Matthew Salzano (Stony Brook), AI and DEIA: Getting at the Roots

Link to the talk (no pre-registration required this time): https://stonybrook.zoom.us/j/96209347479?pwd=Cs8fEfFdbXrGTC5cQgyHRb8Msh5vp8.1Meeting ID: 962 0934 7479 Passcode: 272489

Abstract: Conversations about AI and DEIA (Diversity, Equity, Inclusion, and Access) often unwittingly assume that social problems can and should have technical fixes. Left unaddressed, scholars, advocates, and technologists inevitably miss important consequences in our proposed solutions, and focus on surface-level problems rather than addressing the root causes of inequity. Drawing from scholarship in communication, rhetoric, and critical digital studies, this talk explains how we are often trimming branches when we need to pull out roots -- and introduces new terms and questions that can help reorient our conversations about AI and DEIA.

Speaker Bio: Matthew Salzano, Ph.D., is a communication scholar researching new media technologies, user practices, and cultural trends that threaten to limit possibilities for diverse engagement in public argument, debate, and protest. His scholarship has appeared in journals like The Quarterly Journal of Speech, Critical Studies in Media Communication, and Women's Studies in Communication, and his research on DEIA, AI, and advocacy communications has been funded by the Waterhouse Family Institute at Villanova University. He is currently an Inclusion, Diversity, Equity, and Access fellow in Ethical AI at Stony Brook University's School of Communication and Journalism and Alan Alda Center for Communicating Science.

What AI tools are available to help with the scholarly research process? Are they helpful? What do they do and is it worth the time and energy to try them out? Join librarian Christine Fena to explore and compare established and emerging AI research tools such as Elicit, Scite, Consensus, and Undermind. The online workshop will provide a starting point to understanding what these tools are, the basics of how they work, and how AI research assistants might bring changes to your search process in the future. All are welcome!



Register here via Zoom.

Abstract:
Quantum Machine Learning (QML) holds significant promise for solving computational challenges across diverse domains. However, its practical deployment is constrained by the limitations of noisy intermediate-scale quantum (NISQ) devices, including noise, limited scalability, and trainability issues in variational quantum circuits (VQCs). We introduce the multi-chip ensemble VQC framework, which partitions high-dimensional computations across smaller quantum chips to enhance scalability, trainability, and noise resilience. We show that this approach mitigates barren plateaus, reduces quantum error bias and variance, and maintains robust generalization through controlled entanglement. Designed to align with current and emerging quantum hardware, the framework demonstrates strong potential for enabling scalable QML on near-term devices, as validated by experiments on standard benchmark datasets (MNIST, FashionMNIST, CIFAR-10).

IACS Student Seminar Speaker:
Junghoon Park, Seoul National University
BA in Economics, Seoul National University, Korea
PhD Candidate for Interdisciplinary Programme in Artificial Intelligence at Seoul National University
Visiting Researcher at Brookhaven National Laboratory


Current Research Interests
Quantum Machine Learning


Recent Papers
Park, J., Cha, J., Chen, S. Y.-C., Yoo, S., & Tseng, H.-H. (2025). Addressing the Current Challenges of Quantum Machine Learning through Multi-Chip Ensembles. In Review at ICML.
Park, J., Kim, K., & Cha, J. (2025). How to Assess AI Ethics: Suggestions for Ethical Rating Agencies. In Review at IJCAI.
Park, J., Cha, J., Chen, S. Y.-C., Yoo, S., & Tseng, H.-H. (2024, 15-20 Sept.). Over the Quantum Rainbow: Explaining Hybrid Quantum Reinforcement Learning. 2024 IEEE International Conference on Quantum Computing and Engineering (QCE).
Park, J., Lee, E., Cho, G., Hwang, H., Kim, B.-G., Kim, G., Joo, Y. Y., & Cha, J. (2024). Gene-Environment Pathways to Cognitive Intelligence and Psychotic-Like Experiences in Children. eLife, 12, RP88117. DOI:10.7554/eLife.88117

This seminar will be held in person (food provided!) in the IACS Seminar Room, and online (zoom link below!)
https://stonybrook.zoom.us/j/96548538719?pwd=jBmI43H68q2UkdcRRjVbTkgrC6F942.1
Meeting ID: 965 4853 8719
Passcode: 493290

Abstract: Jailbreak attacks circumvent LLMs' built-in safeguards by concealing harmful queries within adversarial prompts. While most existing defenses attempt to mitigate the effects of adversarial prompts, they often prove inadequate as adversarial prompts can take arbitrary, adaptive forms. This paper introduces RobustKV, a novel jailbreak defense that takes a fundamentally different approach by selectively removing critical tokens of harmful queries from key-value (KV) caches. Intuitively, for an adversarial prompt to be effective, its tokens must achieve sufficient `importance' (measured by attention scores), which consequently lowers the importance of tokens in the concealed harmful query. Therefore, by carefully evicting the KVs of low-ranked tokens, RobustKV minimizes the harmful query's presence in the KV cache, thus preventing the LLM from generating informative responses. Extensive evaluation using benchmark datasets and models demonstrates that RobustKV effectively counters state-of-the-art jailbreak attacks while maintaining the LLM's performance on benign queries. Notably, RobustKV creates an interesting effectiveness-evasiveness dilemma for the adversary, leading to its robustness against adaptive attacks.

Speaker: Tanqiu Jiang

Where: NCS 220 and Zoom (https://stonybrook.zoom.us/j/6406956411)

Learn how these two AI tools will help you this year.

AI has been all over, but figuring out the tools that we may use is critical. Background remover of images and a replacement for Google Search may disrupt the industry this year. Learn and refresh your knowledge about these tools.

Understand how AI can be used for your marketing.

Register here.

Learn about how to ask AI question of Notebook LM and the new interactive features.

Notebook LM allows you to interact with multiple sources of information. You can chat or ask follow up questions. Learn how you can gain more insights through these interactions.

Register here.

Spring 2025, Mondays 3.30 to 4.50 pm, NCS 220.

The seminar will be jointly taught by Prof. Chao Chen, chao.chen.1@stonybrook.edu and Prof. Dimitris Samaras samaras@cs.stonybrook.edu

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 Ph.D. program or (2) receive permission from the instructors.

Each seminar will consist of multiple short talks (around 15 minutes) by multiple students. Students can register for 1 credit for CSE656. Registered students must attend and present a minimum of 2 talks. Registered students must attend in person. Up to 3 absences will be excused. Everyone else is welcome to attend.

Join here. Meeting ID: 927 2069 8658. Passcode: 130934.
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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.