AI is everywhere -- and so are the privacy concerns that come with it. At its core, the most common forms of AI we use today are online digital services -- and thus inherit the usual privacy risks of any internet-based tool. However, AI also introduces a set of unique and evolving risks. We'll take a closer look at one of the newest developments in this area: indirect prompt injection -- a technique that can trick AI tools into revealing or extracting private information. You'll learn how this emerging form of AI manipulation works, why it matters, and how to protect yourself -- as well as how similar techniques are being used in academic contexts to manipulate systems and even mislead researchers.

Register for this Zoom workshop.

This is Stony Brook's quantum moment. Join us for a spotlight on the core achievements and research excellence of faculty across the Colleges of Arts and Sciences (CAS), and Engineering and Applied Sciences (CEAS) - and their collaborative advancements in quantum science and technology. Learn about the real world impact of their enduring work, their leadership in translating foundational science into entrepreneurial opportunities, and their impetus for making connections to next generation innovation.

Presented by: Catherine Chen, Ph.D., Research Development Associate

Welcome remarks: President Andrea Goldsmith

Panel moderators: Dean David Wrobel, CAS, and Dean Andrew Singer, CEAS

Presentations and panel featuring our faculty:

  • Jennifer Cano, CAS, Physics and Astronomy

  • P. Scott Carney, CEAS, Mechanical Engineering

  • Hyeongrak Chuck Choi, CEAS, Electrical and Computer Engineering

  • Eden Figueroa, CAS, Physics and Astronomy

  • Humanshu Gupta, CEAS, Computer Science

  • Angela Kelly, CAS, Physics and Astronomy

Location: Theatre at the Charles B. Wang Center, Stony Brook University

Reserve your tickets by March 26!

You are cordially invited to attend the biweekly Brookhaven AI Mixer (BAM). BAM includes three short talks on AI research happening at BNL, followed by an open mixer over coffee and snacks for everyone to network and discuss all things AI. The first half hour will consist of presentations that will be available via ZOOM, and the second half hour will be for in person only networking.

Join us every other Tuesday at noon in CDSD's Training Room (building 725, 2nd floor) to learn about interesting AI methods and applications, engage with potential collaborators, prepare for pending FASST funding calls, and build a community of AI for Science at BNL.

Tuesday, January 7, 2025, 12:00 pm -- CDS, Bldg. 725, Training Room

Speakers

Maria Zawadowicz, EBNN--ML for Atmospheric Aerosol Research

Mohammad Atif, CDS--An Extensible Digital Twin Framework

Guang Zhao, CDS--Pareto Prompt Optimization

Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1615289117?pwd=Hqkbj9itxWrFnkhZ8rQXHPInO2gxdF.1

Meeting ID: 161 528 9117
Passcode: 991382

Please join us for the next CSE 600 Seminar this Friday, October 11th, at 2:30pm in New Computer Science 120 given by Assistant Professor Mohammad Javad Amiri. Abstract: Today's distributed transaction processing systems must deal with untrustworthy environments where multiple mutually distrustful entities communicate with each other, and maintain data on untrusted infrastructure. Byzantine Fault-Tolerant (BFT) protocols have recently been extensively used by distributed transaction processing systems to establish consensus on the order of transactions. However, the proliferation of different BFT protocols has made it difficult to navigate the BFT landscape, let alone determine the protocol that best meets application needs. Moreover, as novel smart contracts, modern hardware, and new cloud platforms arise, future-proof distributed transaction processing systems need to be designed with full-stack adaptivity in mind. This talk presents our vision for a reinforcement learning (RL)-based distributed transaction processing system that adjusts effectively in real-time to changing fault scenarios and workloads.
Abstract: In today's digital era, language functions not only as a medium of information transmission but also as a mechanism of persuasion, framing, and control. The proliferation of online platforms has amplified this dual role: while enabling unprecedented access to knowledge, it has also exacerbated challenges such as misinformation, rhetorical manipulation, and cultural or linguistic disparities in information access. As a result, pragmatic language understanding and information integrity have emerged as central concerns for both computational linguistics and society at large. This research follows how claims are produced, reframed, and contested online through three interconnected threads. First, it models pragmatic deflection in discourse by investigating whataboutism, a rhetorical device that deflects criticism by redirecting discourse, and introduced novel datasets from Twitter (now X) and YouTube. This work underscores how subtle pragmatic maneuvers can erode discourse integrity without relying on outright falsehoods. Second, it advances retrieval and alignment for information integrity in health and news communication. These systems trace claims and narratives across genres (e.g., social posts and news reports) and languages (Chinese and English), linking social posts with journalistic reporting and aligning Chinese news with English biomedical evidence. By accounting for cultural context, assertions can be linked to reliable evidence and organized for systematic comparison. This work surfaces the risks of missing sources, unverifiable claims, and framing disparities in global health discourse, and demonstrates computational solutions that enhance both the credibility and accessibility of information. Third, the methodological centerpiece is Class Distillation (ClaD), a geometry-aware training paradigm for distilling a small, well-defined target class from a large, heterogeneous background. ClaD couples a distribution-aware contrastive loss (instantiated here in a Mahalanobis form when its assumptions fit the data) with an interpretable decision algorithm tuned for class separation. Evaluated on sarcasm, metaphor, and sexism detection, ClaD delivers strong efficiency and robustness, matching or surpassing larger models while using fewer computational resources, making these pipelines practical by learning reliably from small, sharply defined classes. In sum, this research presents an integrated account of language understanding in the digital age. It exposes how integrity falters through pragmatic deflection, cross-genre drift, and cross-lingual misalignment, and translates these insights to move pragmatic language understanding to systems for evidence retrieval, alignment, and verification; and it sheds light on where and how integrity is threatened, and delivers methods that leverage pragmatic language use.

Speaker: Chenlu Wang

Location: (Old) Computer Science Building, Room 2311

International Love Data Week is a global event dedicated to celebrating data in all its forms. This year, Stony Brook University is excited to celebrate Love Data Week with a series of 30-minute webinars aimed to promote proficiency with data, showcase innovative data projects, and foster a community of data enthusiasts across campus. Hosted by the Division of Educational & Institutional Effectiveness and facilitated by the Office of Educational Effectiveness, we invite all SBU faculty, staff and students to join in the festivities, learn from colleagues in our campus community, and fall in love with the power of data!

Learn more here.