Learn which AI models are available at SBU and how to access them.

As AI becomes part of our everyday work, it's important to know which AI models are available at Stony Brook and how to access them. In the first half of this session, we'll show you how to access Stony Brook's Gemini and Microsoft Copilot models and how to make sure you're using the university-approved environment. In the second half, we'll review Stony Brook's Data Classification policies so you know what institutional data can be used with these AI tools and what information should remain protected, including sensitive data. This session will provide the latest campus guidance so you can use AI confidently and responsibly.

Register here.

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 15 minutes) by multiple students. Students can register for 1 credit for CSE656. Registered students must attend and present a minimum of 2 talks. Everyone else is welcome to attend. Fill in https://forms.gle/q6UG9ygauLp2a8Po8 to subscribe to our mailing list for further announcement.
Abstract: In this talk, we take a step back and argue that many varied and seemingly unrelated attacks on the web are actually symptoms of one deeper problem that has existed since the web's inception. Whether it is attacks due to expired domain names, cloaking done by malicious websites, malvertising, or even our growing distrust of the news can be largely attributed back to the issue of stateless linking. Stateless linking refers to the absence of any integrity guarantees between the time that a link for a remote resource was created, to a future time when this link is resolved by web clients. We draw on 10+ years of research to demonstrate how stateless linking and the resulting lack of content integrity is the true culprit for many of our past, current, and likely future web problems. Successfully tackling this one really challenging problem, has the potential of solving many of our web woes.

Bio: Nick Nikiforakis is affiliated with the National Security Institute. He received his PhD in Computer Science from KU Leuven in Belgium. He received his MSc, in Parallel and Distributed Systems and BSc in Computer Science from the University of Crete, Greece. His research focuses on web security and privacy, software security, and intrusion detection.

Location: NCS 120

Chat with Sociology faculty as they share their paths to StonyBrook-what inspired their careers, what led them to teaching,and the experiences that shaped their academic journey.

Dr. Yongjun Zhang

Assistant Professor of Sociology, Departments of Sociology and AAAS

Join this opportunity to talk to Yongjun Zhang about his new interest in the following responsible usage of AI in addressing climate and health issues. Lunch will be served.

Location: SBS Level 4- Sociology Reading Room

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Topic: AI Seminar: Owen Rambow
Time: Mar 17, 2021 10:00 AM Eastern Time (US and Canada)
Join Zoom Meeting

https://stonybrook.zoom.us/j/93614644178?pwd=MzJtVDJYYmU5T1dtMzJiUFMxb0x4dz09
Meeting ID: 936 1464 4178.    Passcode: 965936






Natural Language Understanding and Semantic Parsing

(Partly joint work with former colleagues at Elemental Cognition)

Semantic parsing refers to the task of determining the propositional content of language: who did what to whom.  It is part of the larger task of natural language understanding (NLU).  I will start out by discussing what full NLU means, and argue that we are still far away, as a field, from solving full NLU, or even from knowing how to evaluate it.

In the second part of the talk, I will situate semantic parsing in the context of several other NLU subtasks.  Typically, the target representation of semantic parsing uses an ontology (such as PropBank or FrameNet).  Semantic parsing includes the subtasks of word sense disambiguation, argument detection, and argument role labeling.  I will discuss choices among possible target ontologies.  I will justify why we created a new ontology, Hector, based on FrameNet and the lexical resource NOAD, and explain some of its characteristics.

In the third part of the talk, I will present experiments we performed using transformer models.  We obtain best results using a two-phase model, in which we first choose the frame, and then, given the frame, choose the arguments.  We encode the problem for both tasks using indices in the sentence.  While we develop the parser for our new ontology Hector, this approach also beats the state of the art for FrameNet and PropBank parsing.Biography:  I am a professor in the Department of Linguistics at Stony Brook University with a joint appointment in IACS.

Until recently, I was a research scientist at Elemental Cognition. Elemental Cognition is working on deep natural language understanding.

I got my PhD with Aravind Joshi at the University of Pennsylvania in 1994. I have worked at CoGenTex, and at AT&T Labs -- Research, and for many years I was a research scientist at Columbia University in the Center for Computational Learning Systems.


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Register here.
2026 MIC Symposium on Multilingualism | Featured Talk

Beyond Cultural Pastiche: Developing CritIC in AI-mediated Clinical Communication

Abstract: Health professional education is taking diversity and cultural sensitivity seriously, and rightly so. As future clinicians work with diverse patients, educators are seeking meaningful, scalable opportunities for communication training. AI-facilitated simulation appears to offer one solution, with avatar patients generated with recognisable cultural cues. Yet this raises an unsettling question: how does AI reproduce culture and what impact does it have on health trainees? Through analysing multimodal clinical communication data from human-AI and human-human interactions, this talk explores fundamental differences in how AI and humans evoke culture. AI-generated cultural talk resembles a pastiche of essentialist cues that flatten context, diminish human criticality, and reproduce structural inequalities. Human cultural talk, by contrast, creates context, expands imagination, and subverts pre-existing boundaries. I conclude by arguing that the next generation of health professionals needs not only EDI awareness but also Critical AI Literacy, conceptualised as CritIC (Critical Interactional Competence), in AI-facilitated clinical communication.

Speaker: David Wei Dai is Associate Professor in Professional Communication at University College London. His research examines social interaction across healthcare education, intercultural and professional communication, language learning and assessment, and human-AI engagement. His work has appeared in leading venues across applied linguistics, health education, and learning analytics, including Applied Linguistics, Language Learning, Annual Review of Applied Linguistics, Medical Education, Advances in Health Sciences Education, BMC Medical Education, and the LAK26 Proceedings. He is the author of the open-access monograph Assessing Interactional Competence. His research has been funded by the British Academy, Leverhulme, the British Council, OET, Duolingo, and TOEFL.

Faculty Response Panel:
  • Yi Wang (Panel Chair; Assistant Professor, Asian and Asian American Studies)
  • Renee Fabus (Chair and Program Director, Speech-Language Pathology)
  • Andrew Flescher (Professor, English and Program in Public Health)
  • Ben Zhang (Assistant Professor, Technology, AI and Society)

Location: Humanities 1006

This workshop synthesizes the latest research on the impact of AI usage in education so that you could make informed decisions on whether and how to use AI to facilitate your learning. You might have seen conflicting reports on whether the use of AI is good for learning. In this workshop, we are going to tease out, drawing on the latest research, which types of AI usage are beneficial or harmful for different kinds of learning. At the end of the workshop, you should walk away with more clarity on when and how to use AI for your own learning. Join PRODIG+ fellow on critical AI, Zheng Fu, in this informative workshop.

Register for this Zoom workshop.