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: Modern decision-making increasingly relies on complex data, imperfect models, and limited domain expertise--yet decisions must still be made with confidence and accountability. This talk presents a research perspective on visual analytics as a bridge between data, models, and human judgment. Through three case studies spanning public-health risk analysis, multivariate scientific visualization, and causal model auditing with large language models, I will show how interactive visualization can reveal structure in high-dimensional data, support reasoning under uncertainty, and help humans critically assess both statistical and AI-generated explanations. Together, these examples illustrate how visual analytics enables users not only to explore data, but to form, challenge, and refine beliefs that underpin scientific and societal decisions.

Bio: Klaus Mueller received his Ph.D. in Computer Science from The Ohio State University in 1998. He is a Professor in the Department of Computer Science at Stony Brook University and a Senior Scientist at the Computational Science Initiative at Brookhaven National Laboratory. He currently serves as the Acting Chair of the Department of Technology and Society at Stony Brook. From 2012 to 2015, he was the Founding Chair of the Computer Science Department at SUNY Korea, where he also served as Vice President for Academic Affairs and Finance for two years.
His research interests span visual analytics, explainable AI, machine learning and data science, human-centered responsible AI, fairness, belief modeling and personalized communication, virtual and augmented reality, and computational and medical imaging. Dr. Mueller received the U.S. National Science Foundation Early Career Award in 2001, the SUNY Chancellor's Award for Excellence in Scholarship and Creative Activity in 2011, and the Meritorious Service Certificate and Golden Core Award of the IEEE Computer Society in 2016. In 2018, he was inducted into the U.S. National Academy of Inventors.
To date, he has authored more than 300 peer-reviewed journal and conference papers, which have been cited over 15,000 times. He is a frequent speaker at international conferences, has organized or participated in 18 tutorials, chaired the IEEE Visualization Conference in 2009, served as elected Chair of the IEEE Technical Committee on Visualization and Computer Graphics (VGTC) from 2012-2015, and was Editor-in-Chief of IEEE Transactions on Visualization and Computer Graphics from 2019-2022. He is a Fellow of the IEEE.

Location: NCS 120
Research challenges in using computer vision in robotics systems Abstract The past decade has seen a remarkable increase in the level of performance of computer vision techniques, including with the introduction of effective deep learning techniques. Much of this progress is in the form of rapidly increasing performance on standard, curated datasets. However, translating these results into operational vision systems for robotics applications remains a formidable challenge. This talk with explore some of the fundamental questions at the boundary between computer vision and robotics that need to be addressed. This includes introspection/self-awareness of performance, anytime algorithms for computer vision, multi-hypothesis generation, rapid learning and adaptation. The discussion will be illustrated by examples from autonomous air and ground robots.
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Abstract:
Artificial intelligence (AI)-based methods and computational materials science continue to make inroads into accelerated materials design and development. I will review Al-enabled advances made in the subfield of polymer informatics, with a particular focus on the design of application-specific practical polymeric materials. I will describe exemplar design attempts within a few critical and emerging application spaces, including materials designs for storing, producing, and conserving energy, and those that can prepare us for a sustainable economy powered by recyclable and/or biodegradable polymers. Al- powered workflows help efficiently search the staggeringly large chemical and configurational space of materials, using modern machine-learning (ML) algorithms to solve forward and inverse materials design problems. A practical informatics-based design protocol involves creating a set of application-specific target property criteria, building ML model predictors for those relevant target properties, enumerating or generating a tangible population of viable polymers, and selecting candidates that meet design recommendations. The protocol will be demonstrated for several energy and sustainability-related applications. Finally, I will offer an outlook on the lingering obstacles that must be overcome to achieve widespread adoption of informatics-driven protocols in industrial-scale materials development.

