Stony Brook University Libraries invites students, faculty, & staff to join a conversation about how AI is transforming the private sector workforce. As AI tools move from experimentation to everyday business use, companies are rethinking roles, skill sets, leadership, and long-term strategy. This discussion-based event will focus on the fast-paced changes and directions at tech companies and their possible impact. This event will be particularly relevant for students preparing for an AI influenced job market and how to position themselves for opportunities in a rapidly evolving professional landscape.

The discussion will be led by Tariq Khan, Senior Director of Private Cloud Solutions at Hewlett Packard Enterprise. Tariq is a technology leader and architect with experience across private cloud, hybrid cloud, and data center platforms. He is responsible for shaping the technology architecture and strategic direction of HPE's Private Cloud offerings across on premises and cloud integrated environments.

Light refreshments will be served.


Location: Melville Library, NRR, Learning Lab
Are you interested in understanding the challenges that lie ahead as Artificial Intelligence (AI) systems become increasingly autonomous, dynamically acquire information, and adapt behaviors?
 
Join us for an exciting afternoon of talks by visionaries and leaders from industry, government, and academia as we kickoff a three-part Trusted AI Challenge Series designed to Build the Vision - Formalize Challenges - Advance the Art of next generation of AI systems.
 
The Air Force Research Laboratory Information Directorate, The State University of New York, Innovare Advancement Center, NYSTEC, and Griffiss Institute invite you to join us for this half-day virtual event!
 
WHEN: Wednesday, October 14, 2020, 12:00 PM - 4:00 PM EDT
 
Hosted by Innovare Advancement Center, this webinar is the first of a three-part series designed to cultivate, define and fund creative solutions to a set of challenge problems in trustworthy AI with a particular focus on dynamic, autonomous systems that learn and adapt behaviors.
 
Keynote speakers include Dr. David Goldstein of  Space X; Dr. Scott Hubbard of Stanford University; Dr. Pramod Khargonekar of UC Irvine, and more!
 
This event is designed for academic and government researchers, university students, and small businesses.
 
Would you like to understand some of the most formidable technical challenges in future autonomous systems?  Would you like to sponsor some of the brightest minds in AI to work on problems of interest to you? Would you like to learn more about AI in real systems?
 
If so, Save the Date! Wednesday, October 14, 2020, 12:00 PM - 4:00 PM EDT.
 
Please see additional information on the three-part series here. Registration details to follow! 
 
Stay tuned: https://www.innovare.org/news-events  

Description:

Curious about what AI image generation tools are out there and how they work? Come down to the library Galleria space (outside the Central Reading Room) to see some demonstrations and learn more about them.

Librarians Chris Kretz and Ahmad Pratama, along with David Ecker of DoIT, will be hosting Explore AI demos from Monday - Wednesday this week on different topics. Whether you're new to AI or an experienced user, stop by and take a look!

Location: Library Galleria

Abstract: Reward hacking, where a reasoning model exploits loopholes in a reward function to achieve high rewards without solving the intended task, poses a significant threat. This behavior may be explicit, i.e. verbalized in the model's chain-of-thought (CoT), or implicit, where the CoT appears benign thus bypasses CoT monitors. To detect implicit reward hacking, we propose TRACE (Truncated Reasoning AUC Evaluation). Our key observation is that hacking occurs when exploiting the loophole is easier than solving the actual task. This means that the model is using less effort than required to achieve high reward. TRACE quantifies effort by measuring how early a model's reasoning becomes sufficient to obtain the reward. We progressively truncate a model's CoT at various lengths, force the model to answer, and estimate the expected reward at each cut-off. A hacking model, which takes a shortcut, will achieve a high expected reward with only a small fraction of its CoT, yielding a large area under the reward-vs-length curve. TRACE achieves over 65% gains over our strongest 72B CoT monitor in math reasoning, and over 30% gains over a 32B monitor in coding. We further show that TRACE can discover unknown loopholes during training. Overall, TRACE offers a scalable unsupervised approach for oversight where current monitoring methods prove ineffective.

Speaker: Dikshya

Location: Old Computer Science Building - CS2311
Abstract: The recent expansion of online sport wagering and igaming has led to higher rates of problem gambling, particularly among emerging adults and other population subgroups. The Center for Gambling Studies (CGS) at the Rutgers University, School of Social Work, is using big data analysis, machine learning and GIS mapping to identify geographic locations with populations most at risk to guide the development of targeted interventions. This presentation will review the GIS StoryMap for the State of New Jersey, including a blueprint for the highest risk target service areas in the state. It will also present findings from a machine learning model that identifies the key risk factors for high-intensity online casino bettors. Implications for prevention, treatment and policy initiatives will be discussed.

Bio: Lia Nower, J.D., Ph.D., is a Distinguished Professor, Associate Dean for Research, and Director of the Center for Gambling Studies at Rutgers University. A clinician and attorney, her research focuses on big data analysis and machine learning models for online gambling and sports wagering; gambling and video gaming among emerging adults; policy initiatives around harm reduction and responsible gambling, and etiology and treatment of problem gambling. Dr. Nower serves as a senior editor for Addiction. She has received both the Research (2019) and the Lifetime Research Award (2022) from the National Council on Problem Gambling and the Board of Trustees Award for Research (2022) from Rutgers University.

