The Division of Educational & Institutional Effectiveness is excited to host International Love Data Week at SBU, February 9-13, 2026!
Join us for a mix of 30-minute virtual sessions, an in-person kickoff on Monday, and a student-focused event on Wednesday celebrating data and data-informed decision-making.
Wrap up the week at the Love Data Week Open House on Friday, 2/13 with light refreshments, data-themed swag, photos with Wolfie, and time to connect with presenters.
Learn more and register on https://www.stonybrook.edu/commcms/oee/recognition/Love%20Data%20Week%202026%20Save%20the%20Date%20Placeholder.php
Abstract: Large language models (LLMs) may exhibit unintended or undesirable behaviors. Recent works have concentrated on aligning LLMs to mitigate harmful outputs. Despite these efforts, some anomalies indicate that even a well-conducted alignment process can be easily circumvented, whether intentionally or accidentally. Does alignment fine-tuning yield have robust effects on models, or are its impacts merely superficial? In this work, we make the first exploration of this phenomenon from both theoretical and empirical perspectives. Empirically, we demonstrate the elasticity of post-alignment models, i.e., the tendency to revert to the behavior distribution formed during the pre-training phase upon further fine-tuning. Leveraging compression theory, we formally deduce that fine-tuning disproportionately undermines alignment relative to pre-training, potentially by orders of magnitude. We validate the presence of elasticity through experiments on models of varying types and scales. Specifically, we find that model performance declines rapidly before reverting to the pre-training distribution, after which the rate of decline drops significantly. Furthermore, we further reveal that elasticity positively correlates with the increased model size and the expansion of pre-training data. Our findings underscore the need to address the inherent elasticity of LLMs to mitigate their resistance to alignment.

Speaker: Huajian Zhang

Location: CS2311

Please join us this Friday, February 13th for the CSE 600 seminar given by Associate Professor Debswapna Bhattacharya, from the Department of Computer Science at Virginia Tech.

Abstract: Building a model of a biological system that can provide actionable hypotheses to form a solid foundation for experimental and theoretical analyses is one of the key challenges in biology and medicine. In this talk, I will present my group's ongoing work in developing, evaluating, and disseminating a new generation of computational methods for biomolecular modeling powered by artificial intelligence (AI) and machine learning (ML). First, I will introduce a new generation of AI/ML methods for improved modeling and characterization of protein-nucleic acid assemblies by deep graph learning using embeddings from biological large language models (LLMs) as well as geometric attention-enabled pairing of heterogeneous biological LLMs, a previously unexplored avenue. Then, I will present a novel generative deep learning model based on equivariant flow matching for end-to-end generation of all-atom RNA 3D structural ensemble. Finally, I will outline my future research directions on attaining atomic-level accuracy in computational modeling of biomolecules and their assemblies at scale.

Speaker: Debswapna Bhattacharya is an Associate Professor in the Department of Computer Science at Virginia Tech. He received his Ph.D. in Computer Science from the University of Missouri-Columbia in 2016. Before joining Virginia Tech in 2022, he was an Assistant Professor at Auburn University from 2017 to 2021. His research interests lie at the intersection of computational biology and machine learning, with a particular focus on artificial intelligence for computational structural biology, specifically in modeling and characterization of biomolecular structures and interactions. His research group has been developing novel computational and data-driven methods, software, and information systems for diverse biomolecular modeling problems, ranking among the best methods in community-wide blind assessments and serving the worldwide community of biomedical users. He received various research awards (NSF CAREER Award, NIH Maximizing Investigators' Research Award, NSF National AI Research Resource Award) and numerous institutional honors (National Distinction and Outstanding Contributor at Virginia Tech, Ginn Faculty Fellowship at Auburn University, Outstanding Engineering Faculty Award at Auburn University).
Location: NCS 120
Time: May 5, 2022, Thursday, 02:00 PM Eastern Time (US and Canada)
Place: New Computer Science (NCS) Room 220, and Zoom

Zoom link: https://stonybrook.zoom.us/j/95948672934?pwd=d3ZDcUJkK3VweFBDVWhIVDhtaFU2Zz09
Meeting ID:  959 4867 2934
Passcode:  082036

Title:  Generative Adversarial Learning using Optimal Transport

Abstract: 

Generative Adversarial Learning (GAL) aims to learn a target distribution in an adversarial manner. A Generative Adversarial Network (GAN) is a concrete implementation of GAL using a discriminator and a generator that play a min-max game. GANs have been used in many machine learning and computer vision applications. However, GANs are known to be hard to train, mainly because a min-max saddle point optimization problem needs to be solved in GAL. In this thesis, I investigate several methods to improve generative adversarial learning using Optimal Transport (OT). 

