18th Annual Engineering Ball
Flowerfield, St. James, NY
Thursday April, 2nd, 7:00 to 10:00 pm
Pick up your tickets in 231 Engineering (Monday - Friday, 10:00 am to 4 pm)
Presenting Partner: L3Harris
Join a faculty development program to support instructors across campus with navigating/integrating AI in their courses. We're inviting interested faculty to participate in the grant project called Fostering Writing-to-Learn Skills with Critical AI Literacy: A Faculty Development and Student Support Program (funded through the AI3 Institute).
https://docs.google.com/document/d/1b51tvfK0HSOkCW7cwYq2nyyeeHtvBZYC7_XHv7Av8wQ/edit?tab=t.0
Time commitment and completion requirements :
Attend four sessions and a final symposium on the following dates/times:
Friday, September 12 from 11am - 12:30pm over Zoom
Friday, September 26 from 11am - 12:30pm over Zoom
Friday, October 10 from 11am - 12:30pm over Zoom
Friday, October 24 from 11am - 12:30pm over Zoom
Friday, November 14 from 10am - 1pm in Wang 201 - please note that this is an in person session only
Engage with online materials in Brightspace prior to each of the sessions (mainly to update a syllabus, assignment, or teaching strategy that you can share and discuss at the workshop)
https://docs.google.com/document/d/1b51tvfK0HSOkCW7cwYq2nyyeeHtvBZYC7_XHv7Av8wQ/edit?tab=t.0
Please join University Libraries on March 29 at 1:00 via Zoom as we welcome Dr. Zhang, SUNY Empire Innovation Professor at SBU's Power Lab. This lab is pioneering the research of coordinated networked microgrids (NMs) that can possibly help to restore neighboring distribution grids after a major blackout. That these NMs hold promise to significantly enhance the day-to-day reliability of the power grids, we are proud to host Dr. Zhang as a member of our STEM Speaker Series. Registration required.
https://library.stonybrook.
What AI tools are available to help with the scholarly research process? Are they helpful? What do they do and is it worth the time and energy to try them out? Join librarian Christine Fena to explore and compare established and emerging AI research tools such as Elicit, Scite, Consensus, and Undermind. The online workshop will provide a starting point to understanding what these tools are, the basics of how they work, and how AI research assistants might bring changes to your search process in the future. All are welcome!
Register here via Zoom.
Register here via Zoom.
You are cordially invited to attend the biweekly Brookhaven AI Mixer (BAM). BAM includes one short talk on AI research happening at BNL, followed by an open mixer over coffee and snacks for everyone to network and discuss all things AI. The first half hour will consist of presentations that will be available via ZOOM, and the second half hour will be for in person only networking.
Join us every other Tuesday at noon in CDSD's Training Room (building 725, 2nd floor) to learn about interesting AI methods and applications, engage with potential collaborators, prepare for pending FASST funding calls, and build a community of AI for Science at BNL.
HPCortex - a new, general-purpose machine learning library for HPC
Abstract: I will introduce HPCortex, a lightweight, C++, MPI-native machine-learning library for heterogeneous HPC systems. It implements many common architecture patterns including transformers, graph neural networks, and convolutional networks, and delivers performance portability across NVIDIA, AMD, and Intel GPUs while depending only on MPI and standard compiler/BLAS stacks. I will illustrate its capabilities via a surrogate model for the RHIC AGS Booster digital twin, a simple GNN for a coupled spring system, and a compact language model, then outline the roadmap.
Biography: Christopher is a research scientist and head of the Scientific Computing Applications Group in the Computational Science Department at Brookhaven National Laboratory. Previously he was an assistant staff scientist in the Physics Dept. at Columbia University, and held physics postdoctoral research positions at both Brookhaven and Columbia. He earned his Ph.D in Theoretical Physics from the University of Edinburgh, UK.
His scientific background is in lattice QCD and high performance computing, but since joining Brookhaven in 2020 his research interests have expanded to include machine learning, applied mathematics and performance analysis, with a particular emphasis on building tools to support scientific research on HPC systems.
Location: CDS, Bldg. 725, Training Room
Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1604143373?pwd=hHT2yaIjahBIQ6tieURFqs8Pwex9gU.1
Meeting ID: 160 414 3373
Passcode: 277410
Abstract:
Large language models (LLMs) have transformed the way humans write code, bringing unprecedented automation to software development. In this talk, I will first provide an overview of my research on enhancing LLMs' code intelligence, optimizing each step of the development pipeline towards more complex software engineering tasks. I will then delve into my key contributions, focusing on how to equip LLMs with a deeper, more comprehensive understanding of software programs. Finally, I will discuss the future of AI-driven software engineering, envisioning a new era of automation that is more reliable, intelligent, and cost-efficient.
Bio:
Yangruibo (Robin) Ding is a Ph.D. candidate in the Department of Computer Science at Columbia University. His research is at the intersection of Software Engineering and Machine Learning, focusing on developing large language models (LLMs) for code. He trains LLMs to generate, analyze, and refine software programs and constructs benchmarks to systematically evaluate LLMs in solving software engineering tasks. He also studies how to improve LLMs' reasoning capability to tackle complex programming tasks, such as debugging and patching. His interdisciplinary research has been published in top-tier conferences of software engineering, programming languages, natural language processing, and machine learning. He won an ACM SIGSOFT Distinguished Paper Award, an IEEE TSE Best Paper Runner-up, and received an IBM Ph.D. Fellowship.
Location:
NCS 120
This event brings together people with interests in Computer Vision theory and techniques and examines current research issues in the field.
Each seminar consists of multiple short talks (around 15 minutes) by several students.
Join Zoom Meeting:
https://stonybrook.zoom.us/j/93547152068?pwd=WVpoRVgzelBXeloxdXVEakNSb2M5UT09
Meeting ID: 935 4715 2068 | Passcode: 481832
Each seminar consists of multiple short talks (around 15 minutes) by several students.
Join Zoom Meeting:
https://stonybrook.zoom.us/j/93547152068?pwd=WVpoRVgzelBXeloxdXVEakNSb2M5UT09
Meeting ID: 935 4715 2068 | Passcode: 481832
Time: 04/28 Wed 3pm-4pm
Remote Access
Join Zoom Meeting https://stonybrook.zoom.us/j/
Meeting ID: 956 1719 7636 Passcode: 924293
Title: Brain imaging genetics for Alzheimer's disease: integrated analysis and machine learning
Li Shen, Ph.D.
Professor of Informatics
Department of Biostatistics, Epidemiology and Informatics
Perelman School of Medicine
University of Pennsylvania
Bio: Li Shen, Ph.D., is a Professor of Informatics in the Department of Biostatistics, Epidemiology and Informatics at the Perelman School of Medicine in the University of Pennsylvania. He is an elected fellow of the American Institute for Medical and Biological Engineering (AIMBE). He obtained his Ph.D. degree in Computer Science from Dartmouth College. The central theme of his lab is focused on developing computational and informatics methods for integrative analysis of multimodal imaging data, high throughput omics data, cognitive and other biomarker data, electronic health record (EHR) data, and rich biological knowledge such as pathways and networks, with applications to complex disorders. His research interests include medical image computing, biomedical informatics, machine learning, network science, imaging genomics, Alzheimