Recently, large-scale language data combined with modern machine learning techniques have shown strong value as means for studying human psychology and behavior. For example, language alone has been shown predictive in mental health, personality, and health behaviors. However, many applications for such language-based assessments have readily available and important data beyond language (i.e. extra-linguistics), such as predicting the subjective well-being of a community using tweets, where one can take into account their age, education, and demographic attributes. Language may capture some characteristics while extra-linguistic variables captures others. We believe that effectively integrating linguistic and extra-linguistic data can yield benefits beyond either independently.
In this thesis, we develop methods which effectively integrate extra-linguistic data with language data focused primarily on social scientific applications. The central challenge is dealing with the size and heterogeneity of, often sparse and noisy, language data versus the, often low-dimensional and non-sparse, extra-linguistic variables. First, we consider structured extra-linguistics, like socioeconomic (income and education rates) and demographics (age, gender, etc.), and propose two integration methods, named residualized controls (RC) and residualized factor adaptation (RFA), to be used in county-wise prediction tasks. Demonstrating techniques that integrate information at both the model-level and data-level, we found consistently strong improvement over naively combining features, for example, increasing county level well-being predictions by over 12%. Next, we consider unstructured extra-linguistic data. In the first part, we incorporate social network connections and language over time to propose a novel metric for quantifying the stickiness of words - their ability to spread across friendship connections in a social network over time (or in other words, stick in ones vocabulary after seeing friends use it). We obtain which language features are more probable to disseminate through friendship and show such a metric is useful for predicting who will be friends and what content will spread. In addition, we analyze language content over time by proposing a novel dynamic content-specific topic modeling technique that can help to identify different sub-domains of a thematic scope and can be used to track societal shifts in concerns or views over time.
Virtual Job Fair for New Stony Brook Graduates & Experienced Alumni
Using a platform called Career Fair Plus, participants will be able to schedule 10-minute video meetings with participating employers of interest to them.
Recent graduates and alumni can register and learn more about how the fair will be run by registering on Handshake.
Launching a University-Wide AI Innovation Institute:
Last spring, the Office of the Provost led a group of over 30 faculty, staff, and administrators to consider how we can expand and leverage our strengths in AI research and discovery. The resulting recommendation was to launch a university-wide AI Innovation Institute (AI3), which would expand the Institute for AI-driven Discovery and Innovation established in 2018 from a department-level institute within the College of Engineering and Applied Science (CEAS) to the university-wide AI Innovation Institute reporting to the provost.
As a university-wide enterprise, the AI Innovation Institute (AI3) is intended to accelerate, coordinate, and organize AI innovation and education across Stony Brook. The institute will serve to empower the entire university community and beyond, catalyzing core AI research, curriculum innovation, and societal change in the ever-evolving landscape of knowledge work.
The AI Town Hall, led by AI3 Interim Director Skiena, is an open house event that will provide an overview of the major AI initiatives on campus, including the new AI Seed Grant program and Stony Brook's role in New York State's Empire AI program. The session will include time for questions and discussion about the future of AI at Stony Brook.
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
IACS Research Theme: Human Centered Computing Seminar
Abstract: The AI art platform Artbreeder hosts daily remix parties where users build on each other's work, creating transparent evolutionary chains of images from a single seed. This study analyzes 130,882 images from 368 remix parties to identify the drivers of novelty, complexity, and competitive success. The results reveal an interesting tension: while more novel parent images produce more novel and complex children and attract more likes, users paradoxically prefer to remix images that are less novel and complex. At the group level, larger remix parties produce more novelty at the cost of lower complexity. Additionally, images tend to converge towards common thematic attractors (e.g., steampunk scenes, alien architecture, furries) over the course of remix parties. These results provide quantitative insights into collective creativity--the production of novelty by groups of people--a typically opaque aspect of human cultural evolution.
Speaker: Dr. Mason Youngblood
Location: Institute for Advanced Computational Science, Seminar Room
Abstract: The AI art platform Artbreeder hosts daily remix parties where users build on each other's work, creating transparent evolutionary chains of images from a single seed. This study analyzes 130,882 images from 368 remix parties to identify the drivers of novelty, complexity, and competitive success. The results reveal an interesting tension: while more novel parent images produce more novel and complex children and attract more likes, users paradoxically prefer to remix images that are less novel and complex. At the group level, larger remix parties produce more novelty at the cost of lower complexity. Additionally, images tend to converge towards common thematic attractors (e.g., steampunk scenes, alien architecture, furries) over the course of remix parties. These results provide quantitative insights into collective creativity--the production of novelty by groups of people--a typically opaque aspect of human cultural evolution.
