AI Innovation Institute

The Thirty-Ninth annual Neural Information Processing Systems conference will be held from Dec 2nd to Dec 7th, 2025 at the San Diego Convention Center. More information can be found here.

Join University Libraries for an engaging panel discussion where we delve in and learn about the impacts of artificial intelligence on the 2024 US elections! Panelists are Paige Lord, Tom Costello, and Musa al-Gharbi. The discussion will be moderated by Library Dean, Karim Boughida. Co-sponsored by the Office of Diversity, Inclusion, and Intercultural Initiatives.

Please RSVP for Democracy in the Digital Age: AI's Influence on 2024 Elections here.

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.

Abstract: AI agents are increasingly deployed in complex, interactive environments, yet their runtime remains a major bottleneck for training, evaluation, and real-world use. Typical agent behavior unfolds sequentially, where each action requires an API call that can incur substantial latency. For example, a game of chess between two state-of-the-art agents can take hours. We introduce speculative actions, a lossless acceleration framework for general agentic systems. Inspired by speculative execution in microprocessors and speculative decoding in LLM inference, our method uses faster models to predict likely future actions and executes them in parallel, committing only when predictions match. We evaluate speculative actions across gaming, e-commerce, and web search environments, and additionally study a lossy extension in an operating systems setting. Across domains, we achieve up to 55% next-action prediction accuracy, translating into substantial latency reductions. Finally, we present a cost-latency analysis that formalizes the tradeoff between speculative breadth and time savings. This analysis enables principled tuning and selective branch launching, to ensure multi-branch speculation delivers practical speedups without prohibitive cost growth.

Speaker: Kehan Qi

Location: Old Computer Science Building - CS2311

The North East AI Agents Day Organizing Committee invites you to '2026 AI Agents Day.'

The goal of this workshop is to offer a comprehensive overview of AI agents, bring ML, Systems, and HCI research communities together to share progress, discuss common problems and evaluation setups, and identify opportunities for collaboration. We aim to bring together attendees from diverse disciplines to foster interdisciplinary collaboration and discuss open research questions.

Location: Jane Street Offices, New York

Register here.

Abstract: Many foundation models for digital pathology have been released recently. Benchmarking available methods then becomes paramount to get a clearer view of the research landscape. For this reason, we introduce THUNDER, a tile-level benchmark for digital pathology foundation models, allowing for efficient comparison of many models on diverse datasets with a series of downstream tasks, studying their feature spaces and assessing the robustness and uncertainty of predictions informed by their embeddings. Such foundation models are often used as feature extractors and combined with Multiple Instance Learning (MIL) aggregators at downstream time. Such aggregation must be efficient and reliable. We will focus on two specific examples of this: (I) HistAug, a fast and efficient generative model for controllable augmentations in the latent space of foundation models to perform data augmentation for MIL, and (ii) CAR-MIL, a method based on counterfactual attention regularisation to improve the reliability of attention maps of MIL methods.

Short-bio: Pierre Marza is a Postdoctoral Researcher at CentraleSupelec in the Biomathematics team of the MICS lab, studying Computer Vision and Deep Learning for Medical Imaging, with a focus on Digital Pathology. Prior to this, he was a PhD student at INSA Lyon, in the LIRIS and CITI labs, advised by Christian Wolf, and co-advised by Laetita Matignon and Olivier Simonin. He studied Visual Navigation, Embodied AI, Spatial Reasoning, more specifically how to learn to represent 3D space, generalize to new environments and master diverse tasks from light supervision.

Location: NCS 220

Zoom: https://stonybrook.zoom.us/j/94798224254?pwd=CFraer25qnpORbJ14aAVHRwaSJOjJM.1

CSE 600 Seminar Series | Fall 2025

Abstract: Imagine machines that can see the invisible: drones locating wildfire survivors, cameras predicting building failures, and smartphones detecting skin tumors. These applications lie beyond today's vision systems, which focus only on human-visible information. In this talk, I argue that a wealth of scene information is hidden in light properties invisible to the human eye, such as the travel time of photons and polarization of light waves. I will present how co- designing camera hardware, graphics models, and learning algorithms unlocks these invisible properties to create superhuman vision systems. I will present three superhuman vision capabilities: seeing around blind corners, turning objects into cameras, and extracting internal stress fields. By analyzing faint light reflections on diffuse walls and shiny objects, we create virtual cameras that reveal scenes hidden from the line of sight - enabling autonomous systems to navigate safely. Using the polarization of light, we recover mechanical stress fields hidden inside objects - opening new possibilities for non-destructive material characterization. These capabilities point toward a future where machines can see the invisible: around us, beneath our bodies, and beyond our scientific understanding.

Bio:
Akshat Dave is an Assistant Professor in the Department of Computer Science at Stony Brook
University, USA. His research lies at the intersection of applied optics, computer vision, and
machine learning. His work has been recognized by Rice University's Best Thesis Award, Optica Best Paper Prize, SIGGRAPH Asia Doctoral Consortium, and fellowships by Qualcomm, Texas Instruments, and INK Global Foundation. Prior to Stony Brook, he was a Postdoctoral Associate at MIT Media Lab. He holds a Ph.D. from Rice University and a Masters and a Bachelors from Indian Institute of Technology Madras.

Abstract: Humans perceive the world through global structures such as parts, branches, and their spatial arrangement. Most deep learning models, however, operate mainly at the pixel level. This disconnect between local and global understanding limits interpretability and control. In this thesis, we explore topology as a mathematical framework for bridging local predictions and global structure in dense prediction and generation tasks. We first incorporate topological constraints into semantic segmentation to preserve anatomical relationships and improve multi-class consistency. We next develop structure-level uncertainty estimation, producing more interpretable and actionable measures of model error over branches and connections rather than isolated pixels. Then, we introduce a topology-guided diffusion framework for controllable image generation using structural attributes such as object count and connectivity. Finally, we extend image generation to the longitudinal task, where we aim to capture structural changes across timepoints. All these contributions together establish topology as a unifying interface for building dense prediction models that are structurally aware, interpretable, and controllable.

Speaker: Saumya Gupta

Location: NCS 220

Zoom: https://stonybrook.zoom.us/j/97950688136?pwd=NCa3XOsgIaMIsTVlQBQJ11n27NzL8s.1
Meeting ID: 979 5068 8136
Passcode: 941798

This year's focus is on Machine Learning, Artificial Intelligence, Blockchain and Computational Medicine. For details visit the CEWIT 2019 Conference Website.