Join Zoom Meeting: https://stonybrook.zoom.us/j/95617197636?pwd=KytzZ2pVRG9SZGpKZUtpNXJISjNjZz09
Meeting ID: 956 1719 7636 Passcode: 924293
The Future of Learning: Rethinking Practice in a Changing World

Thursday, March 26, 2026 (Workshops)
Friday, March 27, 2026 (Symposium)

Open to Stony Brook University Faculty, Staff, and Graduate Students. Hosted by the Center for Excellence in Learning and Teaching, Office of the Provost.

Thursday, March 26, 2026
Workshop: AI Tools and Techniques
  • Open to all faculty & staff
  • Hands-on, exploratory
  • Registration only limited to the size of the room
  • Location: In-person, TBD
  • Time: 10 AM - 12 PM
  • Registration required

Friday, March 27, 2026
Keynote: Teaching and Thinking with AI
  • Faculty, TAs, postdocs, and academic staff
  • In-person on-campus conference venue
  • Location: SAC Balroom
  • Time: 9 AM - 3 PM
  • Registration required

Keynote Speaker: José Antonio Bowen

José Antonio Bowen has been leading innovation and change for over 40 years at Stanford, Georgetown and the University of Southampton (UK), as a dean at Miami University and SMU and as President of Goucher College. Bowen has worked as a musician with Stan Getz, Dave Brubeck, and many others and his symphony was nominated for the Pulitzer Prize in Music (1985).
Bowen holds four degrees from Stanford and has written over 100 scholarly articles and books, including the Cambridge Companion to Conducting (2003), Teaching Naked (2012 and the winner of the Ness Award for Best Book on Higher Education), Teaching Naked Techniques with C. Edward Watson (2017) and Teaching Change: How to Develop Independent Thinkers using Relationships, Resilience and Reflection (Johns Hopkins University Press, 2021).
Bowen has appeared in The New York Times, Forbes, The Wall Street Journal, and has three TED talks. Stanford honored him as a Distinguished Alumni Scholar (2010) and he has presented keynotes and workshops at more than 300 campuses and conferences 46 states and 17 countries around the world. In 2018, he was awarded the Ernest L. Boyer Award (for significant contributions to American higher education). He is a senior fellow for the American Association of Colleges and Universities.

Register here.
Mind Brain Lecture: Constructing the World of Taste in Your Head You fork the morsel into your mouth and say yum...chocolate cake. The appreciation of your dessert's taste seems to follow directly, quickly and simply from the placement of the food on your tongue. The truth, however, is far more interesting and complex: your brain actually begins determining whether you will enjoy a bite of food even before the fork approaches your mouth and continues to work the problem well after. Information about your food's color, smell, texture and taste activates multiple parts of your brain, where that information collides with your pre-mouthful beliefs about how it should taste. The coming-together and shuffling of that information around the brain takes time, as networks of neurons work together to help you decide whether the morsel in your mouth is worth swallowing. Referring to work from psychology, biology and computational neuroscience, Professor Katz will de-mystify and reveal the beauty of these complexities of the neuroscience of taste. Donald Katz, Professor of Psychology, Departments of Neuroscience, Psychology, and the Volen National Center for Complex Systems, Brandeis University Free presentation intended for a general audience. Reception to follow. https://www.stonybrook.edu/commcms/mind/

AI on Campus: Your Thoughts, Your Future

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.)

Dates/Times:

  • Wednesday, 2/4 at 2pm

  • Thursday, 2/5 at 12pm

Please register in advance for the Zoom link.

Can't Make It? Share Your Feedback!

Don't worry if you can't attend! You can still share your thoughts via video in our AI Zoom Room or via email: rose.tirotta-esposito@stonybrook.edu.

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:
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: Large Language Models (LLMs) have revolutionized how people interact with knowledge, offering unprecedented opportunities to accelerate the pace of scientific discovery. In this talk, I will discuss my research on the synergy between LLMs and scientific knowledge--specifically how these models extract, induce, and verify knowledge to automate the research lifecycle. First, I will cover our work on improving knowledge extraction from vast scientific literature, focusing on enabling models to comprehend long documents in a cost-efficient and comprehensive manner. I will describe a novel paradigm for representing document-level structured information as question-answer pairs and how we address the challenges of long-context understanding by leveraging global context through retrieval-augmented modeling. Next, I present our pioneering work on using LLMs for new scientific hypothesis generation. We introduce a framework employing reinforcement learning with fine-grained reward modeling and adaptive controllers.
This approach balances novelty, feasibility, and effectiveness to generate inspiring and actionable research hypotheses. Finally, I will discuss work on the first LLM Scientist for machine learning research. I will demonstrate how LLMs can move beyond hypothesis generation to participate in the execution and validation of scientific hypotheses, ensuring that the discovered knowledge is not only innovative but also grounded and verified.

Bio: Xinya Du is a tenure-track assistant professor at UT Dallas Computer Science Department. He earned a Ph.D. degree from Cornell University and was a Postdoctoral Research Associate at the University of Illinois (UIUC). He has also worked at Microsoft Research, Google Research, and Allen Institute AI. His research is on large language models, deep learning, and their applications in science.His work has been published in leading NLP and ML conferences (ACL, ICLR, NeurIPS). His research has received multiple recognitions, including a Best Paper Award at AAAI AI for Research and a Best Poster Award at ICML AI for Science workshop. His work was included in the list of Most Influential ACL Papers and has been covered by major media like New Scientist. He was named a Spotlight Rising Star in Data Science by the University of Chicago and is the recipient of several prestigious awards, including the Amazon Research Award, Cisco Research Award, Open Philanthropy Award, and the NSF CAREER Award.

Location: NCS 120