Join us at AI Campus Connect for an exciting day filled with all things artificial intelligence. Whether you're an AI pro or just starting out, this event is perfect for anyone interested in AI and how it can boost your career. Come network, sign-up for IBM SkillsBuild, and learn about the latest trends in AI technology. Don't miss out on this opportunity to connect with IBM SkillsBuild experts and expand your knowledge in the world of AI. See you there!

Co-hosted With AI Community and SWE

Location: W0550 Frank Melville, Jr. Memorial Library

Register to attend.

Abstract: Pretraining vision encoders with self-supervision (SSL) leads to stronger representations that excel across diverse downstream tasks. One of the key factors enabling self-supervision is extracting multiple views of the same scene to formulate either: 1) View-invariant pretraining (DINO, SimCLR, iBOT), where the objective is predicting the same representation for different views of the scene; or 2) Cross-view pretraining (cross-view Masked Autoencoders), where the objective is predicting missing parts of one view using other views. For extracting multiple views, view-invariant methods rely on a combination of handcrafted augmentations (random cropping, color jittering, gaussian blur, etc.) of the same image, whereas cross-view pretraining methods rely on image cropping or video frames. In this work, we present methods to effectively incorporate synthetic views from diffusion models into SSL training.
For view-invariant pretraining, we introduce Gen-SIS, a method that leverages the ability of diffusion models to generate interpolated images through interpolation in conditioning space. We introduce a disentanglement pretext task: disentangling two source images from an interpolated synthetic image. This disentanglement task, in addition to vanilla single-source generative augmentation for view extraction, improves visual pretraining of various view-invariant methods (DINO, SimCLR, iBOT).
For cross-view pretraining, we introduce CDG-MAE, a novel cross-view masked autoencoder (MAE) based method that uses diverse synthetic views generated from static images via an image-conditioned diffusion model to learn dense correspondences. We present a quantitative method to evaluate the local and global consistency of the generated views to choose the right diffusion model for cross-view pretraining. These generated views exhibit substantial changes in pose and perspective, providing a rich training signal that overcomes the limitations of video (expensive) and crop-based (less variation) methods. CDG-MAE substantially narrows the gap to video-based MAE methods on video label propagation tasks while maintaining the data advantages of image-only MAEs.

Speaker: Varun Belagali

Location: NCS 120
Zoom: https://stonybrook.zoom.us/j/93647452432?pwd=hZaX7LXCAD8KPHWYE1Afw2sDI3owpv.1

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

The Provost's Spotlight Talks feature eminent visitors to the university as well as Stony Brook faculty members who have recently been recognized for outstanding contributions in their field.

Transmedia artist Stephanie Dinkins, Kusama endowed chair in art in the College of Arts and Sciences at Stony Brook University, brings her expertise in AI to the next Spotlight Talk with The Stories We Encode: AI, Love and the Future of Algorithmic Care on Tuesday, October 22, at 3:30 pm in the Charles B. Wang Center Theatre.

Working at the intersection of emerging technologies and social collaboration, Dinkins was named a 2023 TIME 100 Most Influential People in AI. She was recognized for her work with Not the Only One, an ongoing project in which she trained an AI on three generations of Black women to give it cultural roots, a deep history, and a perspective that existing systems do not offer.

The event is free and open to the public, and the discussion will be followed by a reception in the Wang Theatre lobby, hosted by the College of Arts and Sciences for new and promoted faculty.


About the Talk

AI's impact on society necessitates addressing longstanding human rights issues and prejudices. To ensure AI benefits humanity, we must confront institutional biases, rethink our relationship with other beings and emerging technologies, and reconcile ideals with actual power structures. This involves recognizing systemic inequalities, redefining human identity, and equitably distributing resources. AI, if developed and used ethically, offers an opportunity to reimagine a more equitable world for all inhabitants.

How to Do Spectral Learning at Scale for Science and Engineering

Abstract: Spectral decompositions such as singular value decompositions (SVDs) and eigenvalue decompositions (EVDs) are central tools across a vast swath of scientific computing and machine learning, with abundant engineering applications. Yet many modern methods for learning such decompositions in high dimensions struggle with instability, bias, and poor scalability, even when approximation power is not the limiting factor. I argue that these difficulties are not intrinsic to spectral problems, but instead arise from a shared reliance on Rayleigh-quotient-based constrained optimization, which forces explicit orthogonality handling through penalties, normalization, or whitening.
To address these challenges, I present a reformulation based on unconstrained variational objectives that implicitly encode spectral structure, eliminating the need for orthogonalization and ad-hoc regularization. This perspective leads to a conceptually simpler and scalable parametric framework for learning ordered spectral representations via nested optimization. The resulting framework is well matched to diverse settings in science and engineering. As examples, I demonstrate its effectiveness on eigenvalue problems for linear PDEs such as the Schrödinger equation, spectral (Koopman) analysis of nonlinear dynamical systems such as molecular dynamics, and structured representation learning with deep neural nets. Collectively, these examples illustrate how abandoning Rayleigh-quotient-based formulations resolves long-standing optimization pathologies across domains.

