New York Scientific Data Summit (NYSDS) is a premier annual conference that brings together researchers and thought leaders from academia, national labs and industry to exchange ideas and foster collaboration focused on data-driven science and technology. Co-hosted by Brookhaven National Laboratory and the Institute for Advanced Computational Science (IACS) at Stony Brook University, NYSDS 2025 will take place on September 11-12, 2025, in the SUNY Global Center in New York City.

NYSDS 2025 will spotlight artificial intelligence (AI), machine learning (ML) and robotics - fields currently at a pivotal point with transformative impacts on science and technology. From accelerating computationally demanding simulations to discerning signals from noisy data, AI/ML has become an integral part of the scientific workflows. Despite many advances, challenges remain to ensure that AI/ML applications are reliable, explainable and trustworthy.

Robotics, a growing field that couples AI with physically actuated mechanical bodies, has seen increased interest in areas spanning science, technology and manufacturing. The need for real-time decision-making and control, along with the intricate morphology of robots, makes robotics an intriguing application of AI, advanced computing and optimization.


This NYSDS 2025 is open to the public. To be eligible to attend, all participants must register online by August 30, 2025. For questions or assistance with registering, please contact the Summit Coordinator.

Register here.

Visual Analytics and Machine Learning for Biomedical Imaging Diagnosis

 

Arie Kaufman

 

We present an integrated approach using visual analytics and machine learning (ML) to diagnose abnormalities in 3D radiological imaging and biological microscopes. The primary example will involve 3D virtual pancreatography (VP), a novel visualization-ML procedure and application for non-invasive diagnosis and classification of pancreatic lesions, the precursors of pancreatic cancer. Currently, non-invasive screening of patients is performed through visual inspection of 2D axis-aligned CT images, though the relevant features are often not clearly visible nor automatically detected. VP is an end-to-end visual diagnosis system that includes an ML-based automatic segmentation of the pancreatic gland and the lesions, a semi-automatic approach to extract the primary pancreatic duct, an ML-based automatic classification of lesions into four prominent types, and specialized 3D and 2D exploratory visualizations of the pancreas, lesions and surrounding anatomy. We combine volume rendering with pancreas- and lesion-centric visualizations and measurements for effective diagnosis. We designed VP through close collaboration and feedback from expert radiologists, and evaluated it on multiple real-world CT datasets with various pancreatic lesions and case studies examined by the expert radiologists. Other applications include virtual colonoscopy, COVID-19, pathology, brain neurites, etc.


Biography: Arie Kaufman is Distinguished Professor and formerChair of the Department of Computer Science at Stony Brook University, where he is also Director of the Center for Visual Computing (CVC), and Chief Scientist at the Center of Excellence in Wireless and Information Technology (CEWIT). 

He received his PhD in Computer Science at Ben-Gurion University of the Negev in 1977.   He is known for his work in visualization, graphics, virtual reality, user interfaces, multimedia, and their applications, especially in bio-medicine. He is especially well known for his work on the 3-dimensional virtual colonoscopy, a revolutionary low-risk technique for colon cancer screening, and for pioneering the use of Graphics Processing Units (GPUs) and GPU-clusters. In 2012, he presided over the development and opening of the Reality Deck, the largest virtual reality display in the world, at Stony Brook University.

Kaufman was the founding Editor in Chief of IEEE Transactions on Visualization and Computer Graphics (TVCG), co-founded the IEEE Visualization Conference and Volume Graphics series, and is currently the director of IEEE Computer Society Technical Committee on Visualization and Graphics. He is an IEEE Fellow, ACM Fellow, winner of many awards, including the IEEE Visualization Career Award, and member of the European Academy of Sciences.



Steven Skiena is inviting you to a scheduled Zoom meeting.

Topic: AI Seminar: Arie Kaufman
Time: Apr 21, 2021 10:00 AM Eastern Time (US and Canada)

Join Zoom Meeting
https://stonybrook.zoom.us/j/96017498640?pwd=SE0rdHB6ZVlCM2ZpY2RnRUxyVnR3Zz09

Join Stony Brook University's Center for Excellence in Learning and Teaching (CELT) for a boot camp on how to use AI to enhance your teaching and courses. This event will demonstrate how ChatGPT, Microsoft Copilot, NotebookLM, 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.

https://stonybrook.zoom.us/j/92511854285?pwd=QRTHfULqHMWxJYoVyt3piOhNxWLfvs.1
Abstract: Modern technologies enable enhanced integrity and privacy guarantees not just for data, but also for computation. This is perhaps most emphatically demonstrated by the steady rise of zero-knowledge proofs, which are short certificates that attest to the correctness of computations (e.g., an age verification check) without revealing any secret inputs (e.g., the birth date on a digital ID). This subtly powerful technology enables anonymous credentials, privacy-preserving machine learning, anonymous blockchains, and much more--making the question of efficient zero-knowledge proofs fundamental to modern secure systems. Echoing Moore's law for computing, zero-knowledge proofs have improved on this front by ten orders of magnitude in the last two decades. In this talk, I will discuss our work on overcoming a key bottleneck that has emerged in this development: memory efficiency.

Speaker: Abhiram Kothapalli is a postdoctoral scholar at the University of California, Berkeley, hosted by Sanjam Garg. He is a recent graduate of Carnegie Mellon University, where he earned his Ph.D. in Computer Science, advised by Bryan Parno. Previously, he was at the University of Illinois at Urbana-Champaign, where he earned his B.S. in Computer Science and B.S. in Mathematics. Kothapalli's research develops cryptographic techniques aimed at scaling expressive privacy and integrity guarantees across the internet.