Speaker: Dr. Mason Youngblood
Location: Institute for Advanced Computational Science, Seminar Room
Ready for Round Two? Dr. Zach Justus Returns! Join us on October 30, 2025, in the SBU Hilton Garden Inn. Buckle up your curiosity for a high-energy morning session with the engaging Dr. Zach Justus as we navigate how GenAI is reshaping not just how we teach, but what we teach. With real talk and questions that hit hard like Are students learning what we think we're teaching? This is your chance to rethink your program's true destination. Whether you're looking to pick up a few takeaways or chart a new direction entirely, this symposium is your space to explore, reflect, and act.
Check-in and breakfast will begin at 8:30 a.m. in order to begin our program promptly at 9:00 a.m.
Registration will remain open until October 15 or until the event reaches capacity. If closed, please contact educationaleffectiveness@stonybrook.edu to request a spot on the waitlist.
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.
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.
Abstract: Generative models have transformed natural image synthesis, driven by billion-scale datasets of image-caption pairs. Specialized domains, such as digital histopathology and satellite imagery, differ in two ways: their images reach gigapixel resolution, far beyond any feasible training resolution, and they lack paired captions, since captioning the patches of a single whole-slide image (WSI) takes about 40 hours of pathologist time. Overcoming these challenges would enable generative models that create teaching cases of rare diseases, simulate tumor progression, and serve as a foundation for digital twins.
In this thesis, we build generative foundation models for these domains, scaling from small patches to gigapixel images without patch-level annotations. We begin with PathLDM, which conditions histopathology patch generation on
slide-level pathology reports, enabling the first text-to-image generation in histopathology. Since reports do not describe individual patches, we propose LRDM, which uses embeddings from pretrained self-supervised learning (SSL) encoders as patch-level conditioning for both histopathology and satellite images. ZoomLDM extends SSL conditioning across eight magnifications with a single model, capturing structure from individual cells to tissue architecture. To scale across cancer types, we present PixCell, the first generative foundation model for histopathology, trained on 30.8 million patches from 69,184 WSIs across 28 organ types. Synthetic images from PixCell can stand in for real data in SSL training, enable privacy-preserving data sharing, and improve downstream tile-level classifiers. Extending this to gigapixel scale, we introduce BigCell, which generates coherent WSIs of up to 32768x32768 pixels by guiding a 2048x2048 flow-matching transformer with a grid of foundation model embeddings, and speeds up generation by over 3x with an importance-based schedule. BigCell enables text-to-image synthesis at 8192x8192 resolution, with synthetic samples improving slide-level few-shot classification by up to 18%. Beyond pathology, our SSL-conditioned models serve as data generators for self-supervised learning on natural images. Finally, to make gigapixel synthesis practical, we plan to reduce BigCell's generation time from about 5 hours to under 20 minutes per 32768x32768 image through few-step distillation and efficient attention, and to extend text-to-image synthesis from 8192x8192 regions to entire slides with a pretrained language model.
Speaker: Srikar Yellapragada
Location: NCS 220
In this thesis, we build generative foundation models for these domains, scaling from small patches to gigapixel images without patch-level annotations. We begin with PathLDM, which conditions histopathology patch generation on
slide-level pathology reports, enabling the first text-to-image generation in histopathology. Since reports do not describe individual patches, we propose LRDM, which uses embeddings from pretrained self-supervised learning (SSL) encoders as patch-level conditioning for both histopathology and satellite images. ZoomLDM extends SSL conditioning across eight magnifications with a single model, capturing structure from individual cells to tissue architecture. To scale across cancer types, we present PixCell, the first generative foundation model for histopathology, trained on 30.8 million patches from 69,184 WSIs across 28 organ types. Synthetic images from PixCell can stand in for real data in SSL training, enable privacy-preserving data sharing, and improve downstream tile-level classifiers. Extending this to gigapixel scale, we introduce BigCell, which generates coherent WSIs of up to 32768x32768 pixels by guiding a 2048x2048 flow-matching transformer with a grid of foundation model embeddings, and speeds up generation by over 3x with an importance-based schedule. BigCell enables text-to-image synthesis at 8192x8192 resolution, with synthetic samples improving slide-level few-shot classification by up to 18%. Beyond pathology, our SSL-conditioned models serve as data generators for self-supervised learning on natural images. Finally, to make gigapixel synthesis practical, we plan to reduce BigCell's generation time from about 5 hours to under 20 minutes per 32768x32768 image through few-step distillation and efficient attention, and to extend text-to-image synthesis from 8192x8192 regions to entire slides with a pretrained language model.
