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.

Submit an abstract celebrating research, new discoveries and achievements in medicine and science!

We encourage faculty, nurse practitioners, post-doctoral fellows, fellows, residents, medical students, graduate students and undergraduate students to submit an abstract. Original research, case reports and case series are welcome.

Abstract submission deadline: FEBRUARY 7, 2025

For more details, visit here.

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

Join Stony Brook University's Center for Excellence in Learning and Teaching (CELT) for a bootcamp on how to use AI to enhance your teaching and courses. This event will demonstrate how ChatGPT, Microsoft Copilot, and other generative AI platforms can support you in crafting learning objectives, writing exam questions, composing rubrics, and designing course content such as lesson plans, in-class activities, instructional videos, and more.

Register here.
Abstract: Molecular learning has become an emerging field of AI, driving breakthroughs in drug discovery, protein design, and materials design. For high-stakes scientific tasks, however, predictive accuracy alone is not sufficient: models must also be interpretable and trustworthy. Our work aims to study molecular learning under a unified explainability perspective across two major model families: Graph Neural Networks (GNNs) and Large Language Models (LLMs).

GNNs are natural choices for molecular graphs and achieve strong performance on many molecular tasks. To enhance explainability, many GNN explanation methods have been proposed and work well for 2D GNNs. However, 3D GNNs introduce two key challenges: producing chemically meaningful substructures and reducing fidelity loss caused by dense geometric graphs. To address these challenges, I present two methods. 3DGraphX decomposes dense 3D graphs into chemically meaningful 3D motifs, enabling compact explanations that align with chemical intuition. EDMA introduces an energy-based discrete mask approximation approach to reduce the discrepancy between the soft mask optimized during training and the hard mask used for explanation, improving explanation fidelity.

LLMs present different characteristics and challenges compared with GNNs. LLMs can provide a certain level of explanation through step-by-step reasoning, and their natural-language outputs are easy for humans to understand and interpret. However, because LLMs are trained for general-purpose tasks, their performance on scientific tasks often lags behind specialized GNNs. To improve performance, existing methods guide LLMs by providing suggestions through brief feedback, retrieval-augmented generation (RAG), or planner agents. However, these approaches face several limitations, such as vague guidance, introduced bias problems, and high computational cost. To fill the gap, I propose RL-Guider, a lightweight reinforcement-learning agent that converts evaluation feedback into input-specific guidance for molecular optimization. RL-Guider improves over time by accumulating historical experience and transfers efficiently across different LLMs while preserving interpretability.

Together, these efforts aim to provide explanations that are scientifically meaningful and faithful, while also preserving or improving performance on molecular tasks to better meet real scientific needs.

Speaker: Xufeng Liu

Location: New Computer Science-1-Room 115
A talk by Jerome Zhengrong Liang entitled, Machine Learning from Original Images to Texture Patterns: A Paradigm Shift from Non-Medical Application to Medical Diagnosis. Abstract: Artificial intelligence (AI) research for medical diagnosis started soon after human began to use computer, initially called artificial neural network (ANN) and now convolutional neural network (CNN). ANN has been mainly explored to classify the experts' handcrafted features from the original (or raw) images, while CNN has been mainly explored directly on the raw images for both tasks of extracting abstract features and classifying the features. Experimental evidences have been shown that CNN can be trained by a large number of the raw images with experts' scores (or labels) to match or even surpass the experts' performance for both non-medical and medical diagnosis applications. However, the performances of the CNN models as well as the experts on medical diagnosis dropped dramatically when the labels of the raw images were replaced by the corresponding medical pathological reports. Accumulated medical knowledge reveals that the lesion heterogeneity is a footprint of lesion evolution and ecology, and the heterogeneity is an indicator of lesion progress and response to medical intervention. The heterogeneity can be reflected by the image contrast distribution (or texture patterns) across the lesion volume. Image textures have been shown as an effective descriptor of the lesion heterogeneity for computer-aided diagnosis. Can we map the raw images into texture patterns (or images) and train CNN to learn from the texture images? This question is the central theme of this presentation with application to CT Colonography or virtual colonoscopy, a game from AlphaGo to PolypGo. Bio: Jerome Zhengrong Liang, PhD, IEEE Fellow Imaging Research and Informatics Laboratory Department of Radiology, Stony Brook University
Abstract: Self-supervised representation learning (SRL) has emerged as a pivotal advancement in machine learning, offering high-quality data representations without the need for labeled datasets. While SRL has demonstrated enhanced adversarial robustness compared to supervised learning, its resilience against other attack types, particularly backdoor attacks, remains an open question. Recent studies have revealed potential vulnerabilities in SRL, underscoring the necessity for a comprehensive security analysis. However, existing research often extrapolates attacks from supervised learning paradigms, neglecting the unique challenges and opportunities inherent to self-supervised mechanisms.

This thesis proposal aims to address three critical objectives in the realm of self-supervised learning: (1) exploring novel attack vectors, (2) implementing and evaluating practical attacks, and (3) developing robust countermeasures. We focus on two key SRL paradigms: Contrastive Learning and Diffusion Models. For Contrastive Learning, we synthesize existing security vulnerabilities and introduce innovative attack vectors, such as CTRL, to uncover distinctive risks. We conduct a comparative analysis of contrastive and supervised learning approaches in their defense against these threats, exploring potential safeguards and highlighting the limitations of current protective measures in self-supervised contexts. Regarding Diffusion Models, we demonstrate inherent vulnerabilities in their application to adversarial purification.

Our research aims to illuminate the unique challenges posed by emerging attack vectors in self-supervised learning, fostering technical advancements to address underlying security risks in real-world applications. By contributing to the development of more resilient and secure self-supervised representation learning systems, we seek to enhance their reliability and trustworthiness in practical scenarios. This comprehensive examination of SRL's security landscape will provide valuable insights for the broader machine-learning community and pave the way for more robust AI systems.

Join here.
Hidden Biases. Ethical Issues in NLP, and What to Do about Them presented by Dirk Hovy of Bocconi University

ABSTRACT: Through language, we fundamentally express who we are as humans. This property makes text a fantastic resource for research into the complexity of the human mind, from social sciences to humanities. However, it is exactly that property that also creates some ethical problems. Texts reflect the authors' biases, which get magnified by statistical models. This has unintended consequences for our analysis: If our data is not reflective of the population as a whole, if we do not pay attention to the biases contained, we can easily draw the wrong conclusions, and create disadvantages for our users.

In this talk, I will discuss several types of biases that affect NLP models, their sources, and potential counter measures: (1) Bias stemming from data, i.e., selection bias (if our texts do not adequately reflect the population we want to study), label bias (if the labels we use are skewed) and semantic bias (the latent stereotypes encoded in embeddings); (2) Biases deriving from the models themselves, i.e., their tendency to amplify any imbalances that are present in the data; (3) Design bias, i.e., the biases arising from our (the researchers) decisions which topics to analyze, which data sets to use, and what to do with them. For each bias, I will provide examples and discuss the possible ramifications for a wide range of applications, and various ways to address and counteract these biases, ranging from simple labeling considerations to new types of models.

BIO: Dirk Hovey is an associate professor of Computer Science in the department of marketing at Bocconi University. He received his PhD from the University of Southern California in Los Angeles, where he worked as a research assistant at the Information Sciences Institute. 

He works in Natural Language Processing (NLP), a subfield of artificial intelligence. His research focuses on computational social science. His interests include integrating sociolinguistic knowledge into NLP models, using large-scale statistics to model the interaction between people's socio-demographic profile and their language use, and ethics for data science and algorithmic fairness.