Presented by Stony Brook University Department of Biomedical Informatics and Long Island Network for Clinical and Translational Science (LINCATS).

The seminar aims to empower participants with the knowledge and skills necessary to harness AI effectively in clinical practice and research. It will equip attendees with practical insights, case studies, and interactive discussions led by experts in both AI and medicine, fostering a collaborative environment where attendee can explore how to overcome barriers and maximize the potential of AI in transforming modern healthcare delivery.

All Stony Brook Audiences Welcome.
Please note: This exciting event is open to all Stony Brook Faculty/Staff/Students. While the overarching theme for this event is the application of AI in medicine, the event is designed to bridge the professional practice gap that exists between cutting-edge AI research and its practical implementation in clinical settings, While AI holds immense promise for transforming healthcare delivery, many physicians and researchers lack the foundational knowledge and practical skills needed to effectively integrate AI into their daily practices.

THIS CONFERENCE IS FOR STONY BROOK UNIVERSITY & HOSPITAL FACULTY/STAFF & STUDENTS ONLY.


Registration link: https://cme.stonybrookmedicine.edu/continuing-medical-education/conferences/235/bench-to-bedside-understanding-the-practical-application-of-ai-in-medicine-2024/10/17/2024

FOR QUESTIONS
joseph.cesaria@stonybrookmedicine.edu
mary.saltz@stonybookmedicine.edu
Abstract: Modern language agents often need to solve tasks requiring long-horizon, multi-turn interactions, where they retrieve external information, adapt to observations, and answer interdependent queries. Yet, most LLM systems rely on full-context prompting, appending all past turns regardless of their relevance. This leads to un-bounded memory growth, increased computational costs, and degraded reasoning performance on out-of-distribution input lengths due to LLM forgetting the context. We introduce MEM1, an end-to-end reinforcement learning framework that enables agents to operate with constant context size when solving long multi-turn tasks. At each turn, MEM1 updates a compact shared internal state that jointly supports memory consolidation and reasoning. Leveraging reinforcement learning (RL) and rollout trajectory truncation, we train a MEM1 agent to develop internal states that integrate prior memory with new observations from the environment while strategically discarding irrelevant or redundant information. Experiments across three domains, including internal retrieval QA, open-domain web QA, and multi-turn web shopping, show that MEM1-7B improves performance by 3.5x while reducing memory usage by 3.7x compared to Qwen2.5-14B-Instruct on an augmented multi-hop QA dataset with 16 objectives in each task, and generalizes beyond the training horizon. Our results demonstrate the promise of reasoning-driven memory consolidation as a scalable alternative to existing solutions for training long-horizon task-solving agents that involve multiple interactions, where both efficiency and performance are optimized.

Speaker: Yiyang Feng

Location: CS2311
Abstract: In recent years, we have been developing generative AI methods to design increasingly complex objects. Our goal is to improve performance while ensuring that these objects remain controllable. This requires addressing several challenging problems, including:
  • Modeling composite objects in a way that preserves consistency under deformation.
  • Estimating the uncertainty of the surrogate models used to predict performance during optimization.
  • Co-designing objects for both high performance and ease of control.
In this talk, I will present our approach to these challenges and describe end-to-end design pipelines that have the potential to radically transform computer-assisted engineering.

Speaker: Pascal Fua received an engineering degree from Ecole Polytechnique, Paris, in 1984 and a Ph.D. in Computer Science from the University of Orsay in 1989. He joined EPFL (Swiss Federal Institute of Technology) in 1996, where he is a Professor in the School of Computer and Communication Science and head of the Computer Vision Lab. Before that, he worked at SRI International and at INRIA Sophia-Antipolis as a Computer Scientist.
His research interests include shape modeling and motion recovery from images, analysis of microscopy images, and machine learning. He has (co)authored over 400 publications in refereed journals and conferences. He has received several ERC grants. He is an IEEE Fellow and has been an Associate Editor of the IEEE journal Transactions For Pattern Analysis and Machine Intelligence. He often serves as the program committee member, area chair, and program chair of major vision conferences and has cofounded three spinoff companies.

Abstract: The landscape of machine learning evolves rapidly and the complexity of the networks and their architectures defies easy comprehension. AI is touted as the next scientific revolution by allowing the processing and pattern-finding in increasingly massive data sets. One potential end results could be AI enhanced measurement technologies, but what does that mean? This talk will give examples of how classical tools indicate the technical obstacles to this vision in terms of understanding training processes, model comparisons, and feature embeddings. While the results in this talk are largely empirical, they point to interesting directions for (infomation?) theoretical investigation.

