Please join us for the next CSE 600 Seminar this Friday, October 11th, at 2:30pm in New Computer Science 120 given by Assistant Professor Mohammad Javad Amiri.
Abstract:
Today's distributed transaction processing systems must deal with untrustworthy environments where multiple mutually distrustful entities communicate with each other, and maintain data on untrusted infrastructure. Byzantine Fault-Tolerant (BFT) protocols have recently been extensively used by distributed transaction processing systems to establish consensus on the order of transactions. However, the proliferation of different BFT protocols has made it difficult to navigate the BFT landscape, let alone determine the protocol that best meets application needs. Moreover, as novel smart contracts, modern hardware, and new cloud platforms arise, future-proof distributed transaction processing systems need to be designed with full-stack adaptivity in mind. This talk presents our vision for a reinforcement learning (RL)-based distributed transaction processing system that adjusts effectively in real-time to changing fault scenarios and workloads.
Abstract: Formalization of mathematics is the process by which pen-and-paper mathematics is translated into a strict chain of logical deductions down to the axioms of mathematics. The subject has seen renewed interest in the last decades thanks to the development of computer systems called proof assistants, which make this feasible in practice.
There have now been several examples of high-profile mathematical results which have been formalized. In principle, any mathematical domain is accessible. However, existing projects are skewed towards algebra instead of analysis. Notable exceptions are a project which formalized enough of Gromov's convex integration theory to deduce Smale's sphere eversion theorem and the ongoing project to formalize Carleson's convergence theorem for Fourier series.
This workshop will bring together formalization experts and interested mathematicians to give a new impulse to formalization of analysis (in a very broad sense), and to develop abstractions and tools to deduplicate effort.
Application Information: ICERM welcomes applications from faculty, postdocs, graduate students, industry scientists, and other researchers who wish to participate. Some funding may be available for travel and lodging. Graduate students who apply must have their advisor submit a statement of support in order to be considered.
There have now been several examples of high-profile mathematical results which have been formalized. In principle, any mathematical domain is accessible. However, existing projects are skewed towards algebra instead of analysis. Notable exceptions are a project which formalized enough of Gromov's convex integration theory to deduce Smale's sphere eversion theorem and the ongoing project to formalize Carleson's convergence theorem for Fourier series.
This workshop will bring together formalization experts and interested mathematicians to give a new impulse to formalization of analysis (in a very broad sense), and to develop abstractions and tools to deduplicate effort.
Application Information: ICERM welcomes applications from faculty, postdocs, graduate students, industry scientists, and other researchers who wish to participate. Some funding may be available for travel and lodging. Graduate students who apply must have their advisor submit a statement of support in order to be considered.
The deadline to apply for this workshop is January 24, 2026.
Presented by the Stony Brook University Hospital Institutional Ethics Committee and co-sponsored by the Center for Medical Humanities, Compassionate Care and Bioethics, Stony Brook University
9th Annual Medical Ethics Symposium
Friday, August 7, 2026: MART Auditorium live and via Webinar
Artificial intelligence (AI) is transforming healthcare by improving diagnosis, streamlining administrative tasks, supporting personalized treatment plans, and aiding medical research. However, the growing use of AI in medicine also raises important ethical concerns. While AI has the potential to enhance patient care and efficiency, healthcare providers and patients must approach these technologies with caution and critical oversight.
This event invites professionals and students from all healthcare, legal, and other associated disciplines to bring their work, challenges, and solutions to the table at 'Trust but Verify: Ethical Challenges of AI in Healthcare.'
