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 10 minutes) by multiple people. Students can register for 1 credit for CSE 656. Registered students must attend and present a minimum of 2 or 3 talks. Everyone else is welcome to attend. Fill in https://forms.gle/pCVXovgfMfQwGqG38 to subscribe to our mailing list for further announcement.
University Libraries Presents: The Library AI Club is a welcoming space for students, faculty, and staff to explore AI in a supportive, low-pressure environment. Meeting every two weeks, the club features discussions, collaborative projects, guest speakers, and hands-on experiments. Join us to learn, share ideas, and engage with AI responsibly and creatively. We'd love to see you at an upcoming meeting! Location: Melville Library, Scholarly Communication Seminar Room

The Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

For more information and registration, please visit the official website.

Abstract: Modern decision-making increasingly relies on complex data, imperfect models, and limited domain expertise--yet decisions must still be made with confidence and accountability. This talk presents a research perspective on visual analytics as a bridge between data, models, and human judgment. Through three case studies spanning public-health risk analysis, multivariate scientific visualization, and causal model auditing with large language models, I will show how interactive visualization can reveal structure in high-dimensional data, support reasoning under uncertainty, and help humans critically assess both statistical and AI-generated explanations. Together, these examples illustrate how visual analytics enables users not only to explore data, but to form, challenge, and refine beliefs that underpin scientific and societal decisions.

Bio: Klaus Mueller received his Ph.D. in Computer Science from The Ohio State University in 1998. He is a Professor in the Department of Computer Science at Stony Brook University and a Senior Scientist at the Computational Science Initiative at Brookhaven National Laboratory. He currently serves as the Acting Chair of the Department of Technology and Society at Stony Brook. From 2012 to 2015, he was the Founding Chair of the Computer Science Department at SUNY Korea, where he also served as Vice President for Academic Affairs and Finance for two years.
His research interests span visual analytics, explainable AI, machine learning and data science, human-centered responsible AI, fairness, belief modeling and personalized communication, virtual and augmented reality, and computational and medical imaging. Dr. Mueller received the U.S. National Science Foundation Early Career Award in 2001, the SUNY Chancellor's Award for Excellence in Scholarship and Creative Activity in 2011, and the Meritorious Service Certificate and Golden Core Award of the IEEE Computer Society in 2016. In 2018, he was inducted into the U.S. National Academy of Inventors.
To date, he has authored more than 300 peer-reviewed journal and conference papers, which have been cited over 15,000 times. He is a frequent speaker at international conferences, has organized or participated in 18 tutorials, chaired the IEEE Visualization Conference in 2009, served as elected Chair of the IEEE Technical Committee on Visualization and Computer Graphics (VGTC) from 2012-2015, and was Editor-in-Chief of IEEE Transactions on Visualization and Computer Graphics from 2019-2022. He is a Fellow of the IEEE.

Location: NCS 120

Are you tired of drowning in a sea of resumes and losing top talent in the hiring whirlwind? Transform your hiring process through a different lens and learn about AI in the Workplace and the Applicant Tracking System (ATS). Whether you're a recent graduate seeking your first job or an undergraduate student looking to delve into more career-oriented opportunities, this workshop by SBU Career Center is designed to equip you with the knowledge and strategies needed to succeed.

Register here: https://stonybrook.joinhandshake.com/stu/events/1568133?

CSE 656 Seminars in Computer Vision - Wednesdays 11:30am-12:50pm, Room NCS 120

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 10 minutes) by multiple people. Students can register for 1 credit for CSE656. Registered students must attend and present a minimum of 2 or 3 talks. Everyone else is welcome to attend. Fill in https://forms.gle/pCVXovgfMfQwGqG38 to subscribe to our mailing list for further announcement.

The first meeting will be Wed Jan 29 at 11.30am, room 120 New CS. The meeting will deal with organizational matters and we will start right away with some presentations. Send David Paredes Merino <dparedesmeri@cs.stonybrook.edu> an email if you are interested but cannot attend the first meeting. Please forward to people outside the CS department that you think might be interested.
Abstract: Anxiety disorders are characterized by persistent and excessive form of fear and worry that interferes with daily functioning, distinguishing it from the adaptive anxiety that helps individuals respond to challenges. Despite affecting millions worldwide and costing a significant public health burden, anxiety disorders still remain underdiagnosed than actual prevalence due to lack of understanding and stigmatization. Leveraging machine learning (ML) and natural language processing (NLP) approaches can help bridge this gap by enabling scalable and accessible mental health assessments, offering a data-driven understanding of anxiety from individual and societal perspectives, and shedding light on societal stigmas toward mental health conditions. At the same time, advancing ML and NLP techniques for anxiety research presents unique technical challenges, such as effectively modeling linguistic markers of anxiety and ensuring interpretability in mental health predictions.

