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.

OVERVIEW


This workshop, Expanding Horizons in AI with HPC, aims to explore the dynamic intersection of AI and HPC, focusing on how advanced computing can accelerate AI research and applications. As AI models become more complex and data-intensive, traditional computing systems struggle to meet the demand for scalability, efficiency, and speed. HPC offers a solution by providing the necessary infrastructure for training large-scale models, enhancing AI algorithms, and enabling breakthroughs in fields such as deep learning, natural language processing, and autonomous systems.

Through a combination of expert presentations and panel discussions, participants will gain insights into the latest developments in AI-HPC integration. Attendees will also engage in discussions on the future trends, challenges, and ethical considerations surrounding the use of HPC in AI.

The workshop is designed for AI researchers, data scientists, engineers, and HPC professionals seeking to enhance their understanding of how high-performance computing can drive innovation and expand the potential of AI in solving complex, real-world problems.

The workshop will be held at the Wang Center at Stony Brook University.

https://you.stonybrook.edu/hpcai/

PROGRAM

The program features sessions on HPC Architectures for AI, AI Applications in HPC, LLM's in HPC, and AI in HPC Workflows, and open student presentations. The tentative program and list of confirmed speakers is available at https://you.stonybrook.edu/hpcai/program/.

CALL FOR STUDENT PRESENTATIONS & PARTICIPATION

We are excited to offer students the opportunity to present their work in the area of high-performance scientific computing and artificial intelligence at the workshop. We are calling for students to submit their talk proposals (Name + Title) by April 15 to hpc_ai_workshop@stonybrook.edu. The committee will select the best submission to be presented at the workshop. Accepted speakers will be notified by April 22, 2025.

All students, regardless of whether they are presenting, may reach out to hpc_ai_workshop@stonybrook.edu for financial support to cover travel and lodging costs.

REGISTRATION

Registration is available at https://www.eventbrite.com/e/expanding-horizons-in-ai-with-hpc-tickets-1256469978529?aff=oddtdtcreator until May 2nd. The registration fee covers the workshop participation and the social event in the evening of May 9.

Regular registration: $200
Student registration: $100


IMPORTANT NOTE

The registration fee was meant to cover the room rent, catering, and dinner. Thanks to an RF seed grant, we are able to drop the registration fees for SBU students and staff/faculty. We still ask for an informal registration via email to hpc_ai_workshop@stonybrook.edu until April 27, so we can plan for catering and dinner.
Please get in touch with us if you have already registered as an SBU student/faculty/staff member for the workshop so we can handle any reimbursement.

The program is now online at https://you.stonybrook.edu/hpcai/program/.

The Vedanta Forum is devoted to one of humanity's oldest and most profound pursuits -- thinking. Thinking about who we truly are: the one that remains constant through childhood and old age, through waking, dream, and deep sleep. Thinking about the source and cause of creation, and its relationship to what inheres in us.

Across history, such thinking, both meditative and scientific, has been aimed at these questions. The ancient Upanishads proclaimed, Tat Tvam Asi -- Thou Art That -- revealing the non-dual identity of the individual and the ultimate reality. Centuries later, modern scientists such as Schrödinger and Bohr echoed similar intuitions about the unity of existence.

Over time, many philosophical approaches, traditions, and interpretive schools have arisen from such inquiry, each offering unique perspectives. The Forum will:

  • Focus on universal approaches and traditions and examine their teachings,

  • Foster comparative studies, and

  • Explore the practical benefits to society from such thinking,

through scholarly studies, dialogue, and debate also promoting accessibility to all qualified seekers. Additionally, the Forum will explore how these reflections can enrich life, education, and even technology.

Location: NCS 120 (New Computer Science), Engineering Dr, Stony Brook, NY 11794.

The program is available at: https://www.vedantaforum.org/events/program

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. 