Speaker: Srikar Yellapragada
Location: NCS 220
Join the Collaborative for the Earth, the Department of Political Science, and the AI Innovation Institute as we welcome Lav Varshney, Ph.D., Della Pietra Infinity Professor and Inaugural Director of the AI Innovation Institute.
The Physical Economy of Intelligence: Marketcraft, Data Centers, and Climate
Artificial intelligence is often described as immaterial, yet it is a rapidly growing physical industry built from memory chips, servers, concrete, land, electricity, water, and capital. This talk develops a marketcraft perspective on AI and climate, treating memory, compute, and intelligence as interdependent goods shaped by supply constraints, demand shocks, long investment cycles, environmental externalities, and the design of markets and institutions. This framing helps explain why sustainable AI cannot be achieved through more efficient algorithms alone: it also requires better lifecycle accounting, procurement standards, flexible computing, and contracts that shift computational activity toward environmentally favorable times and places. The talk will connect these system-level ideas to work on generative design and experimental validation of low-carbon concrete for data-center construction, as well as AI-enabled optimization of wastewater operations that have each reduced energy/carbon by 30 percent or more. Together, these examples show how the co-design of algorithms, physical infrastructure, markets, and public policy can reduce AI's environmental footprint while using AI to accelerate climate solutions.
Date & Time: Wednesday, September 23, 2026 | 12:30 PM - 1:50 PM
Location: Laufer Center
Registration is compulsory
For any questions and accessibility requests or accommodations, please contact Jennifer Gilday at c4e@stonybrook.edu or call 631-632-4625.
Visual Analytics and Machine Learning for Biomedical Imaging Diagnosis
Arie Kaufman
We present an integrated approach using visual analytics and machine learning (ML) to diagnose abnormalities in 3D radiological imaging and biological microscopes. The primary example will involve 3D virtual pancreatography (VP), a novel visualization-ML procedure and application for non-invasive diagnosis and classification of pancreatic lesions, the precursors of pancreatic cancer. Currently, non-invasive screening of patients is performed through visual inspection of 2D axis-aligned CT images, though the relevant features are often not clearly visible nor automatically detected. VP is an end-to-end visual diagnosis system that includes an ML-based automatic segmentation of the pancreatic gland and the lesions, a semi-automatic approach to extract the primary pancreatic duct, an ML-based automatic classification of lesions into four prominent types, and specialized 3D and 2D exploratory visualizations of the pancreas, lesions and surrounding anatomy. We combine volume rendering with pancreas- and lesion-centric visualizations and measurements for effective diagnosis. We designed VP through close collaboration and feedback from expert radiologists, and evaluated it on multiple real-world CT datasets with various pancreatic lesions and case studies examined by the expert radiologists. Other applications include virtual colonoscopy, COVID-19, pathology, brain neurites, etc.
Biography: Arie Kaufman is Distinguished Professor and formerChair of the Department of Computer Science at Stony Brook University, where he is also Director of the Center for Visual Computing (CVC), and Chief Scientist at the Center of Excellence in Wireless and Information Technology (CEWIT).
He received his PhD in Computer Science at Ben-Gurion University of the Negev in 1977. He is known for his work in visualization, graphics, virtual reality, user interfaces, multimedia, and their applications, especially in bio-medicine. He is especially well known for his work on the 3-dimensional virtual colonoscopy, a revolutionary low-risk technique for colon cancer screening, and for pioneering the use of Graphics Processing Units (GPUs) and GPU-clusters. In 2012, he presided over the development and opening of the Reality Deck, the largest virtual reality display in the world, at Stony Brook University.
Kaufman was the founding Editor in Chief of IEEE Transactions on Visualization and Computer Graphics (TVCG), co-founded the IEEE Visualization Conference and Volume Graphics series, and is currently the director of IEEE Computer Society Technical Committee on Visualization and Graphics. He is an IEEE Fellow, ACM Fellow, winner of many awards, including the IEEE Visualization Career Award, and member of the European Academy of Sciences.
Steven Skiena is inviting you to a scheduled Zoom meeting.
Topic: AI Seminar: Arie Kaufman
Time: Apr 21, 2021 10:00 AM Eastern Time (US and Canada)
Join Zoom Meeting
https://stonybrook.zoom.us/j/