Speaker Bio:
Prof. Ramprasad is the Regents' Entrepreneur, Michael E. Tennenbaum Family Chair and Georgia Research Alliance Eminent Scholar in the School of Materials Science & Engineering at the Georgia Institute of Technology. He is also the CEO and co-founder of Matmerize, Inc. His area of expertise is the development and application of computational and machine learning tools to accelerate sustainable materials development aimed at energy production, storage and utilization. Prof. Ramprasad received his B. Tech. in Metallurgical Engineering at the Indian Institute of Technology, Madras, India, an M.S. degree in Materials Science & Engineering at the Washington State University, and a Ph.D. degree also in Materials Science & Engineering at the University of Illinois, Urbana-Champaign.
Prof. Ramprasad is a Fellow of the Materials Research Society, a Fellow of the American Physical Society, an elected member of the Connecticut Academy of Science and Engineering, and the recipient of the Alexander von Humboldt Fellowship and the Max Planck Society Fellowship for Distinguished Scientists. He has authored or co-authored over 300 peer-reviewed journal articles, 8 book chapters and 8 patents, and has delivered over 300 invited talks at Universities and Conferences worldwide. He is a member of the Editorial Advisory Boards of npj Computational Materials, ACS Materials Letters and Journal of Physical Chemistry A/B/C. He created and chaired the inaugural 2022 Gordon Research Conference on Computational Materials Science and Engineering.

Location: Room 301, Engineering Building
Title: Building foundation models for scientific data Seminar

Speaker: Ruben Ohana, Ph.D. and Michael McCabe, Ph.D - Flatiron Institute, New York

Abstract: Foundation models are very large architectures trained on large-scale datasets and can be used to transfer knowledge from a domain to another. Scientific data, particularly numerical simulations of partial differential equations (PDEs), presents unique challenges due to its complexity and the need for domain expertise to assess prediction quality, complicating the building of the first foundation models in this field. In this talk, we will develop our approach of building foundation models for scientific data, highlighting the requirements and expectations for achieving meaningful results. We will also introduce The Well, a comprehensive collection of datasets encompassing multi-scale simulations of fluid dynamics, astrophysics, and biological systems. The Well serves as a foundation for developing models that generalize across diverse physical phenomena, aiming to accelerate scientific discovery through large-scale learning.

Join Zoom Meeting: https://bnl.zoomgov.com/j/1606898802?pwd=GbbPiLGHlEokDskxjeFheMFWfuboxO.1
Meeting ID: 160 689 8802
Passcode: 281575

Abstract: Spectroscopy and imaging are two primary tools for probing material structures. However, the discovery of trends that guide the design of improved materials is often hindered by intertwined physical interactions or significant experimental noise. In this talk, I will present machine learning approaches that address both challenges. The first part focuses on the interpretation of X-ray absorption spectroscopy (XAS). We developed a controlled projection algorithm, RankAAE, which disentangles coupled structural descriptors in complex datasets and reveals analysis rules for inferring new structural information visually from spectra. The second part targets transmission electron microscopy (TEM) imaging of material structures. We developed a machine learning model capable of denoising extremely noisy images, while demonstrating strong out-of-distribution generalization. I will describe the construction of these models and demonstrate their effectiveness through representative scientific case studies.

Bio: Dr. Xiaohui Qu is a Staff Scientist in the Theory and Computation Group at the Center for Functional Nanomaterials (CFN), Brookhaven National Laboratory. His research focuses on developing interpretable machine learning and data analytics methods for materials science, with an emphasis on extracting structural insights from X-ray absorption spectroscopy and transmission electron microscopy. Dr. Qu earned his B.S. in Environmental Engineering and Ph.D. in Environmental Science from Shandong University, China, followed by postdoctoral research in Physics at Nanyang Technological University, Singapore, in Chemistry at Universidade Nova de Lisboa, Portugal, and in Materials at Lawrence Berkeley National Laboratory.

Location: IACS Seminar Room


Event Details & Calendar Link (includes zoom info): https://calendar.stonybrook.edu/site/iacs/event/iacs-seminar-speaker--xiaohui-qu-brookhaven-national-lab/