IACS and the Dept of Ecology and Evolution invite you to a PRODiG+ Fellowship Seminar.

Computational Approaches to Understanding and Forecasting Biodiversity Responses to Global Change

Summary: Advancing biodiversity forecasting requires developing computational approaches that better represent ecological complexity. In this talk, I will discuss how my research integrates field observations, predictive modeling, machine learning, and spatiotemporally explicit environmental data to develop more mechanistic and reliable forecasting tools.

Speaker: Dr. Anna Thonis, a postdoctoral researcher in NYU's Winchell Lab and Founder and Co-Chair of the IUCN SSC Anoline Lizard Specialist Group, studies how anthropogenic change reshapes the distributions of reptiles. Combining field and quantitative modeling methods, her work focuses on the ecology and conservation of Puerto Rican and urban Anolis lizards. Her website can be accessed here.

Location: IACS Seminar Room, and via Zoom.

Zoom: https://stonybrook.zoom.us/j/97023584426?pwd=HrnrYeYJfVUhaS8iOKjWF3Tbh8HYS1.1&jst=2
ID: 97023584426
Passcode: 241762
Abstract: Many unresolved legal questions over LLMs and copyright center on memorization: whether specific training data have been encoded in the model's weights during training, and whether those memorized data can be extracted in the model's outputs. While many believe that LLMs do not memorize much of their training data, recent work shows that substantial amounts of copyrighted text can be extracted from open-weight models. However, it remains an open question if similar extraction is feasible for production LLMs, given the safety measures these systems implement. We investigate this question using a two-phase procedure: (1) an initial probe to test for extraction feasibility, which sometimes uses a Best-of-N (BoN) jailbreak, followed by (2) iterative continuation prompts to attempt to extract the book. We evaluate our procedure on four production LLMs -- Claude 3.7 Sonnet, GPT-4.1, Gemini 2.5 Pro, and Grok 3 -- and we measure extraction success with a score computed from a block-based approximation of longest common substring (nv-recall). With different per-LLM experimental configurations, we were able to extract varying amounts of text. For the Phase 1 probe, it was unnecessary to jailbreak Gemini 2.5 Pro and Grok 3 to extract text (e.g, nv-recall of 76.8% and 70.3%, respectively, for Harry Potter and the Sorcerer's Stone), while it was necessary for Claude 3.7 Sonnet and GPT-4.1. In some cases, jailbroken Claude 3.7 Sonnet outputs entire books near-verbatim (e.g., nv-recall=95.8%). GPT-4.1 requires significantly more BoN attempts (e.g., 20X), and eventually refuses to continue (e.g., nv-recall=4.0%). Taken together, our work highlights that, even with model- and system-level safeguards, extraction of (in-copyright) training data remains a risk for production LLMs.

Speaker: Xinyue

Location: CS2311

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 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.

Machine Learning for Seismic Low Frequency Extrapolation

Abstract: The cycle skipping problem that plagues seismic inversion can be mitigated by utilizing low-frequency seismic data, which captures the kinematics of wave propagation, in conjunction with a reasonable initial velocity model. However, seismic sources and receivers are band-limited and cannot provide signals down to 0 Hz. To improve solution of the seismic inverse problem one can synthesize the missing low-frequency content by solving a regression problem using machine learning (ML). The recorded high-frequency (HF) seismic data is the input and the ML models are trained to predict the missing low-frequency (LF) seismic data. Deep learning models utilizing convolutional neural networks (CNNs) and generative adversarial networks (GANs) demonstrate important capabilities for LF extrapolation. However, such models require powerful hardware and careful training. We explore the feasibility of using less costly ML models such as a random forest, Gaussian process surrogates, and gradient boosting as alternatives to computationally expensive deep learning models.

Biography: Sue Minkoff is Chair of Applied Mathematics at Brookhaven National Laboratory. From 2012-2024 she was a Professor of Mathematical Sciences and an Affiliated Professor in the Departments of Sustainable Earth Systems Sciences and Science and Mathematics Education at the University of Texas at Dallas. From 2000-2012 she served on the faculty in the Department of Mathematics and Statistics at the University of Maryland, Baltimore County. She received her doctorate in Computational and Applied Mathematics from Rice University. From 1995-1997 she was a National Science Foundation-Industrial postdoc joint with the University of Texas at Austin and British Petroleum, and from 1997-2000 she held the von Neumann Fellowship in the Mathematics Department at Sandia National Labs. In 2000 Minkoff was promoted to Senior Member of the Technical Staff in Sandia's Geophysics Department. Minkoff's research interests include scientific computing, inverse problems, uncertainty quantification and digital twins modeling, Earth science, and photonics.

