Abstract: Materials used in extreme environments, such as high temperatures, irradiation, and stress, often fail due to rapid defect generation and microstructural evolution, and traditional approaches cannot explore the vast design space needed for next-generation alloys. I will present a machine learning framework powered by massive computing that links individual atomic motion to microstructural evolution. Neural network kinetics models trained on first-principles data map vacancy barrier spectra and capture correlated diffusion in multicomponent alloys, revealing design strategies to suppress radiation damage. At larger scales, simulations uncover dislocation patterning and distinguish between confined and extended slip bands, offering new insight into collective dislocation motion and deformation instabilities. By integrating AI-driven modeling, large-scale computing, and experimental validation, my research goal is to accelerate the discovery of damage-tolerant materials and advance fundamental understanding of defect physics in extreme environments.

Speaker Bio: Penghui Cao is an Associate Professor in Mechanical and Aerospace Engineering at the University of California, Irvine, with a joint appointment in Materials Science and Engineering. He received his PhD in mechanical engineering from Boston University and subsequently worked as a Postdoctoral Associate in the Department of Nuclear Science and Engineering at the Massachusetts Institute of Technology from 2014 to 2018. Dr. Cao's research focuses on understanding the fundamental mechanisms that govern radiation responses and microstructure evolution in materials, and on developing advanced alloys for high-performance nuclear energy systems. His lab advances computational and modeling algorithms, integrates advanced manufacturing techniques to tailor microstructures, and leverages state-of-the-art electron microscopy to characterize and assess underlying mechanisms. He is the recipient of the DOE Early Career Research Program Award and the UCI Samueli School's Mid-Career Award for Faculty Excellence in Research.

Location: Institute for Advanced Computational Science, Seminar Room

*This seminar will be held in-person and online. Zoom link below*

Join Zoom Meeting: https://stonybrook.zoom.us/j/96410717491?pwd=3WGMwbLYNMSbI2IF160VXkvv2JmCQ1.1

Meeting ID: 964 1071 7491
Passcode: 399333

Educational objectives:
  1. Describe how bias enters AI systems across the development pipeline, using aesthetic facialevaluation as a case study, and identify mitigation strategies applicable at each stage.
  2. Apply the PROBAST+AI risk-of-bias framework to critically appraise the methodologicalquality of a published prediction model using regression or machine learning methods.
  3. Compare the discriminative performance of multiple modeling architectures against anexisting clinical risk calculator on a large benchmarking dataset.
  4. Recognize the complementary roles of clinical expertise and informatics methodology indefining clinically meaningful AI project objectives, predictors, and evaluation criteria.


Speaker: Dr. Anisha R. Kumar, MD Clinical Assistant Professor, Surgery and Biomedical Informatics, Stony Brook University School of Medicine


Location: MART Building, Room 7M-0602 (7th Floor)

Remote Access: https://stonybrook.zoom.us/j/95617197636?pwd=KytzZ2pVRG9SZGpKZUtpNXJISjNjZz09Meeting ID: 95617197636 | Passcode: 924293

Description:

As artificial intelligence and data science reshape the global information landscape, libraries are emerging as key players in both technological innovation and ethical stewardship. This international Zoom discussion brings together library professionals and educators from the U.S., Philippines, and Hong Kong to explore how institutions are integrating AI and data into their pedagogy and services.

Panelists will share concrete examples from their own libraries--ranging from data literacy initiatives to increasing discoverability. The conversation will also examine regional trends in librarianship, spotlighting how institutions in Asia are navigating the evolving role of data and AI.

Join us for a global conversation that highlights the transformative potential of libraries as hubs for innovation and critical inquiry in the age of AI.

Register for this free Zoom panel.

Panelists:

Ahmad Pratama is a Faculty Member and Associate Librarian at Stony Brook University Libraries, where he is working to build a comprehensive, campus-wide data literacy program within the Libraries. As the Data Literacies Lead, his work focuses on empowering students, faculty, and staff to critically and ethically engage with data and AI, including the development of a credit-bearing course in Critical Data & AI Literacies supported by an EDGE Fund Award from the Provost's Office. Previously, Dr. Pratama served as an Associate Professor of Information Technology, and his research and teaching explore the intersections of technology, policy, and society with a focus on data, AI, and innovation in higher education.

Dan Anthony Dorado is a full-time faculty member at the U.P. School of Library and Information Studies, where he teaches information technology, management and marketing, research methodology, and quantitative research. He was also the director of the Diliman Learning Resource Center under the Office of the Vice Chancellor for Student Affairs. Before that, he was an Information Specialist at the College of Engineering Library, in charge of the System and Network Administration and The Learning Commons. He completed his master's degree at the Technology Management Center in U.P. Diliman and is currently pursuing his PhD in Data Science. As a member of Sync.Bio.Optics laboratory and the Publics, Archives, and Data (PANDA) Lab, his research specialization covers Computational Methods, Open Education, Critical Data Studies, and Radical Statistics.