Previous Wasserstein GANs (WGANs) do not compute the correct Wasserstein distance to train the discriminator. To address this problem, I propose WGAN-TS that uses the L1 transport cost and computes the correct Wasserstein distance to train the discriminator. To ensure the local convergence of WGANs, I propose WGAN-QC that adopts the quadratic transport cost. I prove that WGAN-QC not only computes the correct Wasserstein distance but also converges to a local equilibrium point. To compute the Wasserstein distance over the whole dataset, I propose to use Semi-Discrete Optimal Transport (SDOT) to match noise points and the real images during GAN training. To measure the quality of an SDOT map, I use the Maximum Relative Error (MRE) and the L_1 distance between the target distribution and the transported distribution obtained by an OT map. I propose statistical methods to estimate the MRE and the L_1 distance. I propose an efficient Epoch Gradient Descent algorithm for SDOT (SDOT-EGD). To deal with the 2D special case of GAL, I propose to use OT to learn 2D distributions. In particular, I adopt OT to match persistent diagrams in training a topology-aware GAN and learn density maps in the crowd counting task. Finally, I use OT and the topological maps of the crowd to improve the crowd counting performance and propose a topology-based metric to measure the quality of the crowd density maps.

Abstract: Traditional questionnaires remain the primary method for assessing psychological outcomes and beliefs, capturing individuals' and populations' inner states. This dissertation presents an alternative computational method that overcomes key limitations in current mental health monitoring, particularly in spatiotemporal resolution, responses to major events, and automatic belief identification. By analyzing ∼1 billion Tweets from 2 million geo-located users, we created a big data pipeline for estimating depression and anxiety at the county-week level. These Language-Based Mental Health Assessments (LBMHA) demonstrated higher reliability and validity than traditional survey measures. Our approach effectively captured mental health trends and highlighted significant increases in mental illness following major events. Using the LBMHA pipeline, we conducted quasi-experiments, research designs that simulate randomized control trials, to generate explanations for mental health changes due to COVID-19 incidence/death. Utilizing these time-series analyses, we conducted discontinuity forecasting for community-specific anxiety shifts using statistical learning via ensemble and contextual models. To likewise investigate individual internal states, we created a novel task and annotated dataset for self belief language identification. Our fine-tuned language model for self-belief classification, despite its relatively small scale, outperformed GPT-4o. The self belief topics identified by our model successfully predicted depression, anxiety, and stress, offering insights into the relationship between self-conceptualization and mental health. The adoption of scalable language-based assessments with modern distributed computation presents a promising avenue for advancing community and individual mental health research.

Speaker: Siddharth Mangalik

https://stonybrook.zoom.us/j/91251321639?pwd=faggV5jZ7ByFDCFmnLXD3HiYxjQ1Eb.1&jst=2

You are cordially invited to attend the biweekly Brookhaven AI Mixer (BAM). BAM includes one short talk on AI research happening at BNL, followed by an open mixer. The first half hour will consist of presentations that will be available via ZOOM, and the second half hour will be for in person only networking.

We meet once a month at noon in CDSD's Training Room (building 725, room 2-124) to learn about interesting AI methods and applications, engage with potential collaborators, prepare for pending FASST funding calls, and build a community of AI for Science at BNL.

Multilayer machine-learning framework for screening catalytic activity and selectivity

Abstract: Machine learning (ML) studies based on quantum chemical datasets have emerged as a powerful tool for accelerating catalyst discovery. However, their potential applications remain limited by high costs, low data quality, and restricted reliability. Here, we report a multilayer binary classification framework (MLBC) for screening catalytic performance in multistep processes with ML models. Key features include low-cost synthetic data generation via kinetic Monte Carlo simulations, high-quality data that capture catalytic behavior under reaction conditions, robust treatment of imbalanced data distributions, and descriptor selection that enhances reliability and interpretability. Using carbon dioxide (CO 2 ) hydrogenation to methanol (CH 3 OH) on copper (Cu)-based catalysts as a case study, the MLBC framework outperforms conventional ML models by demonstrating high reliability and strong generalization in classifying systems with activity and methanol selectivity exceeding those of Cu. In addition, feature analysis reveals that control over the critical transition steps between competing pathways governs activity and selectivity in CO 2 hydrogenation.

In addition to our speaker, we will have a number of CDS staff in attendance with expertise in AI methods and applications including image analysis, foundation models development, and inverse problem solving.

