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Register here: https://stonybrook.zoom.us/meeting/register/RD94cHiHRwCj6xNkCZqNEg
Abstract: Gaussian Probability Path-based Generative Models (GPPGMs) generate data by reversing a stochastic process that progressively corrupts samples with Gaussian noise. Despite state-of-the-art results in 3D molecular generation, their deployment is hindered by the high cost of long generative trajectories, often requiring hundreds to thousands of steps during training and sampling. In this work, we propose a principled method, named GAGA, to improve generation efficiency without sacrificing training granularity or inference fidelity of GPPGMs. Our key insight is that different data modalities obtain sufficient Gaussianity at markedly different steps during the forward process. Based on this observation, we analytically identify a characteristic step at which molecular data attains sufficient Gaussianity, after which the trajectory can be replaced by a closed-form Gaussian approximation. Unlike existing accelerators that coarsen or reformulate trajectories, our approach preserves full-resolution learning dynamics while avoiding redundant transport through truncated distributional states. Experiments on 3D molecular generation benchmarks demonstrate that our GAGA achieves substantial improvement on both generation quality and computational efficiency.

Speaker: Jingxiang Qu

Location: New Computer Science 220
Do Natural Language Understanding Systems Learn to Understand or to
Find Shortcuts? (Naoya Inoue, http://naoya-i.github.io/)

ABSTRACT: Recent studies have suggested that natural language understanding (NLU) systems learn to exploit superficial, task-unrelated cues (a.k.a. annotation artifacts) in current datasets. This prevents the community from reliably measuring the progress of NLU systems. In this talk, I will discuss two latest studies from our research team: (i) analysis of annotation artifacts in commonsense causal reasoning and (ii) creation of benchmark for evaluating NLU systems' internal reasoning.
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Learning graph-structured sparse models (Baojian Zhou, https://baojianzhou.github.io/) 

ABSTRACT: Learning graph-structured sparse models has recently received significant attention thanks to their broad applicability to many important real-world problems. However, such models, of more effective and stronger interpretability compared with their counterparts, are difficult to learn due to optimization challenges. In this talk, we will discuss how to learn graph-structured sparse models under stochastic and online learning settings. Some interesting related problems will also be discussed.
Abstract: Large language models are prone to memorizing some of their training data. Memorized (and possibly sensitive) samples can then be extracted at generation time by adversarial or benign users. There is hope that model alignment---a standard training process that tunes a model to harmlessly follow user instructions---would mitigate the risk of extraction. However, we develop two novel attacks that undo a language model's alignment and recover thousands of training examples from popular proprietary aligned models such as OpenAI's ChatGPT. Our work highlights the limitations of existing safeguards to prevent training data leakage in production language models.

Speaker: Pegah Alipoormolabashi

Location: CS2311
CSE 656 Seminars in Computer Vision - Wednesdays 11:30am-12:50pm, Room NCS 120

The overall purpose of this seminar is to bring together people with interests in Computer Vision theory and techniques and to examine current research issues. This course will be appropriate for people who already took a Computer Vision graduate course or already had research experience in Computer Vision. To enroll in this course, you must either: (1) be in the PhD program or (2) receive permission from the instructors.

Each seminar will consist of multiple short talks (around 10 minutes) by multiple people. Students can register for 1 credit for CSE656. Registered students must attend and present a minimum of 2 or 3 talks. Everyone else is welcome to attend. Fill in https://forms.gle/pCVXovgfMfQwGqG38 to subscribe to our mailing list for further announcement.
The overall purpose of this seminar is to bring together people with interests in Computer Vision theory and techniques and to examine current research issues. This course will be appropriate for people who already took a Computer Vision graduate course or already had research experience in Computer Vision. To enroll in this course, you must either: (1) be in the PhD program or (2) receive permission from the instructors.

Each seminar will consist of multiple short talks (around 10 minutes) by multiple people. Students can register for 1 credit for CSE 656. Registered students must attend and present a minimum of 2 or 3 talks. Everyone else is welcome to attend. Fill in https://forms.gle/pCVXovgfMfQwGqG38 to subscribe to our mailing list for further announcement.
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

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 every other Tuesday 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.

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.