This dissertation investigates anxiety from both individual and societal perspectives using artificial intelligence. First, we explore individual manifestations of anxiety through three methodological advancements: (1) integrating contextual and discourse-level embeddings to improve language-based anxiety prediction using Facebook posts and selfreported surveys; (2) enhancing cognitive dissonance detection in Twitter dataset with transfer learning and active learning; and (3) developing longitudinal representation learning approaches that achieve both predictive utility and interpretability of adolescent psychopathology. Finally, we extended our analysis to societal dimension of anxiety by identifying and categorizing social norms expressed in Reddit and Twitter posts and examining their associations with anxiety. By combining data-driven methods with psychological insights, this work studies anxiety from various angles - capturing both individual experiences and societal influences - offering a step toward a more comprehensive understanding of its causes and manifestations.

Speaker: Swanie Juhng

https://stonybrook.zoom.us/j/98905245099?pwd=M7rI7aNfNio281qyebEUdNPBcSiK7Y.1
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

Abstract: Computational pathology has revolutionized cancer diagnosis and research through the analysis of digitized whole slide images (WSIs). However, the giga-pixel size of these images presents profound technical challenges, creating two intertwined bottlenecks: computational inefficiency and label inefficiency. The immense data scale makes standard end-to-end (E2E) training of deep neural networks infeasible due to prohibitive GPU memory requirements, while the reliance on expert pathologists for annotations makes obtaining high-quality labeled data a tedious and expensive process. This proposal confronts these dual challenges by developing a series of novel model architectures, training paradigms, and self-supervised learning methods designed to create a more efficient and effective framework for WSI analysis.

To improve computational efficiency, this proposal first introduces a locally supervised learning paradigm that enables E2E training on entire WSIs by partitioning a network into gradient-isolated modules, circumventing the memory bottleneck of backpropagation. Second, it presents Prompt-MIL, a parameter-efficient fine-tuning framework that reduces the number of trainable parameters, memory consumption, and training time by fine-tuning only few prompts to guide large pre-trained models. Third, this work advances the efficient architecture on WSIs by developing novel State-Space Models (SSMs). It proposes 2DMamba, the first intrinsic Mamba architecture that preserves the crucial 2D spatial structure of images, overcoming the spatial discrepancy inherent in 1D models. Fourth, to address the inefficiency of multi-directional scans in Mamba models, including 2DMamba, it presents Locally Bi-directional Mamba (LBMamba), which introduces a novel, hardware-aware local backward scan that integrates bi-directional scan into a single forward pass, significantly improving throughput performance trade-off. Lastly, it proposes an extension to the LBMamba, warp-level Bi-directional Mamba (WLBMamba) that extends the thread-level bidirectional scan to warp-level bidirectional scan that further improves the throughput performance trade-off.

To improve label efficiency, this proposal proposes a Precise Location-based Matching strategy for self-supervised dense contrastive learning. By allowing a local patch in one augmented view to match multiple overlapping patches in another, creates a more accurate correspondence, leading to superior feature representations for dense prediction tasks like segmentation and detection.

In summary, this proposal presents a holistic investigation into the efficiency bottlenecks in computational pathology. Through these combined contributions in model architecture, training paradigms, and self-supervised learning, this work establishes a more scalable, efficient, and powerful computational framework for analyzing giga-pixel pathology images.

Speaker: Jingwei Zhang

Location: Old Computer Science Room 2114

Zoom: https://stonybrook.zoom.us/j/95187903649?pwd=tV0CNxLu1QKqw7hGmcE1h0rJ2C6n1b.1
Meeting ID: 951 8790 3649 | Passcode: 488916
https://meetings.cshl.edu/meetings.aspx?meet=naisys&year=20  


November 9 - 12, 2020 Virtual
Abstract Deadline: August 14, 2020


Organizers:

Raia Hadsell, DeepMind, United Kingdom
Blake Richards, Mila, Québec AI Institute, Canada
Anthony Zador, Cold Spring Harbor Laboratory

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The current COVID-19 situation is challenging and difficult for all of us - we hope this virtual conference will allow colleagues to share and discuss their latest research, while under travel and stay-at-home restrictions.

