The 39th Annual AAAI Conference on Artificial Intelligence will be held from February 25 to March 4, 2025 in Philadelphia, Pennsylvania, USA. More information can be found here.

Join us to share your thoughts about teaching, learning, and AI!

The landscape of higher education is rapidly evolving with the integration of Artificial Intelligence (AI). Through the Institute on AI, Pedagogy, and the Curriculum with AAC&U, we are exploring ways that we can better address AI in teaching and learning. We want to hear your experiences, your concerns, and your ideas.

This is an open discussion for all faculty and staff to share their perspectives on the opportunities and challenges AI presents in our academic environment.

We'll be exploring critical questions like:

  • In the age of AI, what are the opportunities you see for enriching the classroom and curriculum? How can it enhance student learning or your professional practice?

  • What are the most significant challenges and concerns that AI raises for you regarding academics, student integrity, or your workload?

  • What resources (tools, training, technical support, policy guidance, etc.) do you need to feel confident and successful in the age of AI?

Dates/Times:

  • Tuesday, 2/3 at 2pm

  • Friday, 2/6 at 9:30am

Please register in advance for the Zoom link.

Can't Make It? Share Your Feedback!

We understand schedules are tight. If you cannot attend the live discussion, you can still share your thoughts! Join our AI Zoom Room to share your thoughts via video recording or email rose.tirotta-esposito@stonybrook.edu with your comments and ideas.

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

Your input is vital. From pedagogy to assessment, your insights will be critical. We look forward to a thoughtful and productive conversation!

  • 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)

Synthetic Dreams and Ghost Machines

Dr. Steven Skiena will join the Cinema for a presentation exploring the rapidly evolving worlds of artificial intelligence and robotics.

Dr. Skiena is Distinguished Teaching Professor of Computer Science and Associate Director of the AI Innovation Institute at Stony Brook University. A leading researcher in data science and algorithms, he is the author of several influential books on AI and computation, including The Algorithm Design Manual and The Data Science Design Manual.

The presentation will be followed by a screening of Ex Machina, Alex Garland's provocative science-fiction thriller exploring the seductive and unsettling boundaries between human consciousness and artificial intelligence.

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.
AI3, SBU Libraries and IACS present
at International Love Data Week
sponsored by The Office of the Provost and
Educational and Institutional Effectiveness (EIE)

Special Talk and Panel Discussion

How I Learned to Stop Worrying and Love AI (For Now)

with Paul Fain from The Job and Work Shift

A reporter's take on what we know--and what we don't know--about AI's emerging impacts on the labor market. The discussion will include the latest research from economists and the AI labs themselves about how workers are using AI, and current thinking among experts on how the tech's rapid deployment will play out across job roles, industries, and regions.

Panel discussion to follow with:

  • Lav Varshney, Della Pietra Infinity Professor and inaugural director of the AI Innovation Institute
  • Nicholas Johnson, Director of AI, SBU Libraries
  • Marianna Savoca, Associate Vice President for Career Readiness and Experiential Education
Paul Fain is co-founder of Work Shift, editor of the must-read newsletter, The Job, and host of The Cusp podcast. A veteran higher education reporter, Paul is perhaps the nation's top journalist focused on connections between education and work. He started Work Shift after a decade as a senior reporter and then news editor at Inside Higher Ed, where he led the outlet's coverage of low-income and first-generation students, college completion, community colleges, federal policy, and emerging models of higher education. He also was the founding host of the successful podcast, The Key with Inside Higher Ed, and has contributed chapters for books on innovation in higher education, published by the Harvard University Press and the Stanford University Press. Earlier in his career, Paul was a senior reporter at The Chronicle of Higher Education.

Limited Seats!

Registration is required.

Ready for Round Two? Dr. Zach Justus Returns! Join us on October 30, 2025, in the SBU Hilton Garden Inn. Buckle up your curiosity for a high-energy morning session with the engaging Dr. Zach Justus as we navigate how GenAI is reshaping not just how we teach, but what we teach. With real talk and questions that hit hard like Are students learning what we think we're teaching? This is your chance to rethink your program's true destination. Whether you're looking to pick up a few takeaways or chart a new direction entirely, this symposium is your space to explore, reflect, and act.

Check-in and breakfast will begin at 8:30 a.m. in order to begin our program promptly at 9:00 a.m.

Registration will remain open until October 15 or until the event reaches capacity. If closed, please contact educationaleffectiveness@stonybrook.edu to request a spot on the waitlist.

