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

Speaker: Gary Kazantsev (Head of Quant Technology Strategy in the Office of the CTO at Bloomberg)

 

Date/Time: Friday, October 15, 2021 10:00AM-11:00AM EST

 

Title: Machine Learning in Finance

Abstract: Machine learning is changing our world at an accelerating pace. In this talk we will discuss the recent developments in how machine learning and artificial intelligence are changing finance, from a perspective of a technology company which is a key  participant in the financial markets. We will give an overview and discuss the evolution of selected flagship Bloomberg ML and AI projects, such as sentiment analysis, question answering, social media analysis, information extraction and prediction of market impact of news stories. We will discuss practical issues in delivering production machine learning solutions to problems of finance, highlighting issues such as interpretability, privacy and nonstationarity. We will also discuss current research directions in machine learning for finance. We will conclude with a Q&A session.

Bio: (https://www.techatbloomberg.com/people/gary-kazantsev/) Gary is the Head of Quant Technology Strategy in the Office of the CTO at Bloomberg. Prior to taking on this role, he created and headed the company's Machine Learning Engineering group, leading projects at the intersection of computational linguistics, machine learning and finance, such as sentiment analysis of financial news, market impact indicators, statistical text classification, social media analytics, question answering, and predictive modeling of financial markets.

Prior to joining Bloomberg in 2007, Gary had earned degrees in physics, mathematics, and computer science from Boston University.

He is engaged in advisory roles with FinTech and Machine Learning startups and has worked at a variety of technology and academic organizations over the last 20 years. In addition to speaking regularly at industry and academic events around the globe, he is a member of the KDD Data Science + Journalism workshop program committee and the advisory board for the AI & Data Science in Trading conference series. He is also a co-organizer of the annual Machine Learning in Finance conference at Columbia University.


Join Zoom Meetinghttps://stonybrook.zoom.us/j/93374426887?pwd=cE9zeW51VXFEN2R0YnNPbHF1WFp0Zz09Meeting ID: 933 7442 6887Passcode: 330347One tap mobile+16468769923,,93374426887# US (New York)+13126266799,,93374426887# US (Chicago)Dial by your location +1 646 876 9923 US (New York) +1 312 626 6799 US (Chicago) +1 301 715 8592 US (Washington DC) +1 346 248 7799 US (Houston) +1 408 638 0968 US (San Jose) +1 669 900 6833 US (San Jose) +1 253 215 8782 US (Tacoma)Meeting ID: 933 7442 6887

How to Succeed in Language Design Without Really Trying presented by Professor Brian Kernighan

ABSTRACT: Why do some languages succeed while others fall by the wayside? I've helped create nearly a dozen languages (mostly small) over the years; a handful are still in widespread use, while others have languished or simply disappeared. I've also been present at the creation of several other languages, including some really major ones. In this talk I'll give my humble, but correct, opinion on factors that affect success and failure, and try to offer some insight into what to do if you're trying to design a new language yourself, and why that might be a good thing.

BIO: Brian Kernighan received a PhD in electrical engineering from Princeton in 1969. He joined the Computer Science department at Princeton in 2000, after many years at Bell Labs. He is a co-creator of several programming languages, including AWK and AMPL, and of a number of tools for document preparation. He is the co-author of a dozen books and some technical papers, and holds 5 patents.
He is a member of the National Academy of Engineering and of the American Academy of Arts and Sciences. His research areas include programming languages, tools and interfaces that make computers easier to use, often for non-specialist users. He has also written two books on technology for
non-technical audiences: Understanding the Digital World in 2017 and Millions, Billions, Zillions: Defending Yourself in a World of Too Many Numbers, published in 2018. His most recent book, Unix: A History and a Memoir, was published in October 2019.
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.
Abstract: Many scientific and engineering challenges, such as the design of materials or molecules or the control of experimental systems, rely on the existence of fast predictive models that can evaluate potential designs or control policies. Traditionally this has been accomplished through numerical simulation; more recently data-driven machine learning methods have been applied. However, both approaches leave gaps: physical modeling can be accurate and extrapolates well to previously-unstudied conditions, but it is often computationally expensive and relies on physics approximations that may not be valid. Machine learning can generalize from massive amounts of real-world or simulation data, but suffers from physical grounding and extrapolation into new regimes, as well as in settings where large data sets do not exist.
In this talk I explore an intermediate regime, which is hybrid reduced order models: fast simplified physics approximations where some of the unknown or approximated equations are replaced with data-driven machine learning components. Examples include coarse-grained models where the full macroscopic equations cannot be derived from first-principles microscopic equations, multiscale models with unknown closure terms or sub-grid parameterization schemes, and low-order or latent dynamical systems that learn governing equations on a low-dimensional reduced state space. I discuss how such reduced systems can be identified from very limited data, much less than is often needed in traditional machine learning but at much lower time-to-solution than traditional numerical modeling. This facilitates not only system design and control but also uncertainty quantification approaches that search the space of possible equations for predictive models that can explain the data. I will focus on an example from materials science concerning the design of self-assembling block copolymer nanomaterials.

