Dates:
Friday, April 2, 2021 - 4:00pm to 5:00pm
Location:
Zoom - contact events@cs.stonybrook.edu for Zoom info.
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. We'll take a look at indirect prompt injection- a technique that can trick AI tools into revealing or extracting private information as well as techniques being used in academic contexts to manipulate systems and even mislead researchers.
Register here for the online session.
Register here for the online session.
Abstract:
What is the nature of linguistic knowledge, and how is it acquired from limited data? In recent years, the program of subregular linguistics has identified formal language classes expressive enough to account for most phenomena in natural language but also sufficiently limited to be efficiently learned from positive data. An advantage to these formal learning algorithms is that they come with mathematically proven guarantees about their performance, and it is easy to reason about how and why they behave the way they do.
In this talk, I discuss the Multi Tier-based 2-Strictly Local Inference Algorithm (MT2SLIA), which probably learns the syntactically relevant class of 2-Factor Muti Tier-based Strictly Local (2FMSTL) tree languages. This algorithm efficiently learns from a polynomially-sized sample of positive data by identifying missing substructures and generalizing these as constraints over tiers in a principled manner.
I will introduce a working prototype implementation of this algorithm and demonstrate its behavior on a curated sample of natural language data to show how it can learn relevant syntactic patterns.
Bio:
Logan Swanson is a third year PhD student in the Department of Linguistics at Stony Brook University. He is advised by Dr. Jefferey Heinz and Dr. Thomas Graf. His interests include learning theory, computational syntax, and language change. His current research focuses on understanding the learning-theoretic elements of natural language by designing, implementing, and testing learning algorithms for linguistically relevant formal language classes.
*Please note: this seminar will be held in person (IACS Seminar Room w/ food provided) and online.
Join Zoom Meeting
https://stonybrook.zoom.us/j/95707958315?pwd=6ITUJ0ffCXjRJb4wpt0KMDTApfSLZ0.1
Meeting ID: 957 0795 8315
Passcode: 920473
What is the nature of linguistic knowledge, and how is it acquired from limited data? In recent years, the program of subregular linguistics has identified formal language classes expressive enough to account for most phenomena in natural language but also sufficiently limited to be efficiently learned from positive data. An advantage to these formal learning algorithms is that they come with mathematically proven guarantees about their performance, and it is easy to reason about how and why they behave the way they do.
In this talk, I discuss the Multi Tier-based 2-Strictly Local Inference Algorithm (MT2SLIA), which probably learns the syntactically relevant class of 2-Factor Muti Tier-based Strictly Local (2FMSTL) tree languages. This algorithm efficiently learns from a polynomially-sized sample of positive data by identifying missing substructures and generalizing these as constraints over tiers in a principled manner.
I will introduce a working prototype implementation of this algorithm and demonstrate its behavior on a curated sample of natural language data to show how it can learn relevant syntactic patterns.
Bio:
Logan Swanson is a third year PhD student in the Department of Linguistics at Stony Brook University. He is advised by Dr. Jefferey Heinz and Dr. Thomas Graf. His interests include learning theory, computational syntax, and language change. His current research focuses on understanding the learning-theoretic elements of natural language by designing, implementing, and testing learning algorithms for linguistically relevant formal language classes.
*Please note: this seminar will be held in person (IACS Seminar Room w/ food provided) and online.
Join Zoom Meeting
https://stonybrook.zoom.us/j/95707958315?pwd=6ITUJ0ffCXjRJb4wpt0KMDTApfSLZ0.1
Meeting ID: 957 0795 8315
Passcode: 920473
The coach who led Team USA to four Math Olympiad gold medals shares his blueprint for staying irreplaceable in an AI-driven world.
As artificial intelligence transforms our world, what skills will remain uniquely human? How can we prepare for careers in an automated future?
As artificial intelligence transforms our world, what skills will remain uniquely human? How can we prepare for careers in an automated future?
Join Carnegie Mellon mathematics professor Po-Shen Loh for insights on navigating the AI revolution by embracing our humanity.
Dr. Loh brings a distinctive perspective shaped by his dual expertise: serving as national coach of the USA Mathematical Olympiad team (which has won four gold medals under his leadership) and developing innovative solutions for real-world challenges from pandemic response to educational technology.
Through his nationwide speaking tour that reached 250 audiences across 100 cities, he has refined a practical framework for thriving alongside AI.
In this presentation, Dr. Loh will explore how creative problem-solving, judgment, and communication become more valuable as automation grows -- and how students and professionals can build those strengths now.
The session includes real-world examples, guidance for education and careers, and a Q&A.
