Department of Ecology and Evolution Colloquium
Arthur Porto is Assistant Curator of Al for Biodiversity at the Florida Museum of Natural History. His research sits at the intersection of evolutionary biology, computer vision, and machine learning, with a focus on developing open, scalable methods for extracting biological information from images and natural- history collection objects.
Life Sciences Building, Room 038
Scaling the NY AI Innovation Ecosystem
The State University of New York at Stony Brook will bring together leading AI experts to promote a future where AI drives responsible progress. This two-day event will provide a significant opportunity to explore the future of AI, exchange ideas, and connect with those at the forefront of research and deployment. We invite faculty, staff, and students from all SUNY institutions and beyond, as well as industry AI practitioners and policymakers to attend.
Recognized AI experts from academia, industry, and government will present on topics such as AI applications, innovative developments in research and technology, workforce development, as well as ethical and societal impacts.
A 90-minute poster session is included in the schedule. If you would like to submit an abstract for consideration, please see the Call for Abstracts. The poster session segment of the symposium will be held in honor of the Inauguration of Dr. Andrea Goldsmith, the State University of New York at Stony Brook's seventh President. Poster printing for all participants will be covered by the Inauguration Planning Committee. SUNY students presenting posters are also eligible for travel reimbursement.
We kindly ask faculty to encourage their students to attend and to submit their work for presentation.
For additional information and to register, visit the symposium website. Please direct any questions to suny-ai-symposium-sbu@
The 20th International Conference on Emerging Technologies for a Smarter World (CEWIT 2025)
The Innovation Edge: Harnessing AI for the Future
Exploring Generative AI, Agentic AI, and Frontier Technologies Revolutionizing Healthcare, Defense, Energy, FinTech, and Beyond
The Innovation Edge: Harnessing AI for the Future
Exploring Generative AI, Agentic AI, and Frontier Technologies Revolutionizing Healthcare, Defense, Energy, FinTech, and Beyond
Organized by the New York State Center of Excellence in Wireless and Information Technology (CEWIT) at Stony Brook University, our international conference is a destination for researchers, innovators and entrepreneurs, across borders and disciplines. CEWIT2023 conference attracted over 150 industry and academic participants worldwide. Over twenty-three presenters took the podium in breakout sessions and engaging panel discussions.
Continuing the tradition since the inception of our conference in 2003, CEWIT2025 will be a premier forum for presentations of cutting-edge research as well as the exchange and transfer of emerging technologies and innovative applications. We are expecting renowned speakers, presenters and panelists from industry, academia and government, beginning with a series of plenary presentations & a keynote, and followed by several conversational panels - all for an audience ready to network!
Location: The Center of Excellence in Wireless and Information Technology (CEWIT), Stony Brook University
Event Details: Visit CEWIT2025 site to learn more about the event
Questions/Concerns: CEWIT Conference Team at 631-216-7114 or info@cewit.org
We invite faculty to deliver a 10-minute presentation during our afternoon session at the CELT Symposium on April 11, 2025. Showcase how you use emerging technology (i.e. AI, VR, etc.) to support diverse student populations and enhance learning experiences. Share your innovative strategies and inspire others!
CELT Symposium Theme: A New Era of Inclusivity and Innovation in Higher Education
https://t.e2ma.net/click/5w0gph/5wwlu4oe/9v63j6
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
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
Register here.
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.
Subject: RADIOLOGY GRAND ROUNDS CT Colonography: An Effective Test for Colorectal Cancer Screening- Judy Yee, M.D.
When: Wednesday, May 12, 2021 12:00 PM-1:00 PM (UTC-05:00) Eastern Time (US & Canada).
Where: JOIN ZOOM MEETING
Judy Yee, MD
Chair, Department of Radiology
Professor, Department of Radiology
Abdominal Imaging
Join Zoom Meeting
https://einsteinmed.zoom.us/j/
Meeting ID: 977 8219 0723
Passcode: 101083
Abstract
Driving intelligence test is critical to the development
and deployment of autonomous vehicles. The
prevailing approach tests autonomous vehicles in life-
like simulations of the naturalistic driving environment.
However, due to the high dimensionality of the
environment and the rareness of safety-critical events,
hundreds of millions of miles would be required to
demonstrate the safety performance of autonomous
vehicles, which is severely inefficient. We discover that
sparse but adversarial adjustments to the naturalistic driving environment, resulting in the
naturalistic and adversarial driving environment, can significantly reduce the required test
miles without loss of evaluation unbiasedness. By training the background vehicles to
learn when to execute what adversarial maneuver, the proposed environment becomes
an intelligent environment for driving intelligence testing. We demonstrate the
effectiveness of the proposed environment in a highway-driving simulation. Comparing
with the naturalistic driving environment, the proposed environment can accelerate the
evaluation process by multiple orders of magnitude.
ZOOM LINK: Meeting ID: 950 6760 3617; Passcode: 426506
https://stonybrook.zoom.us/j/95067603617?pwd=dXQybEprSkNlTFY3WHlWYjViUG95UT09
Bio
Professor Henry Liu is a professor in the Department of Civil and Environmental
Engineering at the University of Michigan, Ann Arbor. He is also a Research Professor at
the University of Michigan Transportation Research Institute and the Director for the
Center for Connected and Automated Transportation (USDOT Region 5 University
Transportation Center). Prof. Liu conducts interdisciplinary research at the interface
between civil and mechanical engineering. Specifically, his scholarly interests concern
traffic flow monitoring, modeling, and control, as well as testing and evaluation of
connected and automated vehicles. He has published more than 100 refereed journal
papers and is listed as one of the top 50 leading authors in the past 50 years (1969-2019)
in the prestigious Transportation Research journal. Professor Liu and his work have been
widely recognized in public media for promoting smart transportation innovations. He has
appeared on media outlets including CNBC, Forbes, Technode, etc. In 2019, Professor
Liu was invited to testify on national transportation research agenda in front of the US
House Subcommittee on Research and Technology. Professor Liu has nurtured a new
generation of scholars, and some of his PhD students and postdocs have joined first class
universities such as Columbia University, Purdue University, RPI, etc. Prof. Liu is the
managing editor of Journal of Intelligent Transportation Systems.
The Center of Excellence in Wireless and Information Technology (CEWIT) will host the 16th International Conference on Emerging Technologies for a Smarter World (CEWIT2020) virtually on November 5, 2020. The conference will center on the four major fields which are penetrating our business and personal lives: Machine Learning, Artificial Intelligence, Blockchain and Computational Medicine. For more info visit: https://www.cewit.org/.
Zoom Like a Pro! Unlock Whiteboard, Polls, AI Companion, and more to supercharge student participation. This hands-on workshop explores innovative ways to use Zoom's built-in tools to enhance active learning activities in your classes. Learn how to utilize the Whiteboard feature to make collaborative work more engaging, use Polling and Quizzes for instant feedback, AI Companion for summary, and Breakout Sessions for group activities.
Register here: https://stonybrook.zoom.us/meeting/register/tJckf--rpj4pGdRV0ItgTW8Lk7gn_RuykByO#/registration