Location: NCS 120

We will demonstrate how to make a personalized AI partner that can do certain tasks for you. This step by step process can be used to develop your own companion.

Understand what is a Gemini Gems and how it can be personalized for your custom needs. We will show you how to capture the tasks that you would like to complete and give you higher quality responses that will ensure your communication and tasks are completed with ease.

Register here.


Please join us this Friday, February 13th for the CSE 600 seminar given by Associate Professor Debswapna Bhattacharya, from the Department of Computer Science at Virginia Tech.

Abstract: Building a model of a biological system that can provide actionable hypotheses to form a solid foundation for experimental and theoretical analyses is one of the key challenges in biology and medicine. In this talk, I will present my group's ongoing work in developing, evaluating, and disseminating a new generation of computational methods for biomolecular modeling powered by artificial intelligence (AI) and machine learning (ML). First, I will introduce a new generation of AI/ML methods for improved modeling and characterization of protein-nucleic acid assemblies by deep graph learning using embeddings from biological large language models (LLMs) as well as geometric attention-enabled pairing of heterogeneous biological LLMs, a previously unexplored avenue. Then, I will present a novel generative deep learning model based on equivariant flow matching for end-to-end generation of all-atom RNA 3D structural ensemble. Finally, I will outline my future research directions on attaining atomic-level accuracy in computational modeling of biomolecules and their assemblies at scale.

Speaker: Debswapna Bhattacharya is an Associate Professor in the Department of Computer Science at Virginia Tech. He received his Ph.D. in Computer Science from the University of Missouri-Columbia in 2016. Before joining Virginia Tech in 2022, he was an Assistant Professor at Auburn University from 2017 to 2021. His research interests lie at the intersection of computational biology and machine learning, with a particular focus on artificial intelligence for computational structural biology, specifically in modeling and characterization of biomolecular structures and interactions. His research group has been developing novel computational and data-driven methods, software, and information systems for diverse biomolecular modeling problems, ranking among the best methods in community-wide blind assessments and serving the worldwide community of biomedical users. He received various research awards (NSF CAREER Award, NIH Maximizing Investigators' Research Award, NSF National AI Research Resource Award) and numerous institutional honors (National Distinction and Outstanding Contributor at Virginia Tech, Ginn Faculty Fellowship at Auburn University, Outstanding Engineering Faculty Award at Auburn University).
Location: NCS 120
CSE 600 Seminar Series | Fall 2025


Abstract: The first part of the presentation focuses on the fundamental role that failures play in the Ph.D. journey, highlighting how they offer invaluable learning experiences to build resilience, critical thinking, and adaptability. Instead of viewing failures as signs of inadequacy, they should be recognized as opportunities to learn, re-evaluate, and develop the persistence needed for success in a high-stakes research environment. In the second part of the presentation, we take a quick look at the evolution of distributed databases research at Stony Brook and then focus on different challenges associated with distributed transaction processing systems functioning in untrustworthy environments. 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 applications, modern hardware, and new cloud platforms arise, distributed transaction processing systems need to be designed with full-stack adaptivity in mind. This presentation discusses our vision for a reinforcement learning (RL)-based distributed transaction processing system that adjusts effectively in real time to dynamic fault scenarios and evolving workloads.

Bio: Mohammad Javad Amiri is an Assistant Professor in the Department of Computer Science at Stony Brook University. Before joining Stony Brook, he was a postdoctoral researcher in the Computer and Information Science Department at the University of Pennsylvania. He received his Ph.D. in Computer Science from the University of California, Santa Barbara. His research mainly lies at the intersection of data management and distributed systems, focusing on distributed transaction processing, consensus protocols, and blockchains.

Join the Office of Educational Effectiveness' upcoming workshop on the transformative potential of AI tools to enhance program assessment. Learn how to leverage AI to create targeted learning objectives, detailed rubrics, and precise benchmarks that will elevate the quality and effectiveness of your program assessment process. Join in-person on Oct. 17 at 10:30 am or virtually on Oct. 21 at 12 pm.

Register in advance: https://calendar.stonybrook.edu/site/office-educational-effectiveness/event/leveraging-ai-in-assessment-zoom/
The overall purpose of this seminar is to bring together people with interests in Computer Vision theory and techniques and to examine current research issues. This course will be appropriate for people who already took a Computer Vision graduate course or already had research experience in Computer Vision. To enroll in this course, you must either: (1) be in the PhD program or (2) receive permission from the instructors.

Each seminar will consist of multiple short talks (around 10 minutes) by multiple people. Students can register for 1 credit for CSE 656. Registered students must attend and present a minimum of 2 or 3 talks. Everyone else is welcome to attend. Fill in https://forms.gle/pCVXovgfMfQwGqG38 to subscribe to our mailing list for further announcement.

Join the Department of Biomedical Informatics for an exclusive fall semester programming bootcamp. Discover essential programming, data analytics, and machine learning skills crucial for biomedical informatics. Special topics such as Bioinformatics and NLP will be briefly covered.

Gain hands-on programming experience and discover diverse career opportunities in biomedical informatics.

Don't miss this chance to excel in healthcare data analytics and shape the future of the industry.

https://bmi.stonybrookmedicine.edu/Bootcamp/Bootcamp-Fall-2025

Location: NCS 120