The Vedanta Forum is devoted to one of humanity's oldest and most profound pursuits -- thinking. Thinking about who we truly are: the one that remains constant through childhood and old age, through waking, dream, and deep sleep. Thinking about the source and cause of creation, and its relationship to what inheres in us.

Across history, such thinking, both meditative and scientific, has been aimed at these questions. The ancient Upanishads proclaimed, Tat Tvam Asi -- Thou Art That -- revealing the non-dual identity of the individual and the ultimate reality. Centuries later, modern scientists such as Schrödinger and Bohr echoed similar intuitions about the unity of existence.

Over time, many philosophical approaches, traditions, and interpretive schools have arisen from such inquiry, each offering unique perspectives. The Forum will:

  • Focus on universal approaches and traditions and examine their teachings,

  • Foster comparative studies, and

  • Explore the practical benefits to society from such thinking,

through scholarly studies, dialogue, and debate also promoting accessibility to all qualified seekers. Additionally, the Forum will explore how these reflections can enrich life, education, and even technology.

Location: NCS 120 (New Computer Science), Engineering Dr, Stony Brook, NY 11794.

The program is available at: https://www.vedantaforum.org/events/program

Abstract: How do humans learn the sound patterns of their language? Despite a variety of methods and advances in phonotactic learning, there is still a paucity of computational research, methods and data for languages with tones. In this talk, I will explore this question specifically in light of tone languages, where pitch plays a crucial role in distinguishing words' meaning. I provide an implementation of the Bottom-Up Factor Inference Algorithm over Autosegmental Representations (BUFIA-AR), which learns the rules governing possible tone patterns. Using a dataset of Hausa, a West African tone language, the algorithm successfully identifies patterns that are not permitted in the language. These results (i) confirm long-standing linguistic generalizations, (ii) make more specific predictions about exceptional cases, and (iii) reveal previously unnoticed patterns. The results show how mathematical models of sound structure can be brought into dialogue with both linguistic theory and computational learning, highlighting the broader potential of formal approaches to capture human linguistic knowledge.

Bio: Han Li is a fifth-year Ph.D. student in Linguistics department, specializing in computational linguistics under the supervision of Professor Jeff Heinz. Her research focuses on how sound patterns in language can be formally represented and computationally learned, bridging theoretical linguistics and computer science.

Location: Institute for Advanced Computational Science, Seminar Room

Zoom Meeting: https://stonybrook.zoom.us/j/94043459206?pwd=3ra47h8HghOFRfobRBjZaDMyTwialr.1
Meeting ID: 940 4345 9206
Passcode: 332717

Abstract: Computational pathology has revolutionized cancer diagnosis and research through the analysis of digitized whole slide images (WSIs). However, their giga-pixel size creates two intertwined bottlenecks: computational inefficiency, as prohibitive GPU memory makes standard end-to-end (E2E) training infeasible, and label inefficiency, as expert annotation is tedious and expensive. This dissertation confronts both challenges through novel architectures, training paradigms, and self-supervised learning methods for efficient WSI analysis.
To improve computational efficiency, this dissertation first introduces a locally supervised learning paradigm that enables E2E training on entire WSIs by partitioning a network into gradient-isolated modules, circumventing the memory bottleneck of backpropagation. Second, it presents Prompt-MIL, a parameter-efficient fine-tuning framework training only a few prompts to guide large pre-trained models, reducing trainable parameters, memory, and training time. Third, this work proposes 2DMamba, the first intrinsic Mamba architecture that preserves the crucial 2D spatial structure of images, overcoming the spatial discrepancy in 1D models. Fourth, it presents Locally Bi-directional Mamba (LBMamba), whose hardware-aware local backward scan integrates bi-directional scanning into a single forward pass, improving the throughput-performance trade-off of Mamba models.
To improve label efficiency, this dissertation proposes a precise location based matching strategy for self-supervised dense contrastive learning, which allows a local patch in one augmented view to match multiple overlapping patches in another, producing more accurate correspondences and superior features for dense prediction tasks like segmentation and detection. Additionally, to better scale multi-channel cell imaging modalities, this dissertation introduces ChannelSFormer, a channel-agnostic vision transformer that disentangles spatial and channel-wise reasoning through divided attention and channel class token, enabling effective representation learning across variable channel configurations in both self-supervised and supervised settings.
In summary, this dissertation presents a holistic investigation into the efficiency bottlenecks in computational pathology. Through these combined contributions in model architecture, training paradigms, and self-supervised learning, this work establishes a more scalable, efficient, and powerful computational framework for analyzing giga-pixel pathology images.