Agenda:
8am Registration/Light Breakfast
Provided by The Center for Medical Humanities, Compassionate Care and Bioethics
8:30am Welcome
Jean Mueller, MPS, BS, RN, CPHQ Ethics Symposium Coordinator
Opening Remarks
Carolyn Santora, MS, RN, NEA-BC, CPHQ, Chief Nursing Officer; Chief Nursing Officer; Chief of Regulatory Affairs, Patient Safety and Ethics, Stony Brook University Hospital; Chair/Institutional Ethics Committee, Stony Brook Medicine
KEYNOTE
9- 10:30am Trust, Communication, and Consent in AI-Mediated Healthcare
Kellie Owens, PhD
Assistant Professor, Medical Ethics, Department of Population Health, NYU Grossman School of Medicine
KEYNOTE
10:45- 12:15pm AI That Measures, AI that Speaks: A Decade of Surgical AI and the Problem of Verification
Alexander Winkler- Schwartz, MDCM, PhD, FRCSC, FAANS
Neurosurgeon, Neuroscientist, AI and Surgical Education Researcher
Assistant Professor, Adult and Pediatric Neurosurgery, Stony Brook University
12:30- 1:15pm Lunch
Provided by the Stony Brook University Hospital Institutional Ethics Committee
Panel Discussion and Interactive Ethics Case Presentations
1:30- 3pm Healthcare AI: Designed to Assist, Not Resist Human Judgment
Moderator: David N. Hoffman, JD
Distinguished Panelists
Leah Gancz, MD
Julie Luengas, DNP, MBA, RN, NI-BC, FHIMSS
Tauhid Mahmud, MD, MPH
Pons Materum III, MD
Neil J. Patel, MD, MBA, MS
Carolyn Santora, RN, NEA-BC, CPHQ
Caitlyn Tabor, JD, MBE
Mathew Tharakan, MD, MBA
Alexander Winkler-Schwartz, MDCM, PhD, FRCSC, FAANS
Zhi Wu, MD
3- 3:45pm Poster Awards and Rapid-Fire Sessions
Carolyn Santora, MS, RN, NEA-BC, CPHQ
1st, 2nd and 3rd Place Posters and Colleagues Choice Award
3:45- 4pm Summary and Closing Remarks
Carolyn Santora, MS, RN, NEA-BC, CPHQ
Register here.
9th Annual Medical Ethics Symposium
Friday, August 7, 2026: MART Auditorium live and via Webinar
Artificial intelligence (AI) is transforming healthcare by improving diagnosis, streamlining administrative tasks, supporting personalized treatment plans, and aiding medical research. However, the growing use of AI in medicine also raises important ethical concerns. While AI has the potential to enhance patient care and efficiency, healthcare providers and patients must approach these technologies with caution and critical oversight.
This event invites professionals and students from all healthcare, legal, and other associated disciplines to bring their work, challenges, and solutions to the table at 'Trust but Verify: Ethical Challenges of AI in Healthcare.'
Agenda:
8am Registration/Light Breakfast
Provided by The Center for Medical Humanities, Compassionate Care and Bioethics
8:30am Welcome
Jean Mueller, MPS, BS, RN, CPHQ Ethics Symposium Coordinator
Opening Remarks
Carolyn Santora, MS, RN, NEA-BC, CPHQ, Chief Nursing Officer; Chief Nursing Officer; Chief of Regulatory Affairs, Patient Safety and Ethics, Stony Brook University Hospital; Chair/Institutional Ethics Committee, Stony Brook Medicine
KEYNOTE
9- 10:30am Trust, Communication, and Consent in AI-Mediated Healthcare
Kellie Owens, PhD
Assistant Professor, Medical Ethics, Department of Population Health, NYU Grossman School of Medicine
KEYNOTE
10:45- 12:15pm AI That Measures, AI that Speaks: A Decade of Surgical AI and the Problem of Verification
Alexander Winkler- Schwartz, MDCM, PhD, FRCSC, FAANS
Neurosurgeon, Neuroscientist, AI and Surgical Education Researcher
Assistant Professor, Adult and Pediatric Neurosurgery, Stony Brook University
12:30- 1:15pm Lunch
Provided by the Stony Brook University Hospital Institutional Ethics Committee
Panel Discussion and Interactive Ethics Case Presentations
1:30- 3pm Healthcare AI: Designed to Assist, Not Resist Human Judgment
Moderator: David N. Hoffman, JD
Distinguished Panelists
Leah Gancz, MD
Julie Luengas, DNP, MBA, RN, NI-BC, FHIMSS
Tauhid Mahmud, MD, MPH
Pons Materum III, MD
Neil J. Patel, MD, MBA, MS
Carolyn Santora, RN, NEA-BC, CPHQ
Caitlyn Tabor, JD, MBE
Mathew Tharakan, MD, MBA
Alexander Winkler-Schwartz, MDCM, PhD, FRCSC, FAANS
Zhi Wu, MD
3- 3:45pm Poster Awards and Rapid-Fire Sessions
Carolyn Santora, MS, RN, NEA-BC, CPHQ
1st, 2nd and 3rd Place Posters and Colleagues Choice Award
3:45- 4pm Summary and Closing Remarks
Carolyn Santora, MS, RN, NEA-BC, CPHQ
Register here.
