The New York Academy of Sciences Presents AI for Materials: From Discovery to Production - A Virtual Symposium

Event Description: This interdisciplinary symposium covers the application of artificial intelligence (AI) throughout the entire life cycle of new materials -- from materials simulations and synthesis to translating research into high-volume industrial production.

Event Link & Registration: nyas.org/AI4Materials2020

Topic: AI Seminar: Stanley Bak
Time: Monday Nov 1, 2021 12:00 PM Eastern Time (US and Canada)

Join Zoom Meeting
https://stonybrook.zoom.us/j/91227496273?pwd=M3EyUDlzK3Vzd2pDOGpDU1ZjN0k1UT09

Abstract: The field of formal verification has traditionally looked at proving properties about finite state machines or software programs. The surge in deep learning has been accompanied by a surge of progress in trying to apply mathematical and algorithmic techniques to prove things about the function being computed by a neural network.

This talk formalizes the neural network verification problem and describes technical methods for neural network verification based on reachability analysis. Improvements to analysis efficiency will be given, as well as research directions for further exploration. We also include an objective comparison performed this last summer trying to evaluate the best existing verification methods in terms of speed and network size. The competition was performed on common hardware and involved the participation of twelve international teams (the tool authors) on a common set of benchmarks. 

Biography: Stanley Bak is an assistant professor in the Department of Computer Science at Stony Brook University investigating the verification of autonomy, cyber-physical systems, and neural networks. He strives to develop practical formal methods that are both scalable and useful, which demands developing new theory, programming efficient tools and building experimental systems.
Stanley Bak received a Bachelor's degree in Computer Science from Rensselaer Polytechnic Institute (RPI) in 2007 (summa cum laude), and a Master's degree in Computer Science from the University of Illinois at Urbana-Champaign (UIUC) in 2009. He completed his PhD from the Department of Computer Science at UIUC in 2013. He received the Founders Award of Excellence for his undergraduate research at RPI in 2004, the Debra and Ira Cohen Graduate Fellowship from UIUC twice, in 2008 and 2009, and was awarded the Science, Mathematics and Research for Transformation (SMART) Scholarship from 2009 to 2013. From 2013 to 2018, Stanley was a Research Computer Scientist at the US Air Force Research Lab (AFRL), both in the Information Directorate in Rome, NY, and in the Aerospace Systems Directorate in Dayton, OH. He helped run Safe Sky Analytics, a research consulting company investigating verification and autonomous systems, and performed teaching at Georgetown University before joining Stony Brook University as an assistant professor in Fall 2020.
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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.
Join us for the New York State Innovation Summit on October 28-29, 2024 in Syracuse, NY. This multi-day is event for NYS organizations that want to showcase and discover new and emerging technologies that support innovation and drive business growth. The event serves as an opportunity to foster collaboration; introduce industry to experts that can assist growth, strengthen our statewide innovation ecosystem and showcase promising early stage companies. Whether you're a startup, an economic developer, or an established manufacturer, the NYS Innovation Summit is for you. The 2024 New York State Innovation Summit will showcase companies and researchers at the forefront of emerging technologies and new advancements in production capabilities. This event celebrates New York State leadership in technology-led economic growth with experts in biotechnology, new materials, energy innovation, and artificial intelligence that will explore current technology convergence opportunities, ways to accelerate commercialization, and issues of manufacturing sustainability.

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.

Climate Uncertainty, Decision Making, and AI for Earth System Predictability Dr. Nathan Urban, Brookhaven National Laboratory

Bio: Nathan Urban is the group leader of the Optimal Experimental Design & Uncertainty Quantification group in the Applied Mathematics Department at Brookhaven National Laboratory's Computing & Data Sciences directorate (CDS). He holds a Ph.D. in theoretical condensed matter physics from Penn State, and has previously held research positions at Los Alamos National Laboratory, Princeton, and Penn State. His research interests include Bayesian inference and spatiotemporal statistics, probabilistic prediction and forecasting, multi-model / model-form / model structural uncertainty quantification, reduced order modeling, scientific machine learning and hybrid physical-data driven modeling, in-situ/streaming data analysis at scale, information fusion, decision making under uncertainty and optimal experimental design, and integrated multiscale computational frameworks for decision support.

Location: IACS Seminar Room

Lunch will be provided

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/