AI Innovation Institute

Abstract: Many foundation models for digital pathology have been released recently. Benchmarking available methods then becomes paramount to get a clearer view of the research landscape. For this reason, we introduce THUNDER, a tile-level benchmark for digital pathology foundation models, allowing for efficient comparison of many models on diverse datasets with a series of downstream tasks, studying their feature spaces and assessing the robustness and uncertainty of predictions informed by their embeddings. Such foundation models are often used as feature extractors and combined with Multiple Instance Learning (MIL) aggregators at downstream time. Such aggregation must be efficient and reliable. We will focus on two specific examples of this: (I) HistAug, a fast and efficient generative model for controllable augmentations in the latent space of foundation models to perform data augmentation for MIL, and (ii) CAR-MIL, a method based on counterfactual attention regularisation to improve the reliability of attention maps of MIL methods.

Short-bio: Pierre Marza is a Postdoctoral Researcher at CentraleSupelec in the Biomathematics team of the MICS lab, studying Computer Vision and Deep Learning for Medical Imaging, with a focus on Digital Pathology. Prior to this, he was a PhD student at INSA Lyon, in the LIRIS and CITI labs, advised by Christian Wolf, and co-advised by Laetita Matignon and Olivier Simonin. He studied Visual Navigation, Embodied AI, Spatial Reasoning, more specifically how to learn to represent 3D space, generalize to new environments and master diverse tasks from light supervision.

Location: NCS 220

Zoom: https://stonybrook.zoom.us/j/94798224254?pwd=CFraer25qnpORbJ14aAVHRwaSJOjJM.1

Speaker TBD Refreshments will be provided

Abstract: Retrieval-augmented generation (RAG) systems empower large language models (LLMs) to access external knowledge during inference. Recent advances have enabled LLMs to act as search agents via reinforcement learning (RL), improving information acquisition through multi-turn interactions with retrieval engines. However, existing approaches either optimize retrieval using search-only metrics (e.g., NDCG) that ignore downstream utility or fine-tune the entire LLM to jointly reason and retrieve--entangling retrieval with generation and limiting the real search utility and compatibility with frozen or proprietary models. In this work, we propose s3, a lightweight, model-agnostic framework that decouples the searcher from the generator and trains the searcher using a Gain Beyond RAG reward: the improvement in generation accuracy over naïve RAG. s3 requires only 2.4k training samples to outperform baselines trained on over 70 × more data, consistently delivering stronger downstream performance across six general QA and five medical QA benchmarks.

Speaker: Peter Zeng

Location: CS2311

Qualitative data can be challenging to analyze and interpret effectively. In this workshop, SBU Libraries' Data Literacies Lead, Ahmad Pratama will show you how to extract meaningful insights from textual data, including understanding sentiment trends. Learn to explore qualitative data with Python using word clouds, basic natural language processing (NLP) techniques, and lexicon-based sentiment analysis with VADER.
RSVP via link: https://t.e2ma.net/click/t70ivh/5wwlu4oe/hy5q96

George Em Karniadakis received his SM and PhD from Massachusetts Institute of Technology. He was appointed lecturer in the Department of Mechanical Engineering at MIT in 1987 and subsequently he joined the Center for Turbulence Research at Stanford/Nasa Ames. He joined Princeton University as assistant professor in the Department of Mechanical and Aerospace Engineering and as associate faculty in the program of applied and computational mathematics. He was a visiting professor at Caltech in 1993 in the Aeronautics Department and joined Brown University as associate professor of applied mathematics in the Center for Fluid Mechanics in 1994. After becoming a full professor in 1996, he continues to be a visiting professor and senior lecturer of Ocean/Mechanical Engineering at MIT. He is an AAAS fellow (2018), fellow of the Society for Industrial and Applied Mathematics (2010), fellow of the American Physical Society (2004), fellow of the American Society of Mechanical Engineers (2003) and associate fellow of the American Institute of Aeronautics and Astronautics (2006). He received the Alexander von Humboldt award in 2017, the Ralf E Kleinman award (2015), the J. Tinsley Oden Medal (2013), and the CFD award (2007) from the US Association in Computational Mechanics. His h-index is 103, and he has been cited over 52,000 times.


Abstract:
Karniadakis will present a new approach to develop a data-driven, learning-based framework for predicting outcomes of physical and biological systems, governed by PDEs, and for discovering hidden physics from noisy data. He will introduce a deep learning approach based on neural networks (NNs) and generative adversarial networks (GANs). He will also introduce new NNs that learn functionals and nonlinear operators from functions and corresponding responses for system identification. Unlike other approaches that rely on big data, here we learn from small data by exploiting the information provided by the physical conservation laws, which are used to obtain informative priors or regularize the neural networks. He will demonstrate the power of PINNs for several inverse problems in fluid mechanics, solid mechanics and biomedicine including wake flows, shock tube problems, material characterization, brain aneurysms, etc., where traditional methods fail due to lack of boundary and initial conditions or material properties. He will also present a new NN, DeepM&Mnet, which uses DeepOnets as building blocks for multiphysics problems, and he will demonstrate its unique capability in a 7-field hypersonics application.  

To register and for more information, click here.  

Learn how to use Stony Brook Google Gemini for uploading files. Using the AI to change tone and how long the text is with a few clicks.

In this session, you will

1. Understand Stony Brook Google Gemini
2. See the New Tone and Length features

Register here.

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