The Tiger Team open house will be Monday, September 20 at high noon by Zoom:
https://stonybrook.zoom.us/j/
Join us at the Center for Excellence in Learning and Teaching (CELT) for an interactive Zoom workshop on Generative AI designed for faculty and staff interested in enhancing teaching and assessment practices, increasing student engagement, and navigating the rapidly evolving landscape of AI tools. Participants will be introduced to common AI tools, explore potential instructional uses, and discuss key considerations such as academic integrity, transparency, and equity.
Register now: https://stonybrook.zoom.us/meeting/register/6js1eP64T1ys8tyU57EJ7Q#/registration
Register now: https://stonybrook.zoom.us/meeting/register/6js1eP64T1ys8tyU57EJ7Q#/registration
The Department of AI and Society (AIS) at the University at Buffalo is hosting a two-day AI and Society Workshop focused on building AI systems by society, for society. This workshop brings together researchers and community organizers to explore how AI systems can be developed through meaningful collaboration across disciplines.
Topics include:
- Labor and AI
- Public services and AI
- Community-centered AI systems
- Intersections of humanities, social sciences, arts, and computing
The vision of UB's Department of AI and Society is to create a future where AI systems are built by society, for society. AIS centers community engagement at every stage of AI development through collaboration across disciplines and sectors. AIS was established with a $5 million grant from SUNY, and this workshop is made possible through that support.
Who Should Attend?
- Researchers
- Students
- Community organizers
- Practitioners interested in AI's societal impact
Predictable Autonomy for Cyber-Physical Systems by Stanley Bak, Safe Sky Analytics
ABSTRACT: Cyber-physical systems combine complex physics with complex software. Although these systems offer significant potential in fields such as smart grid design, autonomous robotics and medical systems, verification of CPS designs remains challenging. Model-based design permits simulations to be used to explore potential system behaviors, but individual simulations do not provide full coverage of what the system can do. In particular, simulations cannot guarantee the absence of unsafe behaviors, which is unsettling as many CPS are safety-critical systems.
The goal of set-based analysis methods is to explore a system's behaviors using sets of states, rather than individual states. The usual downside of this approach is that set-based analysis methods are limited in scalability, working only for very small models. This talk describes our recent process on improving the scalability of set-based reachability computation for LTI hybrid automaton models, some of which can apply to very large systems (up to one billion continuous state variables!). Lastly, we'll discuss the significant overlap of techniques used for our scalable reachability analysis methods with set-based input/output analysis of neural networks.
