I will be holding an informal 2-week short optimization course, to try
to cover a few important proofs in the field. The goal will be depth
over breadth, with focus on:

 - convergence proofs for gradient descent and stochastic gradient descent
 - energy functions and continuous time optimization
 - estimate sequences and Nesterov acceleration

and, time permitting, additional topics like variance reduction,
quasi-Newton methods, and Frank-Wolfe methods. If we go super fast, we
can spend a few days at the end brainstorming interesting research
project ideas.

Details: NCS 220 6:15pm-7:45pm, Monday-Friday, Feb 7-Feb 18.

In person only, since I plan to use the whiteboard (but may be recorded)

More details will be uploaded here (notes, specific schedule):
https://sites.google.com/view/optimization-short-course/home
I will be holding an informal 2-week short optimization course, to try
to cover a few important proofs in the field. The goal will be depth
over breadth, with focus on:

 - convergence proofs for gradient descent and stochastic gradient descent
 - energy functions and continuous time optimization
 - estimate sequences and Nesterov acceleration

and, time permitting, additional topics like variance reduction,
quasi-Newton methods, and Frank-Wolfe methods. If we go super fast, we
can spend a few days at the end brainstorming interesting research
project ideas.

Details: NCS 220 6:15pm-7:45pm, Monday-Friday, Feb 7-Feb 18.

In person only, since I plan to use the whiteboard (but may be recorded)

More details will be uploaded here (notes, specific schedule):
https://sites.google.com/view/optimization-short-course/home
I will be holding an informal 2-week short optimization course, to try
to cover a few important proofs in the field. The goal will be depth
over breadth, with focus on:

 - convergence proofs for gradient descent and stochastic gradient descent
 - energy functions and continuous time optimization
 - estimate sequences and Nesterov acceleration

and, time permitting, additional topics like variance reduction,
quasi-Newton methods, and Frank-Wolfe methods. If we go super fast, we
can spend a few days at the end brainstorming interesting research
project ideas.

Details: NCS 220 6:15pm-7:45pm, Monday-Friday, Feb 7-Feb 18.

In person only, since I plan to use the whiteboard (but may be recorded)

More details will be uploaded here (notes, specific schedule):
https://sites.google.com/view/optimization-short-course/home
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.
Are you concerned about AI issues with your asynchronous online courses? Is your fully online course vulnerable to AI plagiarism? Do you want to engage your online students using AI? Discover the future of education with our AI-powered solutions designed specifically for online asynchronous courses. This innovative approach uses artificial intelligence to transform the way courses are delivered, making learning more personalized, engaging, and effective.

Register here: https://stonybrook.zoom.us/meeting/register/RD94cHiHRwCj6xNkCZqNEg
Abstract: AI has achieved remarkable advancements in image recognition and natural language processing. However, its applications in Earth and environmental sciences are still emerging. Unprecedented data from satellites, sensors, and in-situ measurements oIers new opportunities to improve physics-based models and forecasts of environmental systems with AI and to gain deeper insights into these phenomena. Extreme systems, such as weather and climate events, pose distinct challenges for AI, such as limited sampling of rare events, non-trivial data augmentation, errors-in-variables, and complexities of transfer learning across diverse tasks. In this talk, we will explore some of these challenges and showcase AI architectures designed to address them. We will use specific examples of forecasting dust storms, precipitation extremes, flash floods, and drought events in the Middle East. Finally, we will discuss a different AI approach for studying sinkhole formation in the Dead Sea.

Speaker: Prof. Yinon Rudich, Department of Earth and Planetary Sciences, Weizmann Institute, Israel


Join Zoom Meeting
ID: 98731258879
Passcode: cJjGQJqP

The Provost's Office is excited to invite you to join in responding to an extraordinary opportunity to enhance our academic and research capabilities in AI at Stony Brook. SUNY recently made funding available to support the creation of departments of AI and Society at its universities. Stony Brook is well-positioned to seize this opportunity to build upon our interdisciplinary strengths in AI.

The office is hosting a forum on Friday, Nov. 15, from 11:30 a.m. to 1:30 p.m., in Ballroom A, SAC. You are invited to attend to learn more about this opportunity and to help us generate ideas to build a compelling proposal for Stony Brook to submit to SUNY. Lunch will be provided.

Please click here to RSVP as soon as possible.

This funding will support innovation in our curriculum, allowing us to create programs that explore the social and societal impact of AI alongside the technological advancements led by researchers in engineering and scientific disciplines.

We believe we can make a significant impact through this SUNY program and look forward to your participation in this initiative.
The Future Histories Studio welcomes Moontae Lee, LG AI Research.


Generative AI is transforming how we understand, create, and interact with information. Large Language Models (LLMS) comprehend contexts, answer non-trivial questions, and spark creative ideas. This talk introduces the evolution of these models, highlighting the most recent advancements in planning, reasoning, and evaluation. The talk also touches on the criticalconsiderations for both model developers and users, carefully addressing limitations of LLMs as well as ethical and societal implications. Finally, the talk provides ongoing directions in researchand production: from the rise of personalized AI agents to the future frontiers of AI.

Moontae Lee is the Director of the Superintelligence Lab at LG AI Research and an Assistant Professor of Information and Decision Sciences at the University of Illinois Chicago. His journey with Large Language Models began as a visiting scholar at Microsoft Research in 2019, continuously consulting the Deep Learning Group at Redmond until joining LG. He holds a PhD in Computer Science from Cornell, an MS from Stanford, and BS degrees in Computer Science, Mathematics, and Psychology from Sogang University. He has been an area chair for major AI conferences and earned recognition in Operations Research and Computational Social Science, including awards from INFORMS and Amazon.

His research interests include:
● Computational Creativity, Algorithmic Awareness
● Retrieval-Augmented Generation and Evaluation
● Code Generation, Reasoning, Planning
● Fine-grained Alignment from Human/AI Feedback in Generative AI
● Large Time-series Models, Diffusion/Consistency
● Machine Unlearning
● Ranking Monopoly, Voting Fairness
● AI Safety, Ethics, and Market Impacts

Join us in person @ Future Histories Studio Staller Center for the Arts, 4222