Abstract:
Artificial Intelligence for Science (AI4Sci) has become a transformative approach in
modeling and understanding complex physical systems, encompassing different scales such as
atomistic systems and continuum systems. In atomistic systems, AI has shown potential in
accelerating simulations, optimizing molecular dynamics, and predicting material and
molecular properties through data-driven approaches, enhancing computational efficiency
while preserving accuracy. For continuum systems, AI provides powerful tools for solving
partial differential equations (PDEs) and learning physical patterns from data, capturing
intricate dynamics that govern physical and engineering processes. This work explores AI
methods--particularly equivariance for neural networks and neural operators--bridging
atomistic and continuum representations. We analyze the implications of incorporating
symmetries to improve model robustness and learning efficiency, providing a cohesive AI- driven framework for advancing scientific discovery. The findings aim to underscore the role
of AI in enhancing accuracy, applicability, scalability, interopretability, and generalization
across scales, from molecular simulations to physical modeling, opening pathways for next- generation applications in computational science.
Biography:
Wenhan Gao is a third-year Ph.D. student in Applied Mathematics at Stony Brook University, where he works under the supervision of Professor Yi Liu. Wenhan's research focuses on
equivariant neural networks, graph neural networks, and AI for partial differential equations. Wenhan's work seeks to leverage the power of symmetries to aid AI models, particularly in
fields such as computer vision (image and video generation), physical simulation (modeling
climate change), and computational chemistry (drug discovery). He has published papers on
the aforementioned topics in leading venues like NeurIPS, Transactions on Machine Learning
Research (TMLR), and Journal of Computational Physics (JCP). He also has several preprints
under review in leading venues like ICLR and CVPR. In addition to his research, Wenhan has
served as a reviewer for top-tier conferences, including ICLR, NeurIPS, ICML, and KDD, and
as a lecturer for undergraduate and graduate courses at Stony Brook University. Wenhan was
awarded the NeurIPS Travel Award and Excellence in Teaching for Fall 2023.
Learn how SBU staff are using AI.
Over the past year, we've explored the AI tools and services available at Stony Brook. Now it's time to hear from colleagues who are putting these tools to work in their everyday roles. Join Tamara Gregorian, Herb Schramm, and Allie Seal moderated by David Ecker as they share how they use AI in their position, the tasks it helps them accomplish and the lessons they've learned along the way. This session is an opportunity to see practical examples, ask questions, and gather ideas that you can apply in your own work.
Register here.
The GE Vernova Advanced Research Center invites you to the 2026 AI EDGE Symposium.
This collaborative and hands-on learning experience connects 300+ industry thought leaders in the fields of AI, Edge, Robotics, Cybersecurity, and Controls & Optimization.
Attendees will learn about the latest challenges and innovations with luminaries from key government agencies, industry customers, and technology partners as they discuss the latest advancements and trends in these fields.
Register here.
This collaborative and hands-on learning experience connects 300+ industry thought leaders in the fields of AI, Edge, Robotics, Cybersecurity, and Controls & Optimization.
Attendees will learn about the latest challenges and innovations with luminaries from key government agencies, industry customers, and technology partners as they discuss the latest advancements and trends in these fields.
Register here.
Virtual Talk: Metadata Matters: Robust Document Classification via Adaptation Methods for Text-driven Public Health by Xiaolei Huang
Zoom link to follow.
Abstract: Document classifiers have been widely applied in solving health-related issues, such as suicide prevention, flu vaccination surveillance and disease diagnosis. However, document metadata including time, gender, age and location has an enormous impact on robustness of
document classifiers. Language varies across the metadata bringing both challenges and opportunities to build reliable document classifiers. For example, online written language changes over time, and males and females express opinions differently. This talk describes how to use domain adaptation to integrate temporal and user demographic factors into document classifiers. By adapting knowledge of how language varies across the metadata, models can learn generalized representations of language through the metadata-invariant embeddings.
This approach will lead to metadata-adapted document classifiers and can also extend to personalize classification models by user embedding.
Bio: Xiaolei Huang is a 4th-year PhD candidate in Information Science at the University of Colorado, Boulder. He is currently a visiting scholar at the Johns Hopkins University. His research interests are in Natural Language Processing, Machine Learning and Public Health. Particularly, he focuses on domain adaptation, cross-lingual transfer learning, user modeling and fairness.
Zoom link to follow.
Abstract: Document classifiers have been widely applied in solving health-related issues, such as suicide prevention, flu vaccination surveillance and disease diagnosis. However, document metadata including time, gender, age and location has an enormous impact on robustness of
document classifiers. Language varies across the metadata bringing both challenges and opportunities to build reliable document classifiers. For example, online written language changes over time, and males and females express opinions differently. This talk describes how to use domain adaptation to integrate temporal and user demographic factors into document classifiers. By adapting knowledge of how language varies across the metadata, models can learn generalized representations of language through the metadata-invariant embeddings.
This approach will lead to metadata-adapted document classifiers and can also extend to personalize classification models by user embedding.
