Abstract: The development of embodied AI has largely focused on scaling data and computational power, often at the cost of energy efficiency. In contrast, biological intelligence achieves remarkable adaptability with minimal resources, inspiring a shift toward neuromorphic AI, an approach that mimics the structure and dynamics of biological neural systems. In this talk, I will explore the promises and challenges of neuromorphic computer vision from three key perspectives: algorithms, robot actions, and data. First, I will discuss algorithmic advances, including continuous visual hull reconstruction, continuous-time human motion field estimation, and unsupervised independent motion segmentation. Next, I will illustrate how neuromorphic vision enables agile robotic actions by leveraging event-based perception for real-time decision-making. Finally, I will address challenges in training data-driven models with event data, highlighting strategies to enhance data availability and efficiency. By integrating these elements, neuromorphic AI paves the way for energy-efficient, high-performance embodied intelligence in dynamic real-world environments.

Speaker Bio: Ziyun (Claude) Wang is a fifth-year Ph.D. student in the General Robotics, Automation, Sensing & Perception (GRASP) Lab at the University of Pennsylvania, advised by Professor Kostas Daniilidis. His research focuses on developing algorithms for neuromorphic computer vision and integrating them with real hardware to enable agile perception in embodied AI systems. Prior to his Ph.D., he worked at the Samsung AI Center New York, where he developed 3D reconstruction techniques for robotic applications and earned three patents. He also contributed to the Apple Vision Pro team, enhancing user comfort for AR glasses. His research work has been recognized at major computer vision, robotics, and machine learning venues including the AAAI Conference on Artificial Intelligence (AAAI), European Conference on Computer Vision (ECCV), International Conference on Learning Representations (ICLR), Conference on Computer Vision and Pattern Recognition (CVPR) workshops, and IEEE Robotics and Automation Letters (R-AL), with an oral presentation at ECCV placing in the top 2.7%. His research aims to drive the development of next-generation bio-inspired AI systems, enabling more efficient, adaptive, and intelligent embodied perception.

The Association for Computational Linguistics is the international scientific and professional society for people working on problems involving natural language and computation. Membership includes the ACL quarterly journals, Computational Linguistics and Transactions of the ACL, reduced registration at most ACL-sponsored conferences, discounts on ACL-sponsored publications, and participation in ACL Special Interest Groups.

An annual meeting is held each summer in locations where significant computational linguistics research is carried out.

For more information and registration, visit the official website.

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.
Join Stony Brook University's Center for Excellence in Learning and Teaching (CELT) for a bootcamp on how to use AI to enhance your teaching and courses. This event will demonstrate how ChatGPT, Microsoft Copilot, and other generative AI platforms can support you in crafting learning objectives, writing exam questions, composing rubrics, and designing course content such as lesson plans, in-class activities, instructional videos, and more.

Register here.
Tired of drowning in grading? Discover how Gradescope can reduce grading time by 30%-50% for exams, homework, and online assignments. In this 90-minute session, learn to grade efficiently with AI-assisted tools, update rubrics easily, manage paperless submissions, and use analytics to identify learning gaps.

This session will go over:

  • Grade items simultaneously: Use AI-assisted grading to group identical student answers and grade them all at once.

  • Edit rubrics on the fly: Adjust your rubric points at any time, and let Gradescope automatically update previously graded papers retroactively.

  • Go paperless: Effortlessly manage student-submitted PDFs or instructor-scanned exams without the clutter.

  • Spot learning gaps: Use instant analytics dashboards to see exactly which concepts your students mastered and where they struggled.


Register here by 09/13 to attend the Zoom training session.
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.

IdeaLens: Detecting AI Ideas in Long-form Writing

Abstract: While modern AI detectors identify who wrote the words, emerging policies on AI use increasingly hinge on a different question: who came up with the ideas? We introduce IdeaLens, a detector that identifies whether a document's ideas came from a human or AI (idea provenance), regardless of who wrote its words. To focus IdeaLens on ideas rather than prose, we represent documents as outlines: lists of items that each pair a discourse role with a brief, paraphrased description of the content, minimizing word-level overlap with the raw text. We train IdeaLens on 1M FineWeb documents with silver labels from Pangram, a prose provenance detector. Since the outlines are largely stripped of surface-level information, the labels must be fit mainly through the ideas. In a controlled study, IdeaLens's AI flag rate drops from 95% to 7% as models write from increasingly detailed human plans, while Pangram 4 still flags 92%; from AI-derived plans, IdeaLens stays above 96%. Conversely, on a new dataset of 50 stories that human authors wrote from AI-generated plans, IdeaLens flags 68% of the stories as AI, compared to 8% for Pangram 4. On a comprehensive suite of 19 existing detection benchmarks, we show that IdeaLens maintains strong detection rates at low false positive rates, suggesting that ideas themselves provide a powerful discriminative signal, and its performance holds across domains, formats, and languages. Finally, we examine 90K predictions from IdeaLens to characterize systematic differences between human and AI ideation. We release our models and labeled datasets to facilitate future research on idea provenance detection.

Location: NCS 220

Zoom Link: https://stonybrook.zoom.us/j/92942833294?pwd=927683
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