Ryun LEE is Associate University Librarian at The Chinese University of Hong Kong Library, leading Digital Initiatives and Library IT and Systems. He drives digital innovation through emerging technologies, particularly artificial intelligence to enhance services, streamline operations, and support CUHK's mission in research, education, and knowledge advancement. With a background in cataloging and digital repository development, Ryun leads projects in digitization, OCR, data visualization, text and network analysis, GIS, and digital scholarship. He actively promotes knowledge graph applications in Hong Kong studies and oversees efforts to digitize and preserve resources related to Hong Kong and Southern China. His recent work focuses on creating seamless digital experiences and developing data-driven infrastructure. He is currently exploring AI-driven approaches to digitization workflows and entity extraction, aiming to improve access, discovery, and long-term preservation of library materials.

The Center of Excellence in Wireless and Information Technology (CEWIT) will host the 16th International Conference on Emerging Technologies for a Smarter World (CEWIT2020) virtually on November 5, 2020. The conference will center on the four major fields which are penetrating our business and personal lives: Machine Learning, Artificial Intelligence, Blockchain and Computational Medicine. For more info visit: https://www.cewit.org/.  
ICB&DD 19th Annual Symposium

Iwao Ojima, Director, ICB&DD
Ivet Bahar Chair, Organizing Committee
Dima KozakovCo-Chair, OrganizingCommittee

There will be poster sessions on projects conducted in the ICB&DD member's laboratories aswell as other laboratories in the area. Awards will be given to the best three posters.

Please see the link for the registration and poster sessions in:
https://www.stonybrook.edu/commcms/icbdd/https://forms.gle/Wh4UzVx9U4HWStXb8

The Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

For more information and registration, please visit the official website.


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
Talk by Zhenhua Liu to be followed by AI Institute updates


Abstract: Decision making with uncertainty has been studied in multiple communities extensively. Recently, online optimization has gained popularity partially because of its promising performance guarantees by incorporating predictions. In this talk, I will provide an overview of our work on algorithm designs for online optimization and its applications. Then, I will talk about our recent work in ACM Sigmetrics 2019 on choosing predictions and control algorithms simultaneously and dynamically. Finally, I will discuss some ongoing efforts and collaboration opportunities.

Bio: Zhenhua Liu is currently an assistant professor in the Department of Applied Mathematics and Statistics at Stony Brook University. He is also affiliated with the Department of Computer Science, the AI Institute and the Smart Energy Technology Cluster. He received his PhD degree in Computer Science from California Institute of Technology. His current research interests include cloud computing, online optimization and learning, smart grid, market design and distributed control. His research combines rigorous analysis and system design, and goes from theory, to prototype, and eventually to industry to make real impacts.
Abstract Driving intelligence test is critical to the development and deployment of autonomous vehicles. The prevailing approach tests autonomous vehicles in life- like simulations of the naturalistic driving environment. However, due to the high dimensionality of the environment and the rareness of safety-critical events, hundreds of millions of miles would be required to demonstrate the safety performance of autonomous vehicles, which is severely inefficient. We discover that sparse but adversarial adjustments to the naturalistic driving environment, resulting in the naturalistic and adversarial driving environment, can significantly reduce the required test miles without loss of evaluation unbiasedness. By training the background vehicles to learn when to execute what adversarial maneuver, the proposed environment becomes an intelligent environment for driving intelligence testing. We demonstrate the effectiveness of the proposed environment in a highway-driving simulation. Comparing with the naturalistic driving environment, the proposed environment can accelerate the evaluation process by multiple orders of magnitude. ZOOM LINK: Meeting ID: 950 6760 3617; Passcode: 426506 https://stonybrook.zoom.us/j/95067603617?pwd=dXQybEprSkNlTFY3WHlWYjViUG95UT09 Bio Professor Henry Liu is a professor in the Department of Civil and Environmental Engineering at the University of Michigan, Ann Arbor. He is also a Research Professor at the University of Michigan Transportation Research Institute and the Director for the Center for Connected and Automated Transportation (USDOT Region 5 University Transportation Center). Prof. Liu conducts interdisciplinary research at the interface between civil and mechanical engineering. Specifically, his scholarly interests concern traffic flow monitoring, modeling, and control, as well as testing and evaluation of connected and automated vehicles. He has published more than 100 refereed journal papers and is listed as one of the top 50 leading authors in the past 50 years (1969-2019) in the prestigious Transportation Research journal. Professor Liu and his work have been widely recognized in public media for promoting smart transportation innovations. He has appeared on media outlets including CNBC, Forbes, Technode, etc. In 2019, Professor Liu was invited to testify on national transportation research agenda in front of the US House Subcommittee on Research and Technology. Professor Liu has nurtured a new generation of scholars, and some of his PhD students and postdocs have joined first class universities such as Columbia University, Purdue University, RPI, etc. Prof. Liu is the managing editor of Journal of Intelligent Transportation Systems.