AI-Driven Physics-Informed Phase Retrieval from a Single X-ray

Abstract: X-ray phase-contrast imaging enables the visualization of weakly absorbing or low-contrast structures and plays an important role in materials, biological, and energy research. Conventional X-ray holography and phase-retrieval techniques typically require multiple intensity measurements acquired at different propagation distances to recover phase information, increasing acquisition time, radiation dose, and experimental complexity. In this work, we present an AI-driven, physics-informed approach for phase retrieval using only a single X-ray intensity measurement. The method adapted a generative neural network as an inverse reconstruction engine, with physical models of X-ray wave propagation embedded directly into the optimization process. This allows phase and absorption information to be recovered from a single hologram without relying on paired, unpaired, or simulated training datasets. By combining physical constraints with self-supervised AI reconstruction, the approach achieves stable and quantitative results across a wide range of imaging conditions. The results demonstrate how physics-informed AI can reduce experimental requirements and enable data-efficient, automated phase retrieval for next-generation X-ray imaging workflows.

Biography: Xiaogang Yang is a computational scientist in the Data Analysis & Workflow Integration group at NSLS-II, focusing on AI development for X-ray imaging, data analysis, and automated workflows. He earned his PhD from Delft University of Technology, completed his postdoctoral research at Argonne National Laboratory, and previously held a tenured position at PETRA III (DESY).

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.

CSE 600 Seminar Series | Fall 2025



Abstract:

We often talk about AI as if it begins with a dataset and ends with an application. But behind every model lie two sets of actors who are rarely acknowledged in technical documentation: the workers who train AI systems and the researchers who try to make sense of them. This talk brings both groups into view.
Dr. Ben Zhang will offer an on-the-ground examination of the prevailing values and invisible labor that underpin commercial AI production and data production. Drawing on ethnographic research inside AI data annotation centers in China, he introduces the concept of precision labor to unpack the labor dimension of constructing, managing, and performing technical accuracy. This concept highlights the hidden and excessive labor required to reconcile the ambiguity and uncertainty involved in AI training. A precision labor lens challenges the legitimacy and sustainability of the relentless pursuit of technical accuracy, raising new questions about its consequences and implications.
On the other end of the pipeline, as LLMs become embedded in society, social scientists like Dr. Jieshu Wang is scrutinizing their potential biases while employing them as research tools. She will present her recent work auditing LLM responses across different contexts, revealing that LLMs exhibit varying levels of environmental awareness and disproportionately reward institutional prestige in peer-review simulations. She also demonstrates how LLMs can serve as useful tools in social-science pipelines, e.g., extracting location information, inferring demographics, parsing citations, mapping social networks, and analyzing occupational data.
By placing these two worlds side by side - the labor of training AI and the scholarly efforts to study it - we show why responsible AI should go beyond the deployment phase - emphasizing fairness audits, and model explainability. It requires reimaging the values, labor regimes, and social science practices that shape AI systems from annotation to analysis.


Bios:

Dr. Jieshu Wang is an interdisciplinary researcher studying the human and social dimensions of artificial intelligence (AI) and how people can thrive in an AI-integrated future. She combines computational methods with qualitative insights to trace technology trends and understand their broader societal impact. She earned her Ph.D. in Human and Social Dimensions of Science and Technology from Arizona State University, after earlier degrees in Civil Engineering, Economics, and Science and Technology Studies. She has also worked as a patent examiner, an editor at a popular science magazine, and co-founded Synced (机器之心), an AI-focused media company in China. Her research looks both backward and forward. Backward-looking, she examines how AI are created, who creates them, and who is missing from the process. Forward-looking, she studies how AI is transforming the way we live, connect, invent, work, and adapt, as well as how AI might help address challenges such as climate change and workforce transitions.
Dr. Ben Zhang is an Assistant Professor in the Department of Technology. His research explores the production and sociotechnical impacts of AI systems in critical areas such as work, health, and sustainability. Drawing from his background in Human-Computer Interaction (HCI), Human-Centered AI, and Science and Technology Studies (STS), he employs a life-cycle-centered approach to holistically examine the promises and harms of these systems and to inform the design of responsible AI infrastructures across their development, deployment, and governance. Ben received his Ph.D. in Information Science from the University of Michigan. Ben's work has been supported by competitive awards and fellowships, including the University of Michigan Rackham Predoctoral Fellowship and the Weizenbaum Fellowship. His research has appeared in premier computing venues, including ACM CHI, ACM CSCW, and AAAI ICWSM.

Location: NCS 120
Join us as we celebrate this year's Brook & Beyond Challenge finalists.
The Office for Research and Innovation invites you to hear about the two-month journey in which the Brook & Beyond team supported eight cohorts in bringing their bold ideas from the lab to the marketplace. It's an energizing evening that highlights the collaboration, creativity, and entrepreneurial spirit driving discovery across the University.
Meet this year's award recipients, hear pitches from the emerging founders, and applaud their achievements.
Connect, celebrate, and be part of the momentum shaping the future of innovation at
Stony Brook University.
Refreshments will be served. Registration is required.
Register Here.