Speaker: Jingwei Zhang

Location: NCS 220

Zoom: https://stonybrook.zoom.us/j/93175806292?pwd=xbtxnQyYGoThz5B1DyJxJxPF9lxiJE.1
Meeting ID: 931 7580 6292
Passcode: 314091



Abstract: The current approach to materials design, driven by strategic experimentation and supported by physics-based simulation across relevant scales, has been the standard for decades. While the theoretical component in this workflow provides valuable understanding of material behavior, it often fails to deliver actionable guidance for implementation. Advances in artificial intelligence and machine learning (AI/ML), together with high-performance computing (HPC), now offer a viable pathway to close this gap and accelerate both discovery and process optimization. This presentation will outline practical approaches for integrating AI/ML with HPC-enabled, high-throughput computation to explore high-dimensional search spaces. Examples will include the development of engineering alloys for extreme environments, the use of neural networks to rapidly improve computational thermodynamic models, and vapor processing optimization for the manufacturing of ultra-high-temperature ceramics. I will highlight how scientific insight and domain expertise remain essential for translating surrogate model predictions into impactful outcomes. Finally, I will conclude with current challenges and future opportunities for AI/HPC-driven materials research.

Speaker: Dongwon Shin
This seminar will be held in person and online

Join Zoom Meeting: https://stonybrook.zoom.us/j/93730374357?pwd=YDLJ7ELqOQnTZEQhlN8Pa4TuhaiFK8.1
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.
Title: AI-Driven Target Selection Methods for Touch and Gaze Input

Abstract: Accurately selecting targets is an essential aspect of  Human-Computer Interaction. Erroneous selections can cause tedious undo and redo actions. Additionally, some selection errors are non-reversible and can lead to undesirable consequences. However, high-accuracy target selection remains a challenge on touchscreen devices due to the small target size and imprecise touch inputs, and in gaze interaction because of the gaze tracking noise and no easy-to-use selection action. We first propose ReLM, a Reinforcement Learning-based Method for touchscreen target selection. ReLM can automatically show suggestions and require a second touch if the input is ambiguous, and can directly select a target candidate when the input is certain. Our empirical evaluation shows that ReLM reduces the error rate from 6.92% to 1.63%, and the selection time from 2.23s to 1.59s over Shift, an existing suggestion-based method. Compared to BayesianCommand, a direct selection-based method, our ReLM reduces the error rate from 3.64% to 0.89%, while increasing the selection time by only 200 ms. Secondly, we investigate how to improve target selection performance for gaze interaction. We propose BayesGaze, an eye-gaze based target selection method. It accumulates the signal of each gaze point for selecting a target calculated by Bayes Theorem, and uses a threshold mechanism to determine the target selection. Our investigation shows that BayesGaze improves target selection accuracy and speed over a dwell-based selection method, and the Center of Gravity Mapping method.

All are welcome. Here  is the zoom meeting link:
https://stonybrook.zoom.us/j/93130953411?pwd=Rm5IRlVPQ3M0cHJsTXpCVFljUlFGUT09Meeting ID: 931 3095 3411Passcode: 999413

What AI tools are available to help with the scholarly research process? Are they helpful? What do they do and is it worth the time and energy to try them out? Join librarian Christine Fena to explore and compare established and emerging AI research tools such as Elicit, Scite, Consensus, and Undermind. The workshop will not offer a lengthy tutorial on how to use any of these tools, but will provide a starting point to understanding what they are, what new ones are emerging, and how AI research assistants might bring changes to your search process. All are welcome!

Register for this Zoom workshop.

CSE 600 Talk: Haibin Ling - Computer Vision Research and Applications


Abstract: Having been intensively studied over half a decade, computer vision has evolved as a broad research area and become mature in many applications. In this talk, we will summarize our work in computer vision in both core vision topics and application-oriented ones. In particular, for core vision problems, we will report studies on visual tracking, visual matching and visual detection; for applications, we will describe our work on medical image analysis, intelligent transportation, smart projector systems and preliminary work on material property prediction.

Bio: Haibin Ling received the BS and MS degrees from Peking University in 1997 and 2000, respectively, and the PhD degree from the University of Maryland, College Park, in 2006. From 2000 to 2001, he was an assistant researcher at Microsoft Research Asia. From 2006 to 2007, he worked as a postdoctoral scientist at the University of California Los Angeles. In 2007, he joined Siemens Corporate Research as a research scientist. From 2008 to 2019, he worked as a faculty member of Temple University. In fall 2019, he joined the Department of Computer Science of Stony Brook University where he is currently a SUNY Empire Innovation Professor. His research interests include computer vision, augmented reality, medical image analysis, and human computer interaction. He received the Best Student Paper Award at the ACM UIST in 2003, and the NSF CAREER Award in 2014. He serves as Associate Editor for several journals including IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), Pattern Recognition (PR), and Computer Vision and Image Understanding (CVIU). He has served or will serve as Area Chair for CVPR 2014, 2016, 2019 and 2020.

Learn which AI models are available at SBU and how to access them.

As AI becomes part of our everyday work, it's important to know which AI models are available at Stony Brook and how to access them. In the first half of this session, we'll show you how to access Stony Brook's Gemini and Microsoft Copilot models and how to make sure you're using the university-approved environment. In the second half, we'll review Stony Brook's Data Classification policies so you know what institutional data can be used with these AI tools and what information should remain protected, including sensitive data. This session will provide the latest campus guidance so you can use AI confidently and responsibly.

Register here.