Speaker: Dr. Nathan Urban, Applied Mathematics Department, Brookhaven National Laboratory

Location: Laufer 101

Zoom: https://stonybrook.zoom.us/j/96090260834?pwd=mw8QTHbMOw9oeU9hazZeoq8bN4VIfH.1
Meeting ID: 960 9026 0834 Passcode: 374969
Unlock the power of AI in your job search! Join the Head of Indeed Job Search Academy and AI experts as they explore how to leverage cutting-edge AI tools to optimize your job search activities, enhance your resume, prepare for interviews, and conduct thorough
career research, as well as answer all your AI-related questions.
This virtual watch party session will equip you with the knowledge to stand out in today's competitive market.

https://forms.gle/TtWu3iDh9bmU3niD6
Abstract: Many unresolved legal questions over LLMs and copyright center on memorization: whether specific training data have been encoded in the model's weights during training, and whether those memorized data can be extracted in the model's outputs. While many believe that LLMs do not memorize much of their training data, recent work shows that substantial amounts of copyrighted text can be extracted from open-weight models. However, it remains an open question if similar extraction is feasible for production LLMs, given the safety measures these systems implement. We investigate this question using a two-phase procedure: (1) an initial probe to test for extraction feasibility, which sometimes uses a Best-of-N (BoN) jailbreak, followed by (2) iterative continuation prompts to attempt to extract the book. We evaluate our procedure on four production LLMs -- Claude 3.7 Sonnet, GPT-4.1, Gemini 2.5 Pro, and Grok 3 -- and we measure extraction success with a score computed from a block-based approximation of longest common substring (nv-recall). With different per-LLM experimental configurations, we were able to extract varying amounts of text. For the Phase 1 probe, it was unnecessary to jailbreak Gemini 2.5 Pro and Grok 3 to extract text (e.g, nv-recall of 76.8% and 70.3%, respectively, for Harry Potter and the Sorcerer's Stone), while it was necessary for Claude 3.7 Sonnet and GPT-4.1. In some cases, jailbroken Claude 3.7 Sonnet outputs entire books near-verbatim (e.g., nv-recall=95.8%). GPT-4.1 requires significantly more BoN attempts (e.g., 20X), and eventually refuses to continue (e.g., nv-recall=4.0%). Taken together, our work highlights that, even with model- and system-level safeguards, extraction of (in-copyright) training data remains a risk for production LLMs.

Speaker: Xinyue

Location: CS2311
Prepare your Business for the AI-driven future with DocItUSA's Document Management Solutions.
In today's digital world, businesses need to leverage the benefits of the Digital Cloud Age by streamlining Document Organization, Storage, and Accessibility.
Michael Feingold, of Digital Onesource Consulting Solutions/DOCS Consulting, Inc. will show you how DocItUSA equips your company with the tools to efficiently capture, classify, and retrieve documents and enable seamless AI integration.
https://nysbdc.ecenterdirect.com/events/1019400
Discover how U.S. Census Bureau Tools can help you find free data for your research projects, community, and more. See how to access the latest American Community Survey and 2020 Census data for various geographies including New York City and Long Island at data.census.gov. Learn about Community Resilience Estimates and how to navigate My Community Explorer; an interactive map-based tool which highlights demographic and socioeconomic data that measure inequality. This session will involve live demonstrations and hands-on exercises for participants. Registrants will receive the Zoom link one day prior to the event.

Please Register for SBU Libraries' AI Club: Exploring Census Data here.