A talk by Jerome Zhengrong Liang entitled, Machine Learning from Original Images to Texture Patterns: A Paradigm Shift from Non-Medical Application to Medical Diagnosis. Abstract: Artificial intelligence (AI) research for medical diagnosis started soon after human began to use computer, initially called artificial neural network (ANN) and now convolutional neural network (CNN). ANN has been mainly explored to classify the experts' handcrafted features from the original (or raw) images, while CNN has been mainly explored directly on the raw images for both tasks of extracting abstract features and classifying the features. Experimental evidences have been shown that CNN can be trained by a large number of the raw images with experts' scores (or labels) to match or even surpass the experts' performance for both non-medical and medical diagnosis applications. However, the performances of the CNN models as well as the experts on medical diagnosis dropped dramatically when the labels of the raw images were replaced by the corresponding medical pathological reports. Accumulated medical knowledge reveals that the lesion heterogeneity is a footprint of lesion evolution and ecology, and the heterogeneity is an indicator of lesion progress and response to medical intervention. The heterogeneity can be reflected by the image contrast distribution (or texture patterns) across the lesion volume. Image textures have been shown as an effective descriptor of the lesion heterogeneity for computer-aided diagnosis. Can we map the raw images into texture patterns (or images) and train CNN to learn from the texture images? This question is the central theme of this presentation with application to CT Colonography or virtual colonoscopy, a game from AlphaGo to PolypGo. Bio: Jerome Zhengrong Liang, PhD, IEEE Fellow Imaging Research and Informatics Laboratory Department of Radiology, Stony Brook University
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.
Event Website: bnl.gov/nysds
Dates: September 28-29, 2026
Location: SUNY Global Center, New York, NY
Co-hosts: Brookhaven National Laboratory, the Institute for Advanced Computational Science (IACS), and the AI Innovation Institute at Stony Brook University.

Join us for this premier annual conference that brings together researchers and thought leaders from academia, national labs, and industry to exchange ideas and foster cross-disciplinary collaboration centered on data-driven science and technology.

The theme of NYSDS 2026 is Transformational AI from Science to Society. This year's conference will focus on the ways in which artificial intelligence (AI), machine learning (ML) and robotics are impacting everything from our everyday lives to scientific endeavors. NYSDS2026 will feature the following main tracks:

  • Robotics and Embodied AI: advances in perception, control, learning and interaction for autonomous physical agents in real-world and scientific environments.

  • AI for Science: innovative applications ranging from the physical sciences to biology and medicine, and discussion of important ways in which AI is changing the nature of science.

  • AI for Energy: how energy is discovered and produced, how hazards are mitigated to ensure reliability of the power grid, and our search for new sources of critical minerals and materials.

  • AI for Risk Assessment: inventive uses of large amounts of data and the AI tools to assess and mitigate risks in national security, climate, and financial markets.

  • AI for Education: the ways in which education is being reimagined in the age of AI and the roadblocks to training the next generation of students.

Each track will include invited presentations, contributed talks, posters, and panel discussions.
Abstract:
Coarse grained (CG) models alleviate the drawbacks of all-atom simulations. The latter still pose challenges because they are computationally expensive and give access to limited spatiotemporal scales, despite the use of modern high-performance computing clusters. CG models ignore some of the atomistic degrees of freedom, leading to fewer interatomic interactions, hence less computing time. Introducing such models emphasizes the need to properly manage these multiple scales, by carefully deriving potentials and reconstructing conformations from their CG representations, usually with the help of Machine Learning. Following a bottom-up and force matching approach, we train a Physics-Informed Neural Network to extract the CG force field parameters from all-atom simulation data. We verify our approach by applying it to fibrin monomers to study multiple-fibrin polymerization in solution at the microsecond scale, after modifying the force field to incorporate further non-bonded interactions, not present in the training data. Access to these scales will allow us to study the effects of some of the molecules' components. Furthermore, we modify recent solutions in data-driven protein backmapping. Taking advantage of the developments in graph neural networks and variational inference, we introduce an intermediate step in the all-atom reconstruction of a molecule given its CG configuration, in an attempt to more accurately de-coarsen structures whose atom-to-CG-beads ratio is very high. The combined effect of our new forward and inverse coarse graining methodology will enable the in silico study of many phenomena that are highly dynamic and intrinsically multiscale.

Bio:
Georgios Kementzidis is a third year PhD student in the Department of Applied Mathematics and Statistics at Stony Brook University. His advisor is Dr. Yuefan Deng. His research interests lie at the intersection of Computational Science, molecular dynamics (MD) simulations, and Machine Learning (ML) applications to Computational Biophysics. He is particularly interested in coarse-graining and multi-scale simulations.

*Note: this seminar will be held in-person (food provided on a first-come, first serve basis) and online*

Join Zoom Meeting https://stonybrook.zoom.us/j/99510099036?pwd=EyowuLBGvUVLZDBlG6F6chkMICFOZ7.1
Meeting ID: 995 1009 9036
Passcode: 132419