New York Scientific Data Summit (NYSDS) is a premier annual conference that brings together researchers and thought leaders from academia, national labs and industry to exchange ideas and foster collaboration focused on data-driven science and technology. Co-hosted by Brookhaven National Laboratory and the Institute for Advanced Computational Science (IACS) at Stony Brook University, NYSDS 2025 will take place on September 11-12, 2025, in the SUNY Global Center in New York City.

NYSDS 2025 will spotlight artificial intelligence (AI), machine learning (ML) and robotics - fields currently at a pivotal point with transformative impacts on science and technology. From accelerating computationally demanding simulations to discerning signals from noisy data, AI/ML has become an integral part of the scientific workflows. Despite many advances, challenges remain to ensure that AI/ML applications are reliable, explainable and trustworthy.

Robotics, a growing field that couples AI with physically actuated mechanical bodies, has seen increased interest in areas spanning science, technology and manufacturing. The need for real-time decision-making and control, along with the intricate morphology of robots, makes robotics an intriguing application of AI, advanced computing and optimization.


This NYSDS 2025 is open to the public. To be eligible to attend, all participants must register online by August 30, 2025. For questions or assistance with registering, please contact the Summit Coordinator.

Register here.

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.

The University at Albany will host a national gathering of professionals and academics that will focus on the transformative potential of AI while addressing the ethical, technical and institutional challenges posed by AI in education.

The Symposium will feature dynamic keynotes, hands-on workshops and engaging conversations with other participants and subject matter experts.

Topics

  • Teaching, Learning and Workforce Development
  • Research, Creative Arts, and Practice
  • Ethics, Governance and Academic Administration

For event information and registration, visit Events@Albany.
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.

Abstract: Much like other AI for Science domains, polymer design poses significant challenges. It requires grounding in empirical data and physical laws, precise handling of domain-specific structured representations, and compositional reasoning over multiple interacting constraints--all while working with limited data.

To address these limitations, we introduce PolyBench, a large-scale benchmark comprising over 125K polymer design and analysis tasks grounded in verified experimental and synthetic data. PolyBench includes tasks created from a wide range of data sources and presents diverse structural, property-driven, and synthesis-oriented reasoning problems. Tasks in PolyBench are organized from simple to complex analytical reasoning problems, enabling generalization tests and includes diagnostic probes to evaluate model capabilities. Additionally, to support effective domain alignment, we propose a knowledge-augmented reasoning distillation framework that enriches the dataset with structured chain-of-thought supervision derived from expert-informed reasoning strategies.

Small language models (7B-14B parameters) trained on PolyBench substantially outperform comparably sized baselines and, in many cases, exceed the performance of larger closed-source frontier models on polymer reasoning tasks, while also demonstrating improved transfer to external polymer benchmarks. Last, we conduct a diagnostic study that reveals a compositionality gap: despite strong performance on decomposed sub-questions, models struggle to integrate multiple interacting constraints and intermediate reasoning steps, highlighting fundamental limitations in current scientific language models.

Speaker: Dikshya Mohanty

Location: NCS 115/Online

Zoom: https://stonybrook.zoom.us/j/94746001760?pwd=BCAd8gu7cXLn3PXM6kkbh11V6r0Mr7.1
Meeting ID: 947 4600 1760 Passcode: 987917

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.

Designed for faculty, staff, presidents, provosts, academic leaders, student affairs professionals, IT specialists, librarians, researchers, administrators, institutional decision-makers, and other higher education stakeholders, the conference highlights practical strategies institutions can implement now while exploring longer-term governance, policy, and ethical considerations. Participants will leave with concrete tools, cross-institutional insights, and collaborative connections that support mission-aligned AI innovation.

Hosted by: AAC&U

Location: Atlanta, GA and Virtual

Register here.


Abstract: Computer vision seeks to extract semantic and geometric information from images and videos, serving as the perceptual foundation for intelligent systems such as robots and autonomous vehicles. Over the past decade, deep learning has driven remarkable progress in the field, advancing capabilities from 2D recognition to 3D reconstruction. However, the current purely data-driven paradigm faces fundamental challenges, including data inefficiency, curse of high dimensionality, and limited understanding of visual entities beyond individual objects.

In this talk, I will present my recent research on modeling and learning rich visual structures to address these challenges. First, I will introduce a novel framework that integrates explicit visual dependency modeling with deep learning for 2D and 3D dense prediction. Next, I will demonstrate how unfolding the manifold structure of visual data enables unsupervised semantic segmentation. Finally, I will present a recent project that represents, parses, and learns the geometric compositionality of 3D objects to facilitate self-supervised part-whole reconstruction. Through these efforts, I aim to bridge the gap between data-driven deep learning and visual structure modeling, paving the way for more efficient, generalizable, and interpretable computer vision models.

Bio: Dr. Wei Tang is an Assistant Professor in the Department of Computer Science at the University of Illinois Chicago (UIC). He obtained his Ph.D. in Electrical Engineering from Northwestern University, where his dissertation was honored with a Best Dissertation Award. His research interests include computer vision, digital image processing, and machine learning. Dr. Tang has served as an associate editor for several international journals, including Pattern Recognition and Machine Vision and Applications, and as an area chair for leading conferences, including CVPR, ICCV, and WACV. His research has been funded by the National Science Foundation (NSF) and industry partners such as Motorola and Wormpex AI Research.


Location: NCS 115

Zoom: https://stonybrook.zoom.us/j/4624091659?omn=95178138684&jst=3