Learn how to streamline your interaction with Gemini and connect it to the entire Google Workspace system.

Learn straightforward tips we can all use within Gemini that interface with the other Google apps such as Gmail, Calendar and Drive.

Register to attend.
AI + Music Seminar - The meeting will consist of introductions and organizational discussions, aimed at understanding participants' interests. We'll discuss what the seminars can focus on going forward.

Abstract: As intelligent systems become more integrated into human environments, fostering trustworthy human-AI collaboration presents a pressing challenge. In this talk, I examine the interplay between an agent's performance and social dynamics in shaping trust in human-AI interactions. My approach combines testbed development, behavioral prototyping, and user study design to create controlled experimental setups that capture real-world interaction complexities, such as ambiguity, multi-agent dynamics, and conflicting goals.

I illustrate this with a recent VR study on multi-user interaction with an autonomous vehicle (AV). Moving beyond dyadic interactions, the study probes human perspectives from the roles of a pedestrian, driver, and AV passenger, all interacting with the AV simultaneously at an ambiguous all-way stop sign intersection. We compare interactions with efficient and prosocial AV behavior strategies, revealing diverging trust perceptions and preferences across user roles. These insights inform a broader research trajectory focused on balancing performance with social considerations in designing trustworthy human-AI collaborations.

Bio: JiHyun Jeong is a postdoctoral researcher at Cornell University working on human-computer interaction and human-robot interaction. Her research develops prototypes and methods to explore performance and social factors that influence collaboration and trust between humans and artificial agents. She holds a Ph.D. and MPS in Information Science from Cornell University, and a BSc in Computer Science and Engineering from Korea University. She is a recipient of an honorable mention for best paper at DIS.

Zoom: https://stonybrook.zoom.us/j/98738234619?pwd=djJFQXBWbkpmblZDT25zNlVMYWpCQT09

Meeting ID: 987 3823 4619
Passcode: 474618

University Libraries Presents: The Library AI Club is a welcoming space for students, faculty, and staff to explore AI in a supportive, low-pressure environment. Meeting every two weeks, the club features discussions, collaborative projects, guest speakers, and hands-on experiments. Join us to learn, share ideas, and engage with AI responsibly and creatively. We'd love to see you at an upcoming meeting! Location: Melville Library, Scholarly Communication Seminar Room
Abstract: Drawing on group-theoretic and information-theoretic foundations, we propose information lattice learning (ILL) as a general framework to learn rules of a signal (e.g., an image or a probability distribution). In our definition, a rule is a coarsened signal used to help us gain one interpretable insight about the original signal. To make full sense of what might govern the signal's intrinsic structure, we seek multiple disentangled rules arranged in a hierarchy, called a lattice. Compared to representation/rule-learning models optimized for a specific task (e.g., classification), ILL focuses on explainability: it is designed to mimic human experiential learning and discover rules akin to those humans can distill and comprehend. We will detail the mathematical foundations and algorithms of ILL, and illustrate how it addresses the fundamental question what makes X an X by creating rule-based explanations designed to help humans understand. Our focus is on explaining X rather than (re)generating it. We show ILL's efficacy and interpretability on benchmarks and assessments, as well as a demonstration of ILL-enhanced classifiers achieving human-level digit recognition using only one or a few MNIST training examples (1-10 per class). We present applications in knowledge discovery, using ILL to distill music theory from scores and chemical laws from molecules and further revealing connections between them. We close with some early work on understanding the principles that govern scattering amplitudes in Super Yang-Mills theory, rather than just predicting them.

Biography: Lav R. Varshney is the Della Pietra Infinity Professor and inaugural director of the AI Innovation Institute at Stony Brook University. He is co-founder and CEO of Kocree, Inc., a startup company building novel human-controllable AI for discovery and creativity, and chief scientist of Ensaras, Inc., a startup company focused on AI and wastewater treatment. He holds appointments at RAND Corporation and at Brookhaven National Laboratory. He was previously on the faculty of the University of Illinois Urbana-Champaign, a visiting scholar at Northwestern's Kellogg School of Management, a principal research scientist at Salesforce Research AI, and a research staff member at IBM Research. He is a former White House staffer, having served on the National Security Council staff as a White House Fellow, where he contributed to national AI and wireless communications policy. His research interests include information theory and artificial intelligence. He received his B.S. degree from Cornell University and his S.M. and Ph.D. degrees from the Massachusetts Institute of Technology.

