The program will fund projects for up to a one-year period, depending on the availability of funds. AI^3 anticipates making at least six awards on this call. A one-year, no-cost extension can be requested in the final 6 months of a project with approval subject to progress towards project goals and active participation in research themes.
Competitive applications will actively incorporate modern AI technologies into the work; integrate students; document significant potential for future funding or other growth-oriented outcomes; and highlight innovations.
The 2024 application deadline will be October 15, at 11:59 PM EST. Recipients will be notified by December 20, and projects are anticipated to commence at the start of the Spring 2025 semester.
The AI Community at Stony Brook University is proud to announce Datathon 2026.
Dive into data analysis and AI/ML, and get ready to build something big. In this year's underwater-themed event, enjoy a weekend of data analysis, hacking, networking, fun activities, and minigames.
Whether you're a seasoned developer, data scientist, designer, or completely new to hacking, this event is your chance to collaborate, learn data science, and create something impactful with data and AI/ML.
What is Datathon?
AI Community's Datathon is the premier data science competition at Stony Brook University, bringing together students of all skill levels for a weekend of data exploration, analysis, and innovation. Just like a typical hackathon, you will be using your skills to build your dream project.
Unlike a regular hackathon, Datathon is focused on data science. You will be given a set of data to work with, analyze, and apply to your project. You can also find your own data to use. Your project will be presented to a panel of judges consisting of professors and industry professionals!
Who Can Participate
* Non-SBU Undergraduate Students are ineligible to receive prizes
* You must be 18+ or older (Excludes minors who are active SBU students)
Location: SAC Ballroom B
Register here.
Deep Surface MeshesPascal FuaEPFLGeometric Deep Learning has recently made striking progress with the advent of Deep Implicit Fields (SDFs). They allow for detailed modeling of watertight surfaces of arbitrary topology while not relying on a 3D Euclidean grid, resulting in a learnable 3D surface parameterization that is not limited in resolution. Unfortunately, they have not yet reached their full potential for applications that require an explicit surface representation in terms of vertices and facets because converting the SDF to such a 3D mesh representation requires a marching-cube algorithm, whose output cannot be easily differentiated with respect to the SDF parameters. In this talk, I will discuss our approach to overcoming this limitation and implementing convolutional neural nets that output complex 3D surface meshes while remaining fully-differentiable and end-to-end trainable. I will also present applications to single view reconstruction, physically-driven Shape optimization, and bio-medical image segmentation.
Pascal Fua received an engineering degree from Ecole Polytechnique, Paris, in 1984 and a Ph.D. in Computer Science from the University of Orsay in 1989. He joined EPFL (Swiss Federal Institute of Technology) in 1996 where he is a Professor in the School of Computer and Communication Science and head of the Computer Vision Lab. Before that, he worked at SRI International and at INRIA Sophia-Antipolis as a Computer Scientist. His research interests include shape modeling and motion recovery from images, analysis of microscopy images, and Augmented Reality. He has (co)authored over 300 publications in refereed journals and conferences. He has received several ERC grants. He is an IEEE Fellow and has been an Associate Editor of IEEE journal Transactions for Pattern Analysis and Machine Intelligence. He often serves as program committee member, area chair, and program chair of major vision conferences and has cofounded three spinoff companies.
