Join CELT on Tuesday, March 31 for a focused, one-hour overview on how to redesign and future-proof assessments in the age of AI! This session will cover three key areas: leveraging AI as a co-pilot for developing effective exam questions, designing authentic assessments, and exploring how AI can strategically support active learning structures like Team-Based Learning (TBL), Project-Based Learning (PBL), and Scenario-Based Learning (SBL).

Register here.
The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.



Location: Colorado Convention Center
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 : 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.us/j/95849648243?pwd=dgPpZtDpgwQrK9z1SaPpNbBifaorzk.1

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.

Uncertainty-Aware Adaptation of LLMs for Protein-Protein Interaction Analysis

Abstract: Identification of protein-protein interactions (PPIs) helps derive cellular mechanistic understanding, particularly in the context of complex conditions such as neurodegenerative disorders, metabolic syndromes, and cancer. Large Language Models (LLMs) have demonstrated remarkable potential in predicting protein structures and interactions via automated mining of vast biomedical literature; yet their inherent uncertainty remains a key challenge for deriving reproducible findings, critical for biomedical applications. In this study, we present an uncertainty-aware adaptation of LLMs for PPI analysis, leveraging fine-tuned LLaMA-3 and BioMedGPT models. To enhance prediction reliability, we integrate LoRA ensembles and Bayesian LoRA models for uncertainty quantification (UQ), ensuring confidence- calibrated insights into protein behavior. Our approach achieves competitive performance in PPI identification across diverse disease contexts while addressing model uncertainty, thereby enhancing trustworthiness and reproducibility in computational biology. These findings underscore the potential of uncertainty-aware LLM adaptation for advancing precision medicine and biomedical research.

Biography: Sanket is a research staff member in the Applied Mathematics department within the Computing and Data Sciences Directorate at Brookhaven National Laboratory. Previously, he was the Amalie Emmy Noether Postdoctoral Fellow in the same department. He earned his Ph.D. in Statistics from Michigan State University.. Sanket's research interests span Bayesian statistics, uncertainty quantification (UQ), deep learning, Markov Chain Monte Carlo (MCMC), variational inference, and sparsity methods. He also focuses on dimensionality reduction, surrogate modeling, hybrid physical-data driven models, and active learning, with applications across climate science, materials science, and life sciences.

Location: CDS, Bldg. 725, Training Room

Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1605691898?pwd=xC7GebG7Kvzxa4AjPSIxJw7e9IZtoY.1

Meeting ID: 160 569 1898
Passcode: 303888

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