Speaker: Po-Shen Loh is a social entrepreneur and inventor, working across the spectrum of mathematics, education, and healthcare.
A math professor at Carnegie Mellon University, he also served a decade-long term as the national coach of the USA International Mathematical Olympiad (IMO) team, taking the team to gold on numerous occasions.
He has pioneered numerous innovations and has been featured in or co-created YouTube videos with more than 25 million views.
Location: Wang Center Theater
The series is offered by Stony Brook University's Institute for Creative Problem Solving in collaboration with the National Museum of Mathematics (MoMath) and Brookhaven National Laboratory.
The event is free but space is limited. Please register to reserve your space.
Abstract: Generating high-fidelity EEG data at scale is essential for overcoming dataset scale limitation and satisfying privacy requirements in computational neuroscience. However, the predominant reliance on discrete denoising formulations fails to adequately capture the continuous temporal evolution and frequency-domain characteristics intrinsic to EEG recordings. Consequently, such approaches often compromise long-range temporal coherence and introduce structural discrepancies in both spectral and temporal domains.
In this work, We argue that faithful EEG synthesis demands generative models that directly characterize the continuous dynamics underlying neural signals. To this end, we propose a conditional flow matching framework that treats EEG as raw waveform sequences traversing continuous-time trajectories. Rather than relying on discretized denoising steps or handcrafted signal representations, our method learns a smooth velocity field mapping noise distributions to the target EEG manifold, thereby naturally preserving temporal continuity and transient neural phenomena. To enforce fidelity to fundamental EEG characteristics, we incorporate principled constraint terms that maintain spectral consistency, temporal stationarity, and signal-level statistical properties. Evaluated on large-scale benchmarks, our approach establishes new state-of-the-art results compared to competitive baselines. Comprehensive analyses further confirm that the proposed framework faithfully recovers core structural attributes of neural dynamics, offering a scalable and theoretically grounded solution for high-fidelity EEG generation.
Speaker: Yifan Wang
Location: https://stonybrook.zoom.us/j/
Meeting ID: 953 0027 9172
Passcode: 418725
Abstract: Language offers a uniquely powerful lens for understanding the mind: one that can access latent psychological realities often missed by traditional measurement tools. However, as language models expand their ability to capture semantics through context length, expansion into deeper levels of semantics is less explored, especially with respect to understanding cognitive patterns of authors. This dissertation proposes that we can uncover deeper cognitive and affective patterns that reflect more accurate underlying mental states by analyzing language at higher levels of discourse semantics and by modeling latent states.
First, the dissertation focuses on uncovering cognitive styles or thinking patterns manifesting in language. We demonstrate that modeling language at deeper semantic levels such as discourse relations, can unveil latent psychological states and traits, including cognitive styles that influence both mental health and behavior. Introducing a novel blend of transfer and active learning, we efficiently curated a new set of linguistic data on cognitive styles like dissonance. This approach allows for more precise measurement when dealing with rare-classes and low-resource tasks. As a second contribution, effective validation methods are introduced to language-based assessments of the underlying cognitive styles. Controlled behavioral experiments and online studies show that cognitive styles detected through linguistic signals reliably predict real-world behaviors such as decision-making and engagement with extremist communities, both at the individual and community levels, sometimes months in advance
The research further moves beyond traditional measurement tools like questionnaires and expert judgments, which rely on Classical Test Theory, by establishing that language-based assessments more closely approximate true psychological states. The mechanisms by which these assessments outperform standard tools are explained, highlighting their predictive power for behaviors linked to underlying traits. Finally, a more sophisticated approach is explored by modeling psychological outcomes with Item Response Theory (IRT), an improvement over Classical Test Theory. Adaptive language-based assessments are introduced, showing that targeted, adaptive testing based on latent IRT scores can efficiently and accurately capture multiple psychological dimensions.
Taken together, these contributions argue for a shift towards language-based psychological assessments. By integrating deeper discourse-level semantics with measurement theory, this dissertation charts a path towards truer scores of mental states: ones that are more precise, and reflective of the complexity of human cognition and emotions.
