DeepMath Conference on the Mathematical Theory of Deep Neural Networks
Recent advances in deep neural networks (DNNs), combined with open, easily-accessible implementations, have made DNNs a powerful, versatile method used widely in both machine learning and neuroscience. These advances in practical results, however, have far outpaced a formal understanding of these networks and their training. The dearth of rigorous analysis for these techniques limits their usefulness in addressing scientific questions and, more broadly, hinders systematic design of the next generation of networks. Recently, long-past-due theoretical results have begun to emerge from researchers in a number of fields. The purpose of this conference is to give visibility to these results, and those that will follow in their wake, to shed light on the properties of large, adaptive, distributed learning architectures, and to revolutionize our understanding of these systems.
Learn about how to ask AI question of Notebook LM and the new interactive features.
Notebook LM allows you to interact with multiple sources of information. You can chat or ask follow up questions. Learn how you can gain more insights through these interactions.
Register here.
Notebook LM allows you to interact with multiple sources of information. You can chat or ask follow up questions. Learn how you can gain more insights through these interactions.
Register here.
Join us in Meeting Room 2 of the Empire State Plaza at 11:30 am for an engaging SUNY-led panel on AI for the Public Good, moderated by Assemblymember Nily Rozic.
The AI Innovation Institute cordially invites you to the Summer Symposium this Friday, July 31st, from 10:00 AM to 12:00 PM in the Stony Brook Union Ballroom.
Poster Presentations: 10:00 - 11:30 am - Union Ballroom
Closing remarks: (11:25) Dr. Carl Lejuez, Executive Vice President & Provost
Group Photos (11:30-12)
Featuring undergraduate researchers/participants of the following:
- AI Innovation & Diffusion Research Experience for Undergraduates (REU)
- Explorations in STEM
- Frances Velay Fellowship Program
- Oncology Research and Clinical Learning Experience (ORACLE)
- SUNY EOP
- SUNY SOAR
- URECA REACT/Research Entry Accelerator for College Transfers
- URECA Summer
Join us in celebrating the hard work and accomplishments of our students.
We will demonstrate how to make a personalized AI partner that can do certain tasks for you. This step by step process can be used to develop your own companion.
Understand what is a Gemini Gems and how it can be personalized for your custom needs. We will show you how to capture the tasks that you would like to complete and give you higher quality responses that will ensure your communication and tasks are completed with ease.
Register here.
An interactive session to discover how to create ALT text tags from images and create high-impact visuals, from identification to communicating ideas with images.
Discover how to use AI to create ALT text from images as well as identify objects in your environment, and build relatable visuals for high-impact presentations. Images communicate ideas as a way to understand concepts. AI-generated images have helped allow anyone to create these.
In this session, you will
- Creating image ALT Tags
- Transform ideas into images that are visually appealing
- Identify objects from visuals
Register here.
Learn how these two AI tools will help you this year.
AI has been all over, but figuring out the tools that we may use is critical. Background remover of images and a replacement for Google Search may disrupt the industry this year. Learn and refresh your knowledge about these tools.
NLP Reading Group | Fall 2025
The Natural Language Processing Reading Group at Stony Brook University meets weekly to discuss recent research papers in NLP and related fields.
Join the Google Group here.
The Natural Language Processing Reading Group at Stony Brook University meets weekly to discuss recent research papers in NLP and related fields.
Join the Google Group here.
Join the Department of Biomedical Informatics for an exclusive fall semester programming bootcamp. Discover essential programming, data analytics, and machine learning skills crucial for biomedical informatics. Special topics such as Bioinformatics and NLP will be briefly covered.
Gain hands-on programming experience and discover diverse career opportunities in biomedical informatics.
Don't miss this chance to excel in healthcare data analytics and shape the future of the industry.
https://bmi.stonybrookmedicine.edu/Bootcamp/Bootcamp-Fall-2025
Location: NCS 120
Abstract: Computational pathology has revolutionized cancer diagnosis and research through the analysis of digitized whole slide images (WSIs). However, their giga-pixel size creates two intertwined bottlenecks: computational inefficiency, as prohibitive GPU memory makes standard end-to-end (E2E) training infeasible, and label inefficiency, as expert annotation is tedious and expensive. This dissertation confronts both challenges through novel architectures, training paradigms, and self-supervised learning methods for efficient WSI analysis.
To improve computational efficiency, this dissertation 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 training only a few prompts to guide large pre-trained models, reducing trainable parameters, memory, and training time. Third, this work proposes 2DMamba, the first intrinsic Mamba architecture that preserves the crucial 2D spatial structure of images, overcoming the spatial discrepancy in 1D models. Fourth, it presents Locally Bi-directional Mamba (LBMamba), whose hardware-aware local backward scan integrates bi-directional scanning into a single forward pass, improving the throughput-performance trade-off of Mamba models.
To improve label efficiency, this dissertation proposes a precise location based matching strategy for self-supervised dense contrastive learning, which allows a local patch in one augmented view to match multiple overlapping patches in another, producing more accurate correspondences and superior features for dense prediction tasks like segmentation and detection. Additionally, to better scale multi-channel cell imaging modalities, this dissertation introduces ChannelSFormer, a channel-agnostic vision transformer that disentangles spatial and channel-wise reasoning through divided attention and channel class token, enabling effective representation learning across variable channel configurations in both self-supervised and supervised settings.
In summary, this dissertation 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: NCS 220
Zoom: https://stonybrook.zoom.us/j/93175806292?pwd=xbtxnQyYGoThz5B1DyJxJxPF9lxiJE.1
Meeting ID: 931 7580 6292
Passcode: 314091
To improve computational efficiency, this dissertation 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 training only a few prompts to guide large pre-trained models, reducing trainable parameters, memory, and training time. Third, this work proposes 2DMamba, the first intrinsic Mamba architecture that preserves the crucial 2D spatial structure of images, overcoming the spatial discrepancy in 1D models. Fourth, it presents Locally Bi-directional Mamba (LBMamba), whose hardware-aware local backward scan integrates bi-directional scanning into a single forward pass, improving the throughput-performance trade-off of Mamba models.
To improve label efficiency, this dissertation proposes a precise location based matching strategy for self-supervised dense contrastive learning, which allows a local patch in one augmented view to match multiple overlapping patches in another, producing more accurate correspondences and superior features for dense prediction tasks like segmentation and detection. Additionally, to better scale multi-channel cell imaging modalities, this dissertation introduces ChannelSFormer, a channel-agnostic vision transformer that disentangles spatial and channel-wise reasoning through divided attention and channel class token, enabling effective representation learning across variable channel configurations in both self-supervised and supervised settings.
In summary, this dissertation 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: NCS 220
Zoom: https://stonybrook.zoom.us/j/93175806292?pwd=xbtxnQyYGoThz5B1DyJxJxPF9lxiJE.1
Meeting ID: 931 7580 6292
Passcode: 314091