Jerome Liang, PhD 

Professor of Radiology, Biomedical Engineering, Electric and Computer Engineering, and Computer Science 

Co-Director of Research 

Department of Radiology 


Artificial intelligence, machine learning and computer-aided diagnosis in cancer Imaging 

February 11, 2021 

12:00pm - 1:00pm 

Virtual Seminar - Zoom 

https://stonybrook.zoom.us/j/98155629970?pwd=YzRvcnJnTlNTT1E5ak1oZEJvWTZHQT09 

Meeting ID: 981 5562 9970 

Passcode: 950410 

Host: 

Wei Zhao, PhD 

Professor of Radiology and Biomedical Engineering 

Educational Objectives  

Upon completion, participants should be able to:  

(1) Learn different medical image representations of cancer attributes, such as heterogeneity, high tendency to grow, etc.  

(2) Learn how computer (machine) can be trained (or programmed) to recognize the image representations.  

(3) Learn how artificial intelligence can drive the machine learning to maximize the performance of computer-aided diagnosis (CADx).  

Disclosure Statement  

In compliance with the ACCME Standards for Commercial Support, everyone who is in a position to control the content of an educational activity provided by the School of Medicine is expected to disclose to the audience any relevant financial relationships with any commercial interest that relates to the content of his/her presentation.  

 

The speaker, Jerome Liang, PhD, the planners; and the CME provider have no relevant financial relationship with a commercial interest (defined as any entity producing, marketing, re-selling, or distributing health care goods or services consumed by, or used on, patients), that relates to the content that will be discussed in the educational activity.  

 

CONTINUING MEDICAL EDUCATION CREDITS  

The School of Medicine, State University of New York at Stony Brook, is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.  

 

The School of Medicine, State University of New York at Stony Brook designates this live activity for a maximum of 1.0 AMA PRA Category 1 Credits™. Physicians should only claim credit commensurate with the extent of their participation in the activity.  

 

Should you be logging in Zoom by using your tablet or mobile device, please be sure to add your Full Name and/or Email for CME credit. 

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.

Don't Just Fix it in Post: A Science of AI Must Study Training Dynamics


Abstract: What would it mean to have a scientific understanding of AI? Models are not static objects: they are snapshots of time-evolving processes shaped by data, objectives, architectures, and optimization dynamics. Yet much of AI research treats models as fixed artifacts, analyzing behaviors after training rather than asking why they emerge. This position paper argues that a science of AI must move beyond post hoc fixes and study the training dynamics that produce model behavior. Such a science should support progressively stronger forms of understanding: predicting outcomes from early training signals, intervening when trajectories go wrong, and ultimately designing training procedures that more reliably produce desired properties. Scaling laws have made prediction routine for loss; the challenge is extending this success to capabilities, biases, robustness, and safety-relevant behaviors. We articulate requirements for such theories grounded in the history and philosophy of science, examine progress in mechanistic interpretability, fairness, memorization, and simplicity bias, and identify concrete open problems.
Location: NCS 220

Zoom Link: https://stonybrook.zoom.us/j/92942833294?pwd=ysKMaQx7Hp0lkq5nOb3zJ9ivNZVvLv.1
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 Pittsburgh Supercomputing Center is pleased to present a Machine Learning and Big Data workshop.

This workshop will focus on topics including big data analytics and machine learning with Spark, as well as deep learning.

This will be an IN PERSON event hosted by various satellite sites, there WILL NOT be a direct to desktop option for this event. SBU's Institute for Advanced Computational Science (IACS) is one of those satellite sites!

Location: IACS Conference Room #2

Interested applicants must first have an ACCESS ID. If you don't have the ID, please visit this page to create one: ACCESS USER REGISTRATION.


Once you have an ACCESS ID, please login (see top right here) then register here.

​This session brings together the scientists, agencies, and community partners generating environmental data across New York City to confront a shared challenge: critical atmospheric and marine data is being collected across the region, but too often in silos that limit its reach and impact.

​Using Governors Island's environmental sensing efforts as a working case, the program opens into a broader conversation. We will discuss how disparate data streams and objectives across NYC can be coordinated, shared, and activated for the benefit of the broader NYC community. And how that data can support healthier and safer communities, emergency preparedness and resilience, more informed city planning and operations, and better decision-making by businesses and investors.

​This session will include a fireside chat on the state of hyperlocal data in NYC with Assistant Commissioner Carolyn Olson, a panel and discussion on deploying hyperlocal data, and a tour of the Governors Island Environmental Observatory (GIEO). We hope to see you there.

Location: 110 Andes Rd New York, NY

Register to join.

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.

Title: Mechanistic Interpretability, Verbalizable Representations Form a Global Workspace in Language Models

Abstract, Mechanistic Interpretability: The rise of the term mechanistic interpretability has accompanied increasing interest in understanding neural models -- particularly language models. However, this jargon has also led to a fair amount of confusion. So, what does it mean to be mechanistic? We describe four uses of the term in interpretability research. The most narrow technical definition requires a claim of causality, while a broader technical definition allows for any exploration of a model's internals. However, the term also has a narrow cultural definition describing a cultural movement. To understand this semantic drift, we present a history of the NLP interpretability community and the formation of the separate, parallel mechanistic interpretability community. Finally, we discuss the broad cultural definition -- encompassing the entire field of interpretability -- and why the traditional NLP interpretability community has come to embrace it. We argue that the polysemy of mechanistic is the product of a critical divide within the interpretability community.

Abstract, Verbalizable Representations Form a Global Workspace in Language Models: If the mind is an ocean, we spend our lives floating at the surface. Beneath us, an enormous amount of processing takes place without our knowledge: our visual systems parsing the contours of a face, our motor circuits maintaining our posture. At any given moment, only a small fraction of this neural activity is accessible to us. Yet it is this privileged sliver of activity that we rely on to reason deliberately: to plan what ingredients to buy for a recipe, or to puzzle out why an engine won't start. Such thoughts can be articulated out loud, deliberately held in mind, and brought to bear on whatever task the moment demands. This distinction, between our accessible thoughts and our unconscious processing, is perhaps the most striking feature of human cognition.
In this paper, we present evidence that an analogous functional distinction has emerged in modern AI models. Specifically, we observe that language models maintain a privileged set of internal representations, available for report, modulation, and flexible internal reasoning, atop a much larger volume of automatic processing. We identify these representations using a new interpretability technique, which surfaces the concepts a model is poised to verbalize at any point in its processing. Measuring and intervening on these representations provides us a window into a model's thought processes, uncovering internal reasoning and reactions that do not appear in its output.

Location: NCS 220

Zoom Link: https://stonybrook.zoom.us/j/92942833294?pwd=ysKMaQx7Hp0lkq5nOb3zJ9ivNZVvLv.1&jst=2