Location: Room 102
Tired of drowning in grading? Discover how Gradescope can reduce grading time by 30%-50% for exams, homework, and online assignments. In this 90-minute session, learn to grade efficiently with AI-assisted tools, update rubrics easily, manage paperless submissions, and use analytics to identify learning gaps.

This session will go over:

  • Grade items simultaneously: Use AI-assisted grading to group identical student answers and grade them all at once.

  • Edit rubrics on the fly: Adjust your rubric points at any time, and let Gradescope automatically update previously graded papers retroactively.

  • Go paperless: Effortlessly manage student-submitted PDFs or instructor-scanned exams without the clutter.

  • Spot learning gaps: Use instant analytics dashboards to see exactly which concepts your students mastered and where they struggled.


Register here by 09/13 to attend the Zoom training session.
Join the Center of Excellence in Wireless and Information Technology (CEWIT) and their co-host IEEE-USA for a livestream panel discussion on Generative Artificial Intelligence (Gen AI). In this engaging livestream, we will dive into the technologies that continue to transform what is possible and explore the dynamic intersection of innovation, creativity, ethics, and Gen AI.

CEWIT is joined by Stony Brook University experts who will provide their insights and perspectives on this rapidly changing technology.

Meet the Panel

Laura Lindenfeld, PhD
Executive Director
Alan Alda Center for Communicating Science®
Dean
School of Communication & Journalism
BIO

Margaret Schedel, PhD
Associate Professor
Composition and Computer Music
Co-Founder
Lyrai
BIO

Steven Skiena, PhD
Interim Director
AI Innovation Institute
Distinguished Professor
Computer Science
BIO

Vivian Zhang
CTO/School Director
NYC Data Science Academy
Chief Data Officer
GoDental.ai
BIO


Register here.

Abstract:

It is known that models like large language models (LLMs) can often suggest colloquial plans given verbal descriptions of tasks, yet they are unable to reliably provide executable and verifiable plans given formally specified environments. In this talk, I will discuss a strand of efforts to have LLMs generate accurate and explainable plans in textual simulations. Instead of directly generating the plan or actions, LLMs are prompted to generate Planning Domain Definition Language (PDDL) that specifies the environment (domain file) and the task (problem file), which can then be deterministically solved with an off-the-shelf planner. In a 3-phase study, my collaborators and I first observed that it is possible but very challenging for LLMs to generate long-form code such as PDDL domain and problem files given textual specifications. Next, we devise methodologies for LLMs to iteratively generate and refine problem files while exploring a partially-observed, simulated, textual environment. Finally, we show that domain files are even more difficult to generate correctly, even on well-established planning tasks such as BlocksWorld. Finally, I will discuss ongoing efforts to improve said ability of structured generation and promising frontiers to explore.

Bio:
Li Harry Zhang is an assistant professor at Drexel University, focusing on Natural Language Processing (NLP) and artificial intelligence (AI). He obtained his PhD degree from the University of Pennsylvania advised by Prof. Chris Callison-Burch. Prior, he obtained his Bachelor's degree at the University of Michigan mentored by Prof. Rada Mihalcea and Prof. Dragomir Radev. His current research uses large language models (LLMs) to reason and plan via symbolic and structured representations. He has published more than 20 peer-reviewed papers in NLP and AI conferences, such as ACL, EMNLP, and AACL, that have been cited more than 1,000 times. He also consistently serves as Area Chair, Session Chair, and reviewer in those venues. Being a musician, producer, and content creator having over 50,000 subscribers, he is also passionate in the research of AI music and creativity.