BIO: Stanley Bak is a computer scientist investigating the predictable design of autonomous cyber-physical systems. He strives to develop practical formal methods that are both scalable and useful, which demands developing new theory, programming efficient tools and building experimental systems. He received a Bachelor's degree in Computer Science from Rensselaer Polytechnic Institute (RPI) in 2007 (summa cum laude), and a Master's degree in Computer Science from the University of Illinois at Urbana-Champaign (UIUC) in 2009. He completed his PhD from the Department of Computer Science at UIUC in 2013. He received the Founders Award of Excellence for his undergraduate research at RPI in 2004, the Debra and Ira Cohen Graduate Fellowship from UIUC twice, in 2008 and 2009, and was awarded the Science, Mathematics and Research for Transformation (SMART) Scholarship from 2009 to 2013. From 2013 to 2018, Stanley was a Research Computer Scientist at the US Air Force Research Lab (AFRL), both in the Information Directorate in Rome, NY, and in the Aerospace Systems Directorate in Dayton, OH. He currently helps run Safe Sky Analytics, a research consulting company investigating verification and autonomous systems, and performs teaching as an Adjunct Professor at Georgetown University.
ABSTRACT: Cyber-physical systems combine complex physics with complex software. Although these systems offer significant potential in fields such as smart grid design, autonomous robotics and medical systems, verification of CPS designs remains challenging. Model-based design permits simulations to be used to explore potential system behaviors, but individual simulations do not provide full coverage of what the system can do. In particular, simulations cannot guarantee the absence of unsafe behaviors, which is unsettling as many CPS are safety-critical systems.
The goal of set-based analysis methods is to explore a system's behaviors using sets of states, rather than individual states. The usual downside of this approach is that set-based analysis methods are limited in scalability, working only for very small models. This talk describes our recent process on improving the scalability of set-based reachability computation for LTI hybrid automaton models, some of which can apply to very large systems (up to one billion continuous state variables!). Lastly, we'll discuss the significant overlap of techniques used for our scalable reachability analysis methods with set-based input/output analysis of neural networks.
BIO: Stanley Bak is a computer scientist investigating the predictable design of autonomous cyber-physical systems. He strives to develop practical formal methods that are both scalable and useful, which demands developing new theory, programming efficient tools and building experimental systems. He received a Bachelor's degree in Computer Science from Rensselaer Polytechnic Institute (RPI) in 2007 (summa cum laude), and a Master's degree in Computer Science from the University of Illinois at Urbana-Champaign (UIUC) in 2009. He completed his PhD from the Department of Computer Science at UIUC in 2013. He received the Founders Award of Excellence for his undergraduate research at RPI in 2004, the Debra and Ira Cohen Graduate Fellowship from UIUC twice, in 2008 and 2009, and was awarded the Science, Mathematics and Research for Transformation (SMART) Scholarship from 2009 to 2013. From 2013 to 2018, Stanley was a Research Computer Scientist at the US Air Force Research Lab (AFRL), both in the Information Directorate in Rome, NY, and in the Aerospace Systems Directorate in Dayton, OH. He currently helps run Safe Sky Analytics, a research consulting company investigating verification and autonomous systems, and performs teaching as an Adjunct Professor at Georgetown University.
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.
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.
The Della Pietra Lecture Series is pleased to present this lecture by Scott Aaronson:
Abstract: I'll survey some areas where I think theoretical computer science, math, and statistics can potentially contribute to the urgent quest to align powerful AI with humane values. These areas include: the watermarking of AI outputs, mechanistic interpretability (including Paul Christiano's No-Coincidence Principle, and succinct digests of the training process to aid interpretability), and theoretical guarantees for out-of-distribution generalization.
Speaker: Scott Aaronson is Schlumberger Chair of Computer Science at the University of Texas at Austin, and founding director of its Quantum Information Center. He received his bachelor's from Cornell University and his PhD from UC Berkeley. Aaronson's research has focused mainly on the capabilities and limits of quantum computers. His first book, Quantum Computing Since Democritus, was published in 2013 by Cambridge University Press. He received the National Science Foundation's Alan T. Waterman Award, the United States PECASE Award, the Tomassoni-Chisesi Prize in Physics, and the ACM Prize in Computing, and is a Fellow of the ACM and the AAAS and a member of the National Academy of Sciences. He blogs at Shtetl-Optimized, https://www.scottaaronson.com/blog.
Location: Della Pietra Family Auditorium (SCGP 103)