. Furthermore, we modify recent solutions in data-driven protein backmapping. Taking advantage of the developments in graph neural networks and variational inference, we introduce an intermediate step in the all-atom reconstruction of a molecule given its CG configuration, in an attempt to more accurately de-coarsen structures whose atom-to-CG-beads ratio is very high. The combined effect of our new forward and inverse coarse graining methodology will enable the in silico study of many phenomena that are highly dynamic and intrinsically multiscale.

Bio:
Georgios Kementzidis is a third year PhD student in the Department of Applied Mathematics and Statistics at Stony Brook University. His advisor is Dr. Yuefan Deng. His research interests lie at the intersection of Computational Science, molecular dynamics (MD) simulations, and Machine Learning (ML) applications to Computational Biophysics. He is particularly interested in coarse-graining and multi-scale simulations.

*Note: this seminar will be held in-person (food provided on a first-come, first serve basis) and online*

Join Zoom Meeting https://stonybrook.zoom.us/j/99510099036?pwd=EyowuLBGvUVLZDBlG6F6chkMICFOZ7.1
Meeting ID: 995 1009 9036
Passcode: 132419