Biography: Dr. Wenjie Liao is a research associate in the Chemistry Division at Brookhaven National Laboratory, where he develops computational and machine-learning approaches to understand and improve heterogeneous catalysts. He earned his Ph.D. from Stony Brook University. His research focuses on identifying active sites, mapping reaction pathways, and predicting catalyst performance, with the goal of accelerating the discovery of more efficient catalysts for energy and chemical transformations.

Location: CDS, Bldg. 725, Training Room

Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1604383624?pwd=ffQ5cUPNxTI7nzClKQO6cnsNbhF9Vf.1

Meeting ID: 160 438 3624
Passcode: 558449

Please Note: Due to a funding shortfall, we are for the time being no longer able to provide pizza and sodas for these events. We will have coffee though, and all are of course welcome to bring their lunch.

You are cordially invited to attend the biweekly Brookhaven AI Mixer (BAM). BAM includes three short talks on AI research happening at BNL, followed by an open mixer over coffee and snacks for everyone to network and discuss all things AI. The first half hour will consist of presentations that will be available via ZOOM, and the second half hour will be for in person only networking.

Join us every other Tuesday at noon in CDSD's Training Room (building 725, 2nd floor) to learn about interesting AI methods and applications, engage with potential collaborators, prepare for pending FASST funding calls, and build a community of AI for Science at BNL.

Tuesday, January 7, 2025, 12:00 pm -- CDS, Bldg. 725, Training Room


Speakers

Deyu Lu
Mingyuan Ge
Kris Reyes


Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1615289117?pwd=Hqkbj9itxWrFnkhZ8rQXHPInO2gxdF.1

Meeting ID: 161 528 9117
Passcode: 991382

Abstract: Astronomers slowly made sense of the cosmos by following the stars night after night. I suggest we examine human identity in a similar way. Let's observe the words individuals use to describe themselves day after day. In this presentation, I will introduce ipseology - a new approach to studying human selves. Ipseology is the systematic, empirical study of ipseity: selfhood, individuality and the elements of identity. The primary idea is that we can learn a lot about people from their self-authored self-descriptions - especially if we follow their revisions over time. I will discuss results from sampling millions of social media bios over more than a decade and present new approaches for observation in the Post-API age.

Bio: Dr. Jason Jeffrey Jones is a computational social scientist whose expertise includes online experiments, social networks, high-throughput text analysis and machine learning. He is interested in humans' perceptions of themselves and the developing role of artificial intelligence in society.

Dr. Jones is the director of CSSERG (pronounced sea surge): the Computational Social Science of Emerging Realities Group. CSSERG is a team of scholars committed to cross-disciplinary collaboration, united by common computational methodologies and always with eyes on the near future. CSSERG has studied the effectiveness of virtual reality in evoking empathy, the dynamics of gender stereotypes in language over decades and temporal trends in personally expressed identity.

This seminar will take place in person and online (zoom link below):

Join Zoom Meeting
https://stonybrook.zoom.us/j/93686609778?pwd=KdHVyIbU3ymML6hTchXsm6JLYKLSru.1

Meeting ID: 936 8660 9778
Passcode: 638699

The Provost's Spotlight Talks feature eminent visitors to the university as well as Stony Brook faculty members who have recently been recognized for outstanding contributions in their field.

Transmedia artist Stephanie Dinkins, Kusama endowed chair in art in the College of Arts and Sciences at Stony Brook University, brings her expertise in AI to the next Spotlight Talk with The Stories We Encode: AI, Love and the Future of Algorithmic Care on Tuesday, October 22, at 3:30 pm in the Charles B. Wang Center Theatre.

Working at the intersection of emerging technologies and social collaboration, Dinkins was named a 2023 TIME 100 Most Influential People in AI. She was recognized for her work with Not the Only One, an ongoing project in which she trained an AI on three generations of Black women to give it cultural roots, a deep history, and a perspective that existing systems do not offer.

The event is free and open to the public, and the discussion will be followed by a reception in the Wang Theatre lobby, hosted by the College of Arts and Sciences for new and promoted faculty.


About the Talk

AI's impact on society necessitates addressing longstanding human rights issues and prejudices. To ensure AI benefits humanity, we must confront institutional biases, rethink our relationship with other beings and emerging technologies, and reconcile ideals with actual power structures. This involves recognizing systemic inequalities, redefining human identity, and equitably distributing resources. AI, if developed and used ethically, offers an opportunity to reimagine a more equitable world for all inhabitants.