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/

Abstract: Capturing the spatio-temporal (4D) dynamics of humans has been a long standing research problem in computer vision and graphics. Synthesizing photorealistic human avatars has broad applications, ranging from immersive telepresence in AR/VR and the movie industry, to enriching the education and healthcare systems. Earlier approaches relied on hand-engineered models that use a small amount of data from one or more subjects. With the advent of neural networks, training on large datasets enhanced the output visual quality. Currently, the combination of neural networks with graphics techniques has achieved natural-looking human animation. However, most approaches are identity-specific, trained only on a single identity, and use only one modality.

In this dissertation, we address the problem of learning neural representations of humans in a holistic way. Given that the video data in the real world include multiple modalities (e.g., audio and video) and multiple identities, we develop multi-modal and multi-identity representations. First, we propose to reconstruct the 4D face geometry of humans by leveraging both audio and video information. In this way, the network produces accurate lip shapes and is robust to cases when either modality is insufficient. Next, we introduce a NeRF-based representation for audio-driven human face animation that achieves high-quality lip synchronization for cinematic content. Since humans communicate with their full body, combining body pose, hand gestures, and facial expressions, we extend the network to capture full-body human motion for multiple identities simultaneously. In order to better disentangle identity and non-identity specific information, we subsequently study non-linear interactions between latent factors of variation, and propose a specific multiplicative module. In this way, we learn a multi-identity NeRF that robustly animates human faces under novel expressions and achieves a significant decrease in the total training time. Similarly, we propose a multi-identity Gaussian splatting representation for human bodies, by constructing a high-order tensor. Assuming a low-rank structure, we learn a tensor decomposition that leads to a significant decrease in the total number of learnable parameters, as well as to a robust animation under novel poses. Last but not least, we propose to jointly synthesize audio and visual outputs from just text input. Given the recent rise of large language models, coupling text with natural-looking avatars can enhance the overall interaction between a human and an AI system.

Location: NCS 220 or Zoom

Since 2021, IIAC has gathered the heads of nations, fabs, and faculties who shape how intelligence is built into the physical world. After four editions at Stanford and a 2025 convening in Ha Noi, the fifth moves to Tokyo for a single, deliberate day on the question beneath every other question: who governs the intelligence that runs the factory, the grid, and the supply chain?

Keynote speakers: Christopher Nguyen (Aitomatic), Yann LeCun (AMI Labs), Akihisa Shiozaki (House of Representatives, Japan), Suresh Venkatarayalu (Honeywell), Jimmy Rhee (ASI Foundry), and Toshikazu Okuya (Commerce and Information Policy Bureau).

Register here to attend.
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 Ph.D. 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. Registered students must attend in person. Up to 3 absences will be excused. Everyone else is welcome to attend. The seminar will be taught by Prof. Chao Chen, chao.chen.1@stonybrook.edu.
Join us at the Center for Excellence in Learning and Teaching (CELT) for an interactive Zoom workshop on Generative AI designed for faculty and staff interested in enhancing teaching and assessment practices, increasing student engagement, and navigating the rapidly evolving landscape of AI tools. Participants will be introduced to common AI tools, explore potential instructional uses, and discuss key considerations such as academic integrity, transparency, and equity.
Register now: https://stonybrook.zoom.us/meeting/register/6js1eP64T1ys8tyU57EJ7Q#/registration


Abstract:
Large language models (LLMs) have transformed the way humans write code, bringing unprecedented automation to software development. In this talk, I will first provide an overview of my research on enhancing LLMs' code intelligence, optimizing each step of the development pipeline towards more complex software engineering tasks. I will then delve into my key contributions, focusing on how to equip LLMs with a deeper, more comprehensive understanding of software programs. Finally, I will discuss the future of AI-driven software engineering, envisioning a new era of automation that is more reliable, intelligent, and cost-efficient.