Because of the ongoing COVID-19/SARS-CoV-2 outbreak, CSHL and the organizers have now reached the difficult decision to restructure the meeting on From Neuroscience to Artificially Intelligent Systems into a virtual meeting scheduled November 9-12, 2020.  NAISys will begin at 10 am (EDT)  on Monday, November 9 and ending with an afternoon session on Thursday, November 12, 2020. Oral sessions will be confined to later morning and afternoon sessions EST to maximize access by participants from around the world. 

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Artificial intelligence (AI) and neural networks have long drawn on neuroscience for inspiration. However, in spite of tremendous recent advances in AI, natural intelligence is still far more adept at interacting with the real world in real-time, adapting to changes, and doing so under significant physical and energetic constraints. The goal of this meeting is to bring together researchers at the intersection of AI and neuroscience, and to identify insights from neuroscience that can help catalyze the development of next-generation artificial systems.

Abstracts are welcomed on all scientific topics related to how principles and insights from neuroscience can lead to better artificial intelligence. Topics of interest include but are not limited to network architectures, computing with spiking networks, learning algorithms, active perception, inductive bias, meta-learning, and online learning. Please note that abstracts should be ONE page (~2900 characters).   




Keynote speakers (pending reconfirmation):Yoshua Bengio, Université de Montréal
Yann Lecun, NYU/Facebook


Invited Speakers (pending reconfirmation):Kwabena Boahen, Stanford University
Dmitri Chklovskii, Simons Foundation
Anne Churchland, Cold Spring Harbor Laboratory
Claudia Clopath, Imperial College London, UK
Jim DiCarlo, MIT
Chelsea Finn, Stanford University
Surya Ganguli, Stanford University
Jeff Hawkins, Numenta
Konrad Kording, University of Pennsylvania
Timothy Lillicrap, DeepMind
Yael Niv, Princeton University
Bruno Olshausen, UC Berkeley
Cristina Savin, New York University
Terry Sejnowski, Salk Institute for Biological Studies
Sebastian Seung, Princeton University
Eero Simoncelli, New York University
Sara A. Solla, Northwestern University
David Sussillo, Google Brain
Andreas Tolias, Baylor College of Medicine


New and revised abstracts should be submitted by the resubmission deadline, Friday, August 14. Individuals originally selected for talks should assume they will still be speaking, but we may select additional talks based on the number of invited and selected speakers who cannot reconfirm.

Abstracts should contain only new and unpublished material and must be submitted electronically by the abstract deadline. Selection of material for oral and poster presentation will be made by the organizers and individual session chairs. Status (talk/poster) of abstracts will be posted on our web site as soon as decisions have been made by the organizers.

We are eager to have as many students and postdocs as possible to attend since they are likely to benefit most from this meeting. We have applied for funds from industry and foundations to partially support graduate students and postdocs. Apply in writing stating need for financial support to Catie Carr at carr@cshl.edu. Preference is given to those submitting abstracts. 

All questions pertaining to registration, fees, abstract submission or any other matters may be directed to Catie Carr at carr@cshl.edu.

We anticipate the following support :

National Science Foundation

Social Media:

The designated hashtag for this meeting is #cshlNeuroAI. Note that you must obtain permission from an individual presenter before live-tweeting or discussing his/her talk, poster, or research results on social media. Click the Policies tab above to see our full Confidentiality & Reporting Policy.


Pricing:

Virtual Academic Package: $285
Virtual Graduate Student Package: $175
Virtual Corporate Package: $425

Lab Group Discounts (not departmental or institutional discounts):

Labs registering 4 people: 20% discount off applicable fees
Labs registering 5 people: 25% discount off applicable fees
Labs registering 6 people: 30% discount off applicable fees

To be eligible for lab group discounts, please submit a list of lab members planning to attend in advance of registration to Catie Carr  to establish appropriate discounted fees. Please include a link to your lab web page for verification purposes. Prior payments will be included in the group discount calculation.

IBRO/International Brain Research Organization are generously supporting the participation of a limited number of individuals from US/Canadian Minority Serving Institutions (check eligibility): $25
Limit: 65 attendees / limit per institution: 5 (contact Catie Carr  to confirm eligibility/availability prior to registering) 

Reduced Pricing for Individuals from US/Canadian Minority Serving Institutions (check eligibility): $50