Abstract: Recent advances in Spatial Transcriptomics (ST) pair histology images with spatially resolved gene expression profiles, enabling predictions of gene expression across different tissue locations based on image patches. This opens up new possibilities for enhancing whole slide image (WSI) prediction tasks with localized gene expression. However, existing methods do not fully leverage the interactions between different tissue locations, which are crucial for accurate joint prediction. To address this, we introduce MERGE (Multi-faceted hiErarchical gRaph for Gene Expressions), which combines a multi-faceted hierarchical graph construction strategy with graph neural networks (GNN) to improve gene expression predictions from WSIs. By clustering tissue image patches based on both spatial and morphological features, and incorporating intra- and inter-cluster edges, our approach fosters interactions between distant tissue locations during GNN learning. As an additional contribution, we evaluate different data smoothing techniques that are necessary to mitigate artifacts in ST data, often caused by technical imperfections. We advocate for adopting gene-aware smoothing methods that are more biologically justified. Experimental results on gene expression prediction show that our GNN method outperforms state-of-the-art techniques on multiple metrics such as mean squared error (MSE), mean absolute error (MAE), and pearson correlation coefficient (PCC). Qualitative analysis establishes the effectiveness of MERGE in capturing cancer marker genes, thus consolidating its utility in diagnostics. As an extension of this work, we use MERGE in a setting with an uncertainty calibration branch to perform robust gene expression smoothing. We show that using patch-wise uncertainty from an uncertainty calibration model and the gene expression predictions from MERGE to enrich the ground truth gene expression matrix, results in better alignment with pathologist annotations, thus establishing that the smoothing is biologically informed.

Speaker: Aniruddha Ganguly

Location: Virtual Zoom Meeting


https://stonybrook.zoom.us/j/5474847973?pwd=Sng0Q2h1c1d3cm9sbFBmYUczMHZNdz09
Meeting ID: 547 484 7973
Passcode: 206739
We live in a new scientific paradigm: the Big Data era, in which a lot of data is available for almost anything. In this new paradigm, the driving force is to use data directly to learn about chemical and physics systems employing artificial intelligence. This paradigm has proven helpful in simulating realistic physical, biological, and chemical models, yielding impressive results. Similarly, the insight gained in these situations can be used to improve our understanding of fundamental processes. In that regard, we want to answer the question: Can a machine learn chemistry? The answer to this question is still debatable, but we will show our ideas and methods to find the answer. We will also discuss our results on predicting atom-diatom reactions and other avenues and work in progress in our group.

Please register for the STEM Speaker Series: Can a Machine learn Chemistry here.

Abstract: How do humans learn the sound patterns of their language? Despite a variety of methods and advances in phonotactic learning, there is still a paucity of computational research, methods and data for languages with tones. In this talk, I will explore this question specifically in light of tone languages, where pitch plays a crucial role in distinguishing words' meaning. I provide an implementation of the Bottom-Up Factor Inference Algorithm over Autosegmental Representations (BUFIA-AR), which learns the rules governing possible tone patterns. Using a dataset of Hausa, a West African tone language, the algorithm successfully identifies patterns that are not permitted in the language. These results (i) confirm long-standing linguistic generalizations, (ii) make more specific predictions about exceptional cases, and (iii) reveal previously unnoticed patterns. The results show how mathematical models of sound structure can be brought into dialogue with both linguistic theory and computational learning, highlighting the broader potential of formal approaches to capture human linguistic knowledge.

Bio: Han Li is a fifth-year Ph.D. student in Linguistics department, specializing in computational linguistics under the supervision of Professor Jeff Heinz. Her research focuses on how sound patterns in language can be formally represented and computationally learned, bridging theoretical linguistics and computer science.

Location: Institute for Advanced Computational Science, Seminar Room

Zoom Meeting: https://stonybrook.zoom.us/j/94043459206?pwd=3ra47h8HghOFRfobRBjZaDMyTwialr.1
Meeting ID: 940 4345 9206
Passcode: 332717

Stony Brook University Northern California Alumni Chapter - Institute for AI-Driven Discovery and Innovation Panel

Join us for a Northern California Alumni and Friends luncheon followed by a panel discussion, celebrating the Institute for AI-Driven Discovery and Innovation, moderated by Fotis Sotiropoulos, Dean, College of Engineering and Applied Sciences.

Panel Discussion with:
Richard Bravman '78, Chief Strategy Officer, Affinity Solutions
Jalal Mahmud, PhD '08, Master Inventor, IBM Watson
Reza Raji '86, CEO, Xenio Systems
Andrew Protter, PhD '83, Chief Scientific Officer, Auansa Inc.

Moderated by:
Fotis Sotiropoulos, Dean, College of Engineering and Applied Sciences

Click here for more information and to register.