Bio: Jongha (Jon) Ryu is a postdoctoral associate at MIT EECS. He received his Ph.D. in Electrical and Computer Engineering from UC San Diego. His research develops statistical and mathematical foundations for scientific machine learning, with a focus on scalable spectral methods, efficient generative modeling, and reliable uncertainty quantification for scientific and engineering systems.

Location: NCS 120
The Future of Learning: Rethinking Practice in a Changing World

Thursday, March 26, 2026 (Workshops)
Friday, March 27, 2026 (Symposium)

Open to Stony Brook University Faculty, Staff, and Graduate Students. Hosted by the Center for Excellence in Learning and Teaching, Office of the Provost.

Thursday, March 26, 2026
Workshop: AI Tools and Techniques
  • Open to all faculty & staff
  • Hands-on, exploratory
  • Registration only limited to the size of the room
  • Location: In-person, TBD
  • Time: 10 AM - 12 PM
  • Registration required

Friday, March 27, 2026
Keynote: Teaching and Thinking with AI
  • Faculty, TAs, postdocs, and academic staff
  • In-person on-campus conference venue
  • Location: SAC Balroom
  • Time: 9 AM - 3 PM
  • Registration required

Keynote Speaker: José Antonio Bowen

José Antonio Bowen has been leading innovation and change for over 40 years at Stanford, Georgetown and the University of Southampton (UK), as a dean at Miami University and SMU and as President of Goucher College. Bowen has worked as a musician with Stan Getz, Dave Brubeck, and many others and his symphony was nominated for the Pulitzer Prize in Music (1985).
Bowen holds four degrees from Stanford and has written over 100 scholarly articles and books, including the Cambridge Companion to Conducting (2003), Teaching Naked (2012 and the winner of the Ness Award for Best Book on Higher Education), Teaching Naked Techniques with C. Edward Watson (2017) and Teaching Change: How to Develop Independent Thinkers using Relationships, Resilience and Reflection (Johns Hopkins University Press, 2021).
Bowen has appeared in The New York Times, Forbes, The Wall Street Journal, and has three TED talks. Stanford honored him as a Distinguished Alumni Scholar (2010) and he has presented keynotes and workshops at more than 300 campuses and conferences 46 states and 17 countries around the world. In 2018, he was awarded the Ernest L. Boyer Award (for significant contributions to American higher education). He is a senior fellow for the American Association of Colleges and Universities.

Register here.

You are cordially invited to attend the biweekly Brookhaven AI Mixer (BAM). BAM includes one short talk 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.

Uncertainty-Aware Adaptation of LLMs for Protein-Protein Interaction Analysis

Abstract: Identification of protein-protein interactions (PPIs) helps derive cellular mechanistic understanding, particularly in the context of complex conditions such as neurodegenerative disorders, metabolic syndromes, and cancer. Large Language Models (LLMs) have demonstrated remarkable potential in predicting protein structures and interactions via automated mining of vast biomedical literature; yet their inherent uncertainty remains a key challenge for deriving reproducible findings, critical for biomedical applications. In this study, we present an uncertainty-aware adaptation of LLMs for PPI analysis, leveraging fine-tuned LLaMA-3 and BioMedGPT models. To enhance prediction reliability, we integrate LoRA ensembles and Bayesian LoRA models for uncertainty quantification (UQ), ensuring confidence- calibrated insights into protein behavior. Our approach achieves competitive performance in PPI identification across diverse disease contexts while addressing model uncertainty, thereby enhancing trustworthiness and reproducibility in computational biology. These findings underscore the potential of uncertainty-aware LLM adaptation for advancing precision medicine and biomedical research.

Biography: Sanket is a research staff member in the Applied Mathematics department within the Computing and Data Sciences Directorate at Brookhaven National Laboratory. Previously, he was the Amalie Emmy Noether Postdoctoral Fellow in the same department. He earned his Ph.D. in Statistics from Michigan State University.. Sanket's research interests span Bayesian statistics, uncertainty quantification (UQ), deep learning, Markov Chain Monte Carlo (MCMC), variational inference, and sparsity methods. He also focuses on dimensionality reduction, surrogate modeling, hybrid physical-data driven models, and active learning, with applications across climate science, materials science, and life sciences.

Location: CDS, Bldg. 725, Training Room

Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1605691898?pwd=xC7GebG7Kvzxa4AjPSIxJw7e9IZtoY.1

Meeting ID: 160 569 1898
Passcode: 303888

Kate Armstrong, a Vancouver-based artist, writer, and independent curator, will explore the role of AI in art and creativity through three AI-driven projects: KEKE Terminal, Botto, and Sasha Stiles' AI collaborator Technelegy. She will compare these projects to historical artistic movements and investigate AI's role as an autonomous creative agent, the function of community participation, and the shifting dynamics of authorship.

Location: Humanities Institute Room 1008