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.

The University's Main Commencement Ceremony will take place on Friday, May 23, 2025 at 11 am at Kenneth P. LaValle Stadium. Gates open at 10 am.

All guests need a valid ticket to enter LaValle Stadium - no exceptions. Children age 1 and older require a ticket. Seating is first-come, first-served.

Register here.

Chat with Sociology faculty as they share their paths to StonyBrook-what inspired their careers, what led them to teaching,and the experiences that shaped their academic journey.

Dr. Yongjun Zhang

Assistant Professor of Sociology, Departments of Sociology and AAAS

Join this opportunity to talk to Yongjun Zhang about his new interest in the following responsible usage of AI in addressing climate and health issues. Lunch will be served.

Location: SBS Level 4- Sociology Reading Room

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You are cordially invited to attend the biweekly Brookhaven AI Mixer (BAM). BAM includes three short talks on AI research happening at BNL, followed by an open mixer over coffee and snacks for everyone to network and discuss all things AI. The first half hour will consist of presentations that will be available via ZOOM, and the second half hour will be for in person only networking.

Join us every other Tuesday at noon in CDSD's Training Room (building 725, 2nd floor) to learn about interesting AI methods and applications, engage with potential collaborators, prepare for pending FASST funding calls, and build a community of AI for Science at BNL.

Tuesday, January 7, 2025, 12:00 pm -- CDS, Bldg. 725, Training Room


Speakers

Deyu Lu
Mingyuan Ge
Kris Reyes


Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1615289117?pwd=Hqkbj9itxWrFnkhZ8rQXHPInO2gxdF.1

Meeting ID: 161 528 9117
Passcode: 991382

What can you learn from over seven years' worth of Twitter bios? Steven Skiena, Distinguished Teaching Professor of Computer Science and Director of SBU's Institute for AI-Driven Discovery and Innovation, will tell us.

Presenting work done with collaborators Jason Jones, Dakota Handzlik, and Xingzhi Guo, Dr. Skiena will discuss what the team learned about how people portray themselves on social media through their political identities and job status. He'll also show us what you can predict about a person based on their self-description.

If you have a disability and are requesting accommodations in order to fully participate in this event, please email libraryevents@stonybrook.edu or call 631-632-7100.

Register now: https://library.stonybrook.edu/library-events/stem-speaker-series-measuring-self-identity/

Hieu Le presents Incorporating Physical Illumination Constraints into Deep Learning Shadow Detection and Removal (PhD Proposal)

Shadows provide useful cues to analyze the scene but also hamper many computer vision algorithms such as image segmentation, object detection or tracking. For those reasons, shadow detection and shadow removal have been well studied topics in computer vision. Early approaches for shadow detection and removal focus on physical illumination models of shadows. These methods can express, identify, and remove shadows in a physically plausible manner. However, these models are often hard to optimize and slow in inference due to reliance on hand-designed image features. On the other hand, recent deep-learning approaches have achieved breakthroughs in performances for both shadow detection and removal. They learn to extract useful features automatically through training while being extremely efficient in computation. However, these models are data-dependent, opaque and ignore the physical aspects of shadows.

We propose to incorporate physical illumination constraints into deep-learning frameworks. Thus the mapping learned by the deep-network closely follows the physics of shadows, enabling the network to systematically and realistically modify shadows in images. For shadow detection, we present a novel GAN framework in which the generator can generate realistic images with attenuated shadows that can be used to train a shadow detector. For shadow removal, we propose a method that uses deep-networks to estimate the unknown parameters for a shadow image formation model that removes shadows. The system outputs shadow-free images in high-quality with no image artifacts and achieves state-of-the-art shadow removal performance. Lastly, we propose a system trained without the need for any shadow-free images in which physical constraints play pivotal roles that enable training the networks.

For Zoom information, please email events@cs.stonybrook.edu.