Join the Center of Excellence in Wireless and Information Technology (CEWIT) and their co-host IEEE-USA for a livestream panel discussion on Generative Artificial Intelligence (Gen AI). In this engaging livestream, we will dive into the technologies that continue to transform what is possible and explore the dynamic intersection of innovation, creativity, ethics, and Gen AI.

CEWIT is joined by Stony Brook University experts who will provide their insights and perspectives on this rapidly changing technology.

Meet the Panel

Laura Lindenfeld, PhD
Executive Director
Alan Alda Center for Communicating Science®
Dean
School of Communication & Journalism
BIO

Margaret Schedel, PhD
Associate Professor
Composition and Computer Music
Co-Founder
Lyrai
BIO

Steven Skiena, PhD
Interim Director
AI Innovation Institute
Distinguished Professor
Computer Science
BIO

Vivian Zhang
CTO/School Director
NYC Data Science Academy
Chief Data Officer
GoDental.ai
BIO


Register here.
CSE 600 Talk: Squeezing Software Performance via Eliminating Wasteful Operations presented by Xu Liu

ABSTRACT: Inefficiencies abound in complex, layered software. A variety of inefficiencies show up as wasteful memory operations, such as redundant or useless memory loads and stores. Aliasing, limited optimization scopes, and insensitivity to input and execution contexts act as severe deterrents to static program analysis. Microscopic observation of whole executions at instruction- and operand-level granularity breaks down abstractions and helps recognize redundancies that masquerade in complex programs. In this talk, I will describe various wasteful memory operations, which pervasively exist in modern
software packages and expose great potential for optimization. I will discuss the design of a fine-grained instrumentation-based profiling framework that identifies wasteful operations in their contexts, which guides nontrivial performance improvement. Furthermore, I will show our recent improvement to the profiling framework by abandoning
instrumentation, which reduces the runtime overhead from 10x to 3% on average. I will show how our approach works for native binaries and various managed languages such as Java, yielding new performance insights for optimization.

BIO: Xu Liu is an assistant professor in the Department of Computer Science at College of William & Mary. He obtained his PhD from Rice University in 2014 and joined the College of William & Mary in the same year. Prof. Liu works on building performance tools to pinpoint and optimize inefficiencies in HPC code bases. He has developed several open-source profiling tools, which are used worldwide at universities, DOE national laboratories and industrial companies. Prof. Liu has published a number of papers in high-quality venues. His papers received Best Paper Award at SC'15, PPoPP'18, PPoPP'19 and ASPLOS'17 Highlights, as well as Distinguished Paper Award at ICSE'19. His recent ASPLOS'18 paper has been selected as ACM SIGPLAN Research Highlights in 2019 and nominated for CACM Research Highlights. Prof. Liu is the receipt of 2019 IEEE TCHPC Early Career Researchers Award for Excellence in High Performance Computing. Prof. Liu served on the program committee of conferences such as SC, PPoPP, IPDPS, CGO, HPCA and ASPLOS.
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 15 minutes) by multiple students. Students can register for 1 credit for CSE656. Registered students must attend and present a minimum of 2 talks. Everyone else is welcome to attend. Fill in https://forms.gle/q6UG9ygauLp2a8Po8 to subscribe to our mailing list for further announcement.

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

The Department of AI and Society (AIS) at the University at Buffalo is hosting a two-day AI and Society Workshop focused on building AI systems by society, for society. This workshop brings together researchers and community organizers to explore how AI systems can be developed through meaningful collaboration across disciplines.

Topics include:

  • Labor and AI
  • Public services and AI
  • Community-centered AI systems
  • Intersections of humanities, social sciences, arts, and computing

The vision of UB's Department of AI and Society is to create a future where AI systems are built by society, for society. AIS centers community engagement at every stage of AI development through collaboration across disciplines and sectors. AIS was established with a $5 million grant from SUNY, and this workshop is made possible through that support.

Who Should Attend?

  • Researchers
  • Students
  • Community organizers
  • Practitioners interested in AI's societal impact

More about the event

Register here

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


Imagine machines that can see beyond human limitations--drones locating hidden survivors, cameras predicting structural failures, or medical devices detecting tumors beneath the skin. Traditional vision systems are constrained by the boundaries of human perception, missing vast information present in light interactions. This talk explores the development of advanced vision systems that capture underutilized dimensions of light, model intricate light-scene interactions, and extract hidden 3D information--around corners, beneath surfaces, and at high speeds. By jointly developing novel imaging hardware, efficient rendering models, and physics-based learning algorithms, we aim to transcend conventional vision capabilities--unlocking critical applications in autonomous navigation, structural monitoring, and non-invasive medical imaging.

Speaker Bio:


Akshat Dave is a Postdoctoral Associate at MIT Media Lab in the Camera Culture group working with Prof. Ramesh Raskar. He received his Ph.D. from Rice University ECE Department in 2023 where he was advised by Prof. Ashok Veeraraghavan. His research lies at the intersection of applied optics, computer graphics, and computer vision. His research focuses on developing vision systems that go beyond human perception. His work has been recognized by Rice University's Best Thesis Award, OSA Best Paper Prize, and fellowships by Texas Instruments and Qualcomm.