Speaker: Vasudha Varadarajan
https://stonybrook.zoom.us/j/
First, the dissertation focuses on uncovering cognitive styles or thinking patterns manifesting in language. We demonstrate that modeling language at deeper semantic levels such as discourse relations, can unveil latent psychological states and traits, including cognitive styles that influence both mental health and behavior. Introducing a novel blend of transfer and active learning, we efficiently curated a new set of linguistic data on cognitive styles like dissonance. This approach allows for more precise measurement when dealing with rare-classes and low-resource tasks. As a second contribution, effective validation methods are introduced to language-based assessments of the underlying cognitive styles. Controlled behavioral experiments and online studies show that cognitive styles detected through linguistic signals reliably predict real-world behaviors such as decision-making and engagement with extremist communities, both at the individual and community levels, sometimes months in advance
The research further moves beyond traditional measurement tools like questionnaires and expert judgments, which rely on Classical Test Theory, by establishing that language-based assessments more closely approximate true psychological states. The mechanisms by which these assessments outperform standard tools are explained, highlighting their predictive power for behaviors linked to underlying traits. Finally, a more sophisticated approach is explored by modeling psychological outcomes with Item Response Theory (IRT), an improvement over Classical Test Theory. Adaptive language-based assessments are introduced, showing that targeted, adaptive testing based on latent IRT scores can efficiently and accurately capture multiple psychological dimensions.
Taken together, these contributions argue for a shift towards language-based psychological assessments. By integrating deeper discourse-level semantics with measurement theory, this dissertation charts a path towards truer scores of mental states: ones that are more precise, and reflective of the complexity of human cognition and emotions.
Speaker: Vasudha Varadarajan
https://stonybrook.zoom.us/j/
The Della Pietra Lecture Series is pleased to present this lecture by Scott Aaronson:
Abstract: Yes, scalable quantum computing should actually work! Sooner than many expect, which will create a huge headache when it breaks the encryption currently used to protect the Internet. But no, we don't think quantum computing can do most of what the popular articles promise in AI and optimization and so forth. Come to this talk to learn about why!
Speaker: Scott Aaronson is Schlumberger Chair of Computer Science at the University of Texas at Austin, and founding director of its Quantum Information Center. He received his bachelor's from Cornell University and his PhD from UC Berkeley. Aaronson's research has focused mainly on the capabilities and limits of quantum computers. His first book, Quantum Computing Since Democritus, was published in 2013 by Cambridge University Press. He received the National Science Foundation's Alan T. Waterman Award, the United States PECASE Award, the Tomassoni-Chisesi Prize in Physics, and the ACM Prize in Computing, and is a Fellow of the ACM and the AAAS and a member of the National Academy of Sciences. He blogs at Shtetl-Optimized, https://www.scottaaronson.com/blog.
Location: Della Pietra Family Auditorium (SCGP 103)
Abstract: Yes, scalable quantum computing should actually work! Sooner than many expect, which will create a huge headache when it breaks the encryption currently used to protect the Internet. But no, we don't think quantum computing can do most of what the popular articles promise in AI and optimization and so forth. Come to this talk to learn about why!
Speaker: Scott Aaronson is Schlumberger Chair of Computer Science at the University of Texas at Austin, and founding director of its Quantum Information Center. He received his bachelor's from Cornell University and his PhD from UC Berkeley. Aaronson's research has focused mainly on the capabilities and limits of quantum computers. His first book, Quantum Computing Since Democritus, was published in 2013 by Cambridge University Press. He received the National Science Foundation's Alan T. Waterman Award, the United States PECASE Award, the Tomassoni-Chisesi Prize in Physics, and the ACM Prize in Computing, and is a Fellow of the ACM and the AAAS and a member of the National Academy of Sciences. He blogs at Shtetl-Optimized, https://www.scottaaronson.com/blog.
Location: Della Pietra Family Auditorium (SCGP 103)
Join us as we celebrate this year's Brook & Beyond Challenge finalists.
The Office for Research and Innovation invites you to hear about the two-month journey in which the Brook & Beyond team supported eight cohorts in bringing their bold ideas from the lab to the marketplace. It's an energizing evening that highlights the collaboration, creativity, and entrepreneurial spirit driving discovery across the University.
Meet this year's award recipients, hear pitches from the emerging founders, and applaud their achievements.
Connect, celebrate, and be part of the momentum shaping the future of innovation at
Stony Brook University.
Refreshments will be served. Registration is required.
Register Here.
The Office for Research and Innovation invites you to hear about the two-month journey in which the Brook & Beyond team supported eight cohorts in bringing their bold ideas from the lab to the marketplace. It's an energizing evening that highlights the collaboration, creativity, and entrepreneurial spirit driving discovery across the University.
Meet this year's award recipients, hear pitches from the emerging founders, and applaud their achievements.
Connect, celebrate, and be part of the momentum shaping the future of innovation at
Stony Brook University.
Refreshments will be served. Registration is required.
Register Here.