Location: CDS, Bldg. 725, Training Room

Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1606848158?pwd=miUtq7OkYL5SNkjbgVb19teZPNennd.1

Meeting ID: 160 684 8158
Passcode: 068399


Abstract: Pre-trained diffusion and flow matching models have made visual generation remarkably powerful, enabling high-fidelity synthesis of images and videos from natural language prompts. However, their behavior is still largely dictated by the pre-training data distribution and likelihood objective, which do not directly encode downstream desiderata such as fine-grained semantic alignment, controllability, or realism. This gap motivates post-training: starting from a base generator and further optimizing it with additional supervision signals derived from human or reward model preferences.This work presents post-training for visual generative models through two complementary case studies. First, Hummingbird addresses the problem of fine-grained contextual alignment in image-text-to-image generation. We introduce a multimodal context evaluator that scores the consistency between rich contextual descriptions and generated images, capturing fine-grained alignment beyond global CLIP similarity. By directly backpropagating these differentiable rewards through the diffusion sampler, Hummingbird substantially improves semantic faithfulness while preserving high visual quality.
Second, PISCES tackles post-training for text-to-video generation, where alignment is inherently semantic-spatio-temporal. We show that naive VLM-based rewards suffer from distributional mismatch and token-level misalignment, leading to reward hacking and suboptimal optimization. PISCES introduces a bi-objective, Optimal Transport (OT)-aligned reward module: distributional OT using Neural Optimal Transport to align text and video embedding distributions, and discrete, partial OT over a spatio-temporal cost matrix to capture semantic alignment at the token level. These rewards are integrated into both direct backpropagation and GRPO-style optimization to post-train state-of-the-art text-to-video generators. Together, Hummingbird and PISCES provide a unified view of how carefully designed visual reward models, coupled with OT-based representation alignment, can reliably improve the downstream behavior of pre-trained image and video generators.

Speaker: Minh Quan Le

Location: NCS 220

Zoom: https://stonybrook.zoom.us/j/94798224254?pwd=CFraer25qnpORbJ14aAVHRwaSJOjJM.1
Educational objectives:
  1. Describe the data modalities available in All of Us (EHR, genomics, wearables) and explainhow OMOP CDM standardization enables reproducible cohort definitions across CDRversions.
  2. Evaluate the role of AI-assisted coding tools in accelerating the research-to-manuscriptpipeline while maintaining investigator oversight and scientific rigor.
  3. Apply a secure two-environment workflow to distinguish between exportable code artifactsand environment-bound data outputs when working within controlled-access researchplatforms.


Speaker: Hants Williams, Ph.D., R.N., Applied Health Informatics, Stony Brook University

Location: MART Building, Room: 7M-0602 (7th Floor)

Zoom: https://stonybrook.zoom.us/j/95617197636?pwd=KytzZ2pVRG9SZGpKZUtpNXJISjNjZz09
Meeting ID: 95617197636
Passcode: 924293
Abstract: Recent advances in Spatial Transcriptomics (ST) pair histology images with spatially resolved gene expression profiles, enabling predictions of gene expression across different tissue locations based on image patches. This opens up new possibilities for enhancing whole slide image (WSI) prediction tasks with localized gene expression. However, existing methods do not fully leverage the interactions between different tissue locations, which are crucial for accurate joint prediction. To address this, we introduce MERGE (Multi-faceted hiErarchical gRaph for Gene Expressions), which combines a multi-faceted hierarchical graph construction strategy with graph neural networks (GNN) to improve gene expression predictions from WSIs. By clustering tissue image patches based on both spatial and morphological features, and incorporating intra- and inter-cluster edges, our approach fosters interactions between distant tissue locations during GNN learning. As an additional contribution, we evaluate different data smoothing techniques that are necessary to mitigate artifacts in ST data, often caused by technical imperfections. We advocate for adopting gene-aware smoothing methods that are more biologically justified. Experimental results on gene expression prediction show that our GNN method outperforms state-of-the-art techniques on multiple metrics such as mean squared error (MSE), mean absolute error (MAE), and pearson correlation coefficient (PCC). Qualitative analysis establishes the effectiveness of MERGE in capturing cancer marker genes, thus consolidating its utility in diagnostics. As an extension of this work, we use MERGE in a setting with an uncertainty calibration branch to perform robust gene expression smoothing. We show that using patch-wise uncertainty from an uncertainty calibration model and the gene expression predictions from MERGE to enrich the ground truth gene expression matrix, results in better alignment with pathologist annotations, thus establishing that the smoothing is biologically informed.

Speaker: Aniruddha Ganguly

Location: Virtual Zoom Meeting


https://stonybrook.zoom.us/j/5474847973?pwd=Sng0Q2h1c1d3cm9sbFBmYUczMHZNdz09
Meeting ID: 547 484 7973
Passcode: 206739

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, November 12, 2024, 12:00 pm -- CDS, Bldg. 725, Training Room

Speakers

Carlos Soto, CDS

Yi Huang, CDS

Kevin Yager, CFN