Bio: Xiaolei Huang is a 4th-year PhD candidate in Information Science at the University of Colorado, Boulder. He is currently a visiting scholar at the Johns Hopkins University. His research interests are in Natural Language Processing, Machine Learning and Public Health. Particularly, he focuses on domain adaptation, cross-lingual transfer learning, user modeling and fairness.
The Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.
The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.
For more information and registration, please visit the official website.
Groundhog Day added by Dano
Abstract : Humans reason about everyday situations by making commonsense-based inferences, derived both from explicitly stated information and implicit, unstated knowledge. In this thesis, I investigate whether NLP models have different aspects of causal knowledge about events and how to improve their understanding of narratives and plans.
Answering questions about why people perform actions in a narrative can test whether NLP systems contain and can effectively apply causal knowledge about events. I introduce TellMeWhy, a dataset concerning why characters in short narratives perform the actions described. An evaluation of then SOTA finetuned models show that they are far worse than humans. To improve models, it is important to understand what aspects of causal knowledge they need and how to best use external sources to inject this knowledge. In KnowWhy, I analyze different ways of injecting knowledge into models, which is difficult since we do not know apriori what type of knowledge will be needed to answer a question, hence requiring a ranking model to pick the most important inference. Results show that this retrieved knowledge helps models of all sizes, thereby improving their understanding of narratives.
Next, I study whether models can reason about causal aspects of plans. I focus on testing whether they understand the underlying causal dependencies reflected in the temporal order of a plan's steps. I introduce CAT-Bench, and find that SOTA models are underwhelming, and that model answers are not consistent across questions about the same step pairs. In their current state, these models cannot yet reliably be used for complex user-facing tasks. I then measure contemporary models' ability to perform user-facing and user-centric plan customization. I introduce the use of semi-symbolic edits in large language model (LLM) based agents and test several multi-LLM-agent architectures for plan customization. While LLMs still lack the ability to understand complex customization hints, my results suggest that LLM-based architectures may be worth exploring further for other customization applications. Finally, I distill complex reasoning capabilities into small language models (SLMs) using synthetic data that reflects a decomposition-then-editing process for plan customization. I demonstrate that explicitly teaching this latent causal reasoning significantly improves the quality of SLM-generated customizations. Overall, my work has improved how well NLP models understand complex reasoning associated with events in different contexts.
Speaker: Yash Kumar Lal
Location: NCS 220 or Zoom https://stonybrook.zoom.
Answering questions about why people perform actions in a narrative can test whether NLP systems contain and can effectively apply causal knowledge about events. I introduce TellMeWhy, a dataset concerning why characters in short narratives perform the actions described. An evaluation of then SOTA finetuned models show that they are far worse than humans. To improve models, it is important to understand what aspects of causal knowledge they need and how to best use external sources to inject this knowledge. In KnowWhy, I analyze different ways of injecting knowledge into models, which is difficult since we do not know apriori what type of knowledge will be needed to answer a question, hence requiring a ranking model to pick the most important inference. Results show that this retrieved knowledge helps models of all sizes, thereby improving their understanding of narratives.
Next, I study whether models can reason about causal aspects of plans. I focus on testing whether they understand the underlying causal dependencies reflected in the temporal order of a plan's steps. I introduce CAT-Bench, and find that SOTA models are underwhelming, and that model answers are not consistent across questions about the same step pairs. In their current state, these models cannot yet reliably be used for complex user-facing tasks. I then measure contemporary models' ability to perform user-facing and user-centric plan customization. I introduce the use of semi-symbolic edits in large language model (LLM) based agents and test several multi-LLM-agent architectures for plan customization. While LLMs still lack the ability to understand complex customization hints, my results suggest that LLM-based architectures may be worth exploring further for other customization applications. Finally, I distill complex reasoning capabilities into small language models (SLMs) using synthetic data that reflects a decomposition-then-editing process for plan customization. I demonstrate that explicitly teaching this latent causal reasoning significantly improves the quality of SLM-generated customizations. Overall, my work has improved how well NLP models understand complex reasoning associated with events in different contexts.
Speaker: Yash Kumar Lal
Location: NCS 220 or Zoom https://stonybrook.zoom.
What comes after today's large language models and deep neural networks? Join the Computing Community Consortium (CCC) for a virtual 30-min community chat led by David Jensen, CCC Council Member and lead author of the new CCC whitepaper, Envisioning Possible Futures for AI Research. Jensen will explore paradigm-shifting AI Research Futures like Neuro-Symbolic, Embodied, Multi-Agent, and Quantum AI, and then open the floor to the audience for an engaging Q&A discussion.
Register here.
Register here.
The Natural Language Processing Reading Group at Stony Brook University meets weekly to discuss recent research papers in NLP and related fields.
Join the Google Group here.
Location: NCS 120
Zoom Link: https://stonybrook.zoom.
Join the Google Group here.
Location: NCS 120
Zoom Link: https://stonybrook.zoom.
CSE 600 Seminar Series | Fall 2025
Speakers: Jessica Roman, Simone Binneman, Michalis Polychronakis