Professor Petar M. Djuric, SUNY Distinguished Professor and Savitri Devi Bangaru Professor in Artificial Intelligence at Stony Brook University, has been selected as a plenary speaker at the upcoming 23rd IEEE Statistical Signal Processing Workshop (SSP 2025). The event will be held from June 8-11, 2025, in Edinburgh, Scotland, and is one of the premier international forums for the latest advances in statistical signal processing.

Professor Djuric's plenary talk, titled Quantifying causal relationships: Dynamic strengths, attributions, and confounders, will take place on June 10 from 9:00 AM to 10:00 AM EST. His presentation addresses foundational challenges in data-driven causality, proposing novel methodologies for quantifying causal strength in both static and dynamic systems, with special attention to latent confounders and attribution analysis.

This work has broad implications across disciplines including healthcare, economics, and climate science--areas where causal understanding drives critical decisions and innovations.

Professor Djuric has been a long-standing leader in the fields of machine learning and signal and information processing. After receiving his Ph.D. from the University of Rhode Island, he joined the faculty at Stony Brook University, where he served as Chair of the Department of Electrical and Computer Engineering from 2016 to 2023. He is also the founding Editor-in-Chief of the IEEE Transactions on Signal and Information Processing Over Networks and a Fellow of IEEE, EURASIP, AAIA, and AIIA.

Early bird registration for the workshop is open until April 30, 2025. For more information, visit the official SSP 2025 website.

The Renaissance School Of Medicine Department of Scientific Affairs and its Single Cell Genomics facility are excited to host a special seminar and discussion on AI and single cell genomics analysis:

With the decreasing cost of sequencing, many biobanks and large research cohorts have moved to whole genome sequencing (WGS) and single-cell RNA-seq. However, making use of this deluge of data remains a challenge. I will discuss statistical and deep learning approaches that we are exploring to address the challenge of noncoding variant interpretation, including our work as part of the Alzheimer's disease sequencing project.

Speaker: David A. Knowles, PhD. Asst. Professor of Computer Science, Interdisciplinary Appointee in Systems Biology, Columbia University Core Faculty Member, New York Genome Center

Join us in person: Health Science Tower Level 3, Lecture Hall 5
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
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.
CSE 656 Seminar in Computer Vision 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 15 minutes) by multiple students. Students can register for 1 credit for CSE656. Registered students must attend and present a minimum of 2 talks. Everyone else is welcome to attend. Fill in https://forms.gle/q6UG9ygauLp2a8Po8 to subscribe to our mailing list for further announcement.
University Libraries Presents:
Join librarian Christine Fena for an interactive workshop that invites you to explore AI tools first hand, not just as users, but as critical investigators.
Through playful experimentation and collaborative discovery, you'll uncover inherent biases, probe algorithmic flaws, and gain a deeper understanding of AI's limitations and societal impacts.

RSVP on SBEngaged

Location: Melville Library, Central Reading Room, Lab B
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.
The Antonija Prelec Memorial Committee in collaboration with Stony Brook University Libraries are very excited to bring you the 2019 Prelec Memorial Lecture! This year, we are pleased to announce our speaker is Patricia Flatley Brennan, RN, PhD, Director of the National Library of Medicine.

No registration required. Find more information here.