Abstract: Recent progress in large language and vision models demonstrates how far we can go by scaling with vast internet-scale data. In contrast, physical AI, agents that perceive and act in the real world, still lags far behind. Today, both academia and industry primarily pursue generalizable physical AI by scaling up: collecting large-scale action-video datasets or training world models that enable interaction through learned environments. However, this paradigm is inherently inefficient and will soon reach a data ceiling. In this talk, I argue for a shift from scaling up to scaling out. I introduce reality world simulators, a new paradigm that converts real-world videos into diverse, interactive simulation environments. Instead of relying on more data collection, this approach expands data through structured reconstruction and recomposition, enabling both higher data efficiency and physically grounded interaction. I will present a three-pronged approach: 1) Scaling out via Digital Twins: reconstructing controllable, interactive environments from monocular videos to support diverse agent exploration. 2) Scaling out via Digital Cousins: disentangling scene structure into compositional elements to generate large-scale variations of real-world environments. 3) Scaling out via Embodied Humans: incorporating realistic human dynamics to improve safety and social compliance in robot learning. Finally, I will outline a roadmap toward building generalizable and safe physical AI systems for open-world deployment.
Bio: Dr. Wayne Wu is a postdoctoral researcher at UCLA Computer Science, working closely with Bolei Zhou, and collaborating with Trevor Darrell (UC Berkeley EECS) and Jiaqi Ma (UCLA CEE). He received his Ph.D. in Computer Science and Technology from Tsinghua University in June 2022 and was previously a visiting Ph.D. student at Nanyang Technological University. He also spent seven years in industry, where he led the research and development of products that reached more than 10 million end users worldwide. His research lies at the intersection of computer vision, robotics, and computer graphics. He focuses on developing infrastructure and methods to scale physical AI, enabling robots to work reliably and safely in the open world. He has published over 50 papers at top-tier venues including CVPR, ICCV, ICLR, NeurIPS, and ICRA, with over 9,500 citations and 10,000 GitHub stars. His work has received a CVPR Best Paper Candidate and multiple Oral, Spotlight, and Highlight presentations. He was also honored with the 2025 UCLA Chancellor's Award for Postdoctoral Research, recognizing the best postdocs at UCLA, and he was the only awardee from the School of Engineering. He serves as an Area Chair at CVPR 2026.
Location: NCS 120
Bio: Dr. Wayne Wu is a postdoctoral researcher at UCLA Computer Science, working closely with Bolei Zhou, and collaborating with Trevor Darrell (UC Berkeley EECS) and Jiaqi Ma (UCLA CEE). He received his Ph.D. in Computer Science and Technology from Tsinghua University in June 2022 and was previously a visiting Ph.D. student at Nanyang Technological University. He also spent seven years in industry, where he led the research and development of products that reached more than 10 million end users worldwide. His research lies at the intersection of computer vision, robotics, and computer graphics. He focuses on developing infrastructure and methods to scale physical AI, enabling robots to work reliably and safely in the open world. He has published over 50 papers at top-tier venues including CVPR, ICCV, ICLR, NeurIPS, and ICRA, with over 9,500 citations and 10,000 GitHub stars. His work has received a CVPR Best Paper Candidate and multiple Oral, Spotlight, and Highlight presentations. He was also honored with the 2025 UCLA Chancellor's Award for Postdoctoral Research, recognizing the best postdocs at UCLA, and he was the only awardee from the School of Engineering. He serves as an Area Chair at CVPR 2026.
Location: NCS 120
Join us to share your thoughts about teaching, learning, and AI!
The landscape of higher education is rapidly evolving with the integration of Artificial Intelligence (AI). Through the Institute on AI, Pedagogy, and the Curriculum with AAC&U, we are exploring ways that we can better address AI in teaching and learning. We want to hear your experiences, your concerns, and your ideas.
This is an open discussion for all faculty and staff to share their perspectives on the opportunities and challenges AI presents in our academic environment.
We'll be exploring critical questions like:
In the age of AI, what are the opportunities you see for enriching the classroom and curriculum? How can it enhance student learning or your professional practice?
What are the most significant challenges and concerns that AI raises for you regarding academics, student integrity, or your workload?
What resources (tools, training, technical support, policy guidance, etc.) do you need to feel confident and successful in the age of AI?
Dates/Times:
Tuesday, 2/3 at 2pm
Friday, 2/6 at 9:30am
Please register in advance for the Zoom link.
Can't Make It? Share Your Feedback!
We understand schedules are tight. If you cannot attend the live discussion, you can still share your thoughts! Join our AI Zoom Room to share your thoughts via video recording or email rose.tirotta-esposito@stonybrook.edu with your comments and ideas.
Videos will not be shared publicly and comments will only be shared in aggregate.
Your input is vital. From pedagogy to assessment, your insights will be critical. We look forward to a thoughtful and productive conversation!
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)