Bio: Anand D. Sarwate is an Associate Professor in the Electrical and Computer Engineering Department at Rutgers, The State University of New Jersey. He received B.S. degrees in math and electrical engineering from MIT and a Ph.D. in electrical engineering from UC Berkeley. Prior to joining Rutgers he was a Research Assistant Professor at TTI-Chicago and a postdoc at the ITA Center at UC San Diego. His research interests include information theory, machine learning, signal processing, optimization, and privacy and security.
Location: Light Engineering 250
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
Abstract:
Artificial intelligence (AI)-based methods and computational materials science continue to make inroads into accelerated materials design and development. I will review Al-enabled advances made in the subfield of polymer informatics, with a particular focus on the design of application-specific practical polymeric materials. I will describe exemplar design attempts within a few critical and emerging application spaces, including materials designs for storing, producing, and conserving energy, and those that can prepare us for a sustainable economy powered by recyclable and/or biodegradable polymers. Al- powered workflows help efficiently search the staggeringly large chemical and configurational space of materials, using modern machine-learning (ML) algorithms to solve forward and inverse materials design problems. A practical informatics-based design protocol involves creating a set of application-specific target property criteria, building ML model predictors for those relevant target properties, enumerating or generating a tangible population of viable polymers, and selecting candidates that meet design recommendations. The protocol will be demonstrated for several energy and sustainability-related applications. Finally, I will offer an outlook on the lingering obstacles that must be overcome to achieve widespread adoption of informatics-driven protocols in industrial-scale materials development.

Speaker Bio:
Prof. Ramprasad is the Regents' Entrepreneur, Michael E. Tennenbaum Family Chair and Georgia Research Alliance Eminent Scholar in the School of Materials Science & Engineering at the Georgia Institute of Technology. He is also the CEO and co-founder of Matmerize, Inc. His area of expertise is the development and application of computational and machine learning tools to accelerate sustainable materials development aimed at energy production, storage and utilization. Prof. Ramprasad received his B. Tech. in Metallurgical Engineering at the Indian Institute of Technology, Madras, India, an M.S. degree in Materials Science & Engineering at the Washington State University, and a Ph.D. degree also in Materials Science & Engineering at the University of Illinois, Urbana-Champaign.
Prof. Ramprasad is a Fellow of the Materials Research Society, a Fellow of the American Physical Society, an elected member of the Connecticut Academy of Science and Engineering, and the recipient of the Alexander von Humboldt Fellowship and the Max Planck Society Fellowship for Distinguished Scientists. He has authored or co-authored over 300 peer-reviewed journal articles, 8 book chapters and 8 patents, and has delivered over 300 invited talks at Universities and Conferences worldwide. He is a member of the Editorial Advisory Boards of npj Computational Materials, ACS Materials Letters and Journal of Physical Chemistry A/B/C. He created and chaired the inaugural 2022 Gordon Research Conference on Computational Materials Science and Engineering.

Location: Room 301, Engineering Building
Face Editing with Machine Learning presented by Zhixin Shu

ABSTRACT: The face is the most informative feature of humans and has been a long-standing research topic in Computer Vision and Graphics. Images of faces are also ubiquitous in photography and social media, and people have devoted significant resources to capturing and editing face images. Face editing can be broadly viewed as the encoding, manipulation and the decoding of some representations for face images. The challenges are that we want to manipulate an image in a controllable way and generate results that are both desirable and as realistic as possible. This thesis explores different Machine Learning-based face-editing approaches. I discuss the role of machine learning for achieving desirable edits by learning both the physical aspects as well as the statistical manifold of human faces. In my work for eye-editing, I discuss the importance of understanding multiple physical elements of a face image, such as shape, illumination, pose, etc. In a deep-learning-based approach, I introduce image formation domain knowledge to the construction and training of a neural network. This network provides transparent access to the disentangled representations of the aforementioned physical properties. With this network, we can achieve various face editing tasks in forms of representation manipulation. After that, I introduce Deforming Autoencoders, a network that learns to disentangle shape and appearance in an unsupervised manner. This disentanglement is beneficial for the learning of some other factors of variations, such as illumination and facial expression. In an extension of Deforming Autoencoders, we incorporate non-rigid structure-from-motion to learn a 3D morphable model for faces that only requires an image set for training. At last, I describe an image-to-image network for 3D face reconstruction, which also utilizes structure-from-motion in deep learning. With real face images in training, this network not only reconstructs 3D faces more accurately than prior art but also has better generalization ability in real-life testing cases.
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.
The Provost's Lecture Series features talks by SUNY Distinguished Academy faculty members at Stony Brook University, showcasing the outstanding research and scholarship that is taking place at our institution.

Joe Mitchell

SUNY Distinguished Professor, Applied Mathematics and Statistics
Chair, Department of Applied Mathematics and Statistics, College of Engineering and Applied Sciences

A Case for Algorithms: A Computational Geometer's Perspective

Algorithms are all around us in every smart device and technology that has consumed our daily lives. As a computational geometer, I study algorithms to solve problems that involve a geometric perspective on data. I have observed that practically every technology and field of study has a need for effective algorithms involving geometric data. I reflect on some favorite algorithmic problems that are easy to visualize, but challenging to solve, and argue that the formal study of algorithms remains essential in the age of AI.

Reception to follow immediately after the talks.

Register here.