Bio:
Yangruibo (Robin) Ding is a Ph.D. candidate in the Department of Computer Science at Columbia University. His research is at the intersection of Software Engineering and Machine Learning, focusing on developing large language models (LLMs) for code. He trains LLMs to generate, analyze, and refine software programs and constructs benchmarks to systematically evaluate LLMs in solving software engineering tasks. He also studies how to improve LLMs' reasoning capability to tackle complex programming tasks, such as debugging and patching. His interdisciplinary research has been published in top-tier conferences of software engineering, programming languages, natural language processing, and machine learning. He won an ACM SIGSOFT Distinguished Paper Award, an IEEE TSE Best Paper Runner-up, and received an IBM Ph.D. Fellowship.
Location:
NCS 120
Abstract: Machine learning (ML) systems fueled by neural networks have entered our daily lives and led to scientific breakthroughs, but many open questions remain. After a nod toward the question of rigor with ML and recent progress, I'll turn to the theory of neural networks. I will argue that understanding neural networks inevitably leads to ideas from field theory (FT), which was already realized in the simplest case in the 1990s, and I will review some essential FT-for-NN results. I will then propose that the connection might be more general, an NN-FT correspondence of sorts, with neural networks providing a way to define a field theory. I'll end with comments on known results including the origin of interactions and various symmetries, but I will also list some open questions. The apparent non-sequitur in the title will be used as a rhetorical device to explore where we are and where we'd like to go.

https://scgp.stonybrook.edu/calendar/full-calendar

The Program in Writing and Rhetoric
Invites you to
A Rhetorical/Deliberative Framework for AI Language Model Alignment
featuring
Prof Zoltan Majdik Professor
North Dakota State University
In this talk, Prof. Majdik proposes a framework for aligning LLMs with values grounded in the norms of rhetorical culture and deliberative democracy. Alongside long-standing AI alignment value targets like safety and transparency, this AI alignment framework assesses to what extent a language model exhibits human and humane values that foster communicative engagement, and it codifies approaches to tuning existing models to better align with such values.

Location: Humanities 1008
Join the Conversation: Share Your Thoughts about Learning, Academics, and AI

The world of college is changing fast, and Artificial Intelligence (AI) is at the center of it. We are part of the Institute on AI, Pedagogy, and the Curriculum with AAC&U, and we need to hear from the people AI affects most: you!

This is an open discussion for all students to share their honest experiences, their top concerns, and their best ideas about AI in our academic environment. We'll be diving into these key questions:
  • How can AI actually make learning better or easier? What opportunities do you see for using AI tools to enhance your assignments, research, or skills?
  • What are your biggest worries about AI? Is it about cheating, being graded fairly, or preparing for the job market? How is AI impacting your workload or stress levels?
  • What specific tools, workshops, or policies would help you use AI responsibly and successfully? (Think training, software, or clear rules.)
Date: Monday, December 1st
Time: 12:30pm-1:45pm
Location: West Campus - Location TBD
or
Date: Wednesday, December 3rd
Time: 10:30am-11:45am
Location: East Campus - HSC 2-154B

Please register in advance so we can confirm the room.

Note: Videos will not be shared publicly and comments will only be shared in aggregate.

Your voice matters. Come tell us how AI is affecting your studies, your stress, and your success!
  • Dr. Rose Tirotta-Esposito (Assistant Provost; Director of CELT)
  • Dr. Elizabeth Hewitt (Associate Professor in the Department of Technology and Society (DTS) in the College of Engineering and Applied Sciences)
  • Chris Kretz (Associate Librarian and Head of Academic Engagement at SBU Libraries)
  • Prof. Rajiv Lajmi (Assistant Professor in the School of Health Professions and Chair of Applied Health Informatics)
  • Dr. Matthew Salzano (Assistant Professor in the Department of Communication in the School of Communication and Journalism)
Abstract: In this talk, we take a step back and argue that many varied and seemingly unrelated attacks on the web are actually symptoms of one deeper problem that has existed since the web's inception. Whether it is attacks due to expired domain names, cloaking done by malicious websites, malvertising, or even our growing distrust of the news can be largely attributed back to the issue of stateless linking. Stateless linking refers to the absence of any integrity guarantees between the time that a link for a remote resource was created, to a future time when this link is resolved by web clients. We draw on 10+ years of research to demonstrate how stateless linking and the resulting lack of content integrity is the true culprit for many of our past, current, and likely future web problems. Successfully tackling this one really challenging problem, has the potential of solving many of our web woes.

Bio: Nick Nikiforakis is affiliated with the National Security Institute. He received his PhD in Computer Science from KU Leuven in Belgium. He received his MSc, in Parallel and Distributed Systems and BSc in Computer Science from the University of Crete, Greece. His research focuses on web security and privacy, software security, and intrusion detection.

Location: NCS 120