West Campus - SAC- Student Activities Center - Ballrooms A & B 100 Nicolls Road Stony Brook NY 11794 Job Fair.jpg The Career Center invites Alumni Employers and Job Seekers to the IT/Computer Science Job and Internship Fair this spring. Job Seekers: A job fair is an opportunity for you to present yourself professionally in person to a potential employer, while showcasing your communication skills. Get more information Alumni Employers: Held in both the fall and spring semesters, this event is ideal for employers looking to fill internship, co-op, part-time and full-time opportunities in the field of information technology (i.e. Software Engineering, Network Administration, Web Development, etc.). Register here to recruit top SBU talent.
AI is everywhere and so are the privacy concerns that come with it. At its core, the most common forms of AI we use today are online digital services and thus inherit the usual privacy risks. We'll take a look at indirect prompt injection- a technique that can trick AI tools into revealing or extracting private information as well as techniques being used in academic contexts to manipulate systems and even mislead researchers.

Register here for the online session.
18th Annual Engineering Ball Flowerfield, St. James, NY Thursday April, 2nd, 7:00 to 10:00 pm Pick up your tickets in 231 Engineering (Monday - Friday, 10:00 am to 4 pm) Presenting Partner: L3Harris

Abstract: Implicit functions have long been a fundamental representation for both 2D and 3D objects in computer graphics, playing a significant role in the field's early development. With the rise of 3D deep learning and the rapid advancement of neural rendering techniques, implicit representations of 3D shapes have regained significant attention in recent years. In this talk, I will present several recent research projects focusing on implicit function-based 3D reconstruction and neural rendering. Furthermore, I will discuss potential future developments in this dynamic and rapidly evolving field.

Biography: Ying He is an Associate Professor at the College of Computing and Data Science, Nanyang Technological University, where he also serves as the Director of the Centre for Augmented and Virtual Reality. His research interests lie in geometric computation and analysis, with applications spanning computer graphics, 3D vision, computer-aided design, multimedia, and wireless sensor networks. Dr. He is an active member of the technical program committees for major conferences on geometric modeling and has served on the editorial boards of IEEE Transactions on Visualization and Computer Graphics, Computer Graphics Forum, and Computational Visual Media. He has also taken on key leadership roles as General/Program Co-Chair for several conferences, including Shape Modeling International (SMI) 2022, Solid and Physical Modeling (SPM) 2022 & 2023, Geometric Modeling and Processing (GMP) 2014 & 2021, and Computational Visual Media (CVM) 2020. For more information, please visit https://personal.ntu.edu.sg/yhe/

Location: NCS 115

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.

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.

Over the past decade, researchers in neuroscience, psychology and artificial intelligence have come together to build advanced computer models that mimic how our brain processes what we see. These models are designed to closely copy the brain's visual system, all the way to a key area called the inferior temporal cortex, which plays an important role in recognizing objects.

Because these computer models can be fully observed, scientists can use them to make detailed predictions about how the brain works -- something older, more theoretical models could not do.

Dr. James DiCarlo's work explores whether these computer digital twin models of the brain could help guide safe, non- invasive ways to infl uence brain activity. In his talk, he explains how such a model could be used to design specific patterns of light. When this carefully designed light is added to what the eye naturally sees, it can precisely influence activity in groups of neurons in the inferior temporal cortex.

Since neural activity in this visual brain area may be connected to emotional states like anxiety, this research could eventually open the door to non-invasive approaches that may benefit mental well-being in the future.

Speaker: James J. DiCarlo, MD, PhD, Peter de Florez Professor, MIT Brain and Cognitive Sciences, and Director, MIT Siegel Family Quest for Intelligence

Location: Staller Center Main Stage

The event will be livestreamed at stonybrook.edu/live

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.
AI Seminar: Computational Pathology: Deep Learning, Classification and
Predicting the Future  - Joel Saltz

Abstract:  Pathologists have been looking at tissue through microscopes since the 1800s.  During each pathologist's career,  he or she views slides having  roughly 1,000,000,000,000 cells. Deep learning methods are rapidly being developed to assimilate the huge amount of information walked inside of tissue images and to use this information to predict outcomes and responses to treatments.

Stony Brook is a leader in this type of multi-disciplinary work. I will provide an overview of Stony Brook computational Pathology efforts and articulate how these have the potential to create biomedical advances as well as to drive development of new computer science. 


Bio: Dr. Joel Saltz is a leader in research on advanced information technologies for large scale data science and biomedical/scientific research. He has developed innovative pathology informatics methods, including: the first published whole slide virtual microscope system; pioneering pathology computer-aided diagnosis techniques; and methods for decomposing pathology images into features and linking those features to cancer omics, response to treatment and outcome. He has broken new ground in big data through development of the filter-stream based DataCutter system, the map-reduce style Active Data Repository and the inspector-executor runtime compiler framework. He has also been an active contributor in clinical informatics, having developed
predictive models for hospital readmissions, point of care laboratory testing quality assurance systems, decision support systems for electrophoresis interpretation and graphical user interfaces to support clinical data warehouse queries. Dr. Saltz has been a pioneer in establishing the field of biomedical informatics; he founded and built two highly successful departments of biomedical informatics, one at Ohio State University and one at Emory University. In 2013, he came to Stony Brook as Vice President for Clinical Informatics and Founding Department Chair of Biomedical Informatics - to create a living laboratory for biomedical informatics and to create a third unique biomedical informatics department dually housed in the School of Medicine and the College of Engineering. Dr. Saltz is trained both as a computer scientist and as a physician through the MSTP program at Duke University. He has deep experience in computer science, having served on the computer science faculties at Yale University and the University of Maryland. He completed his residency in clinical
pathology at Johns Hopkins University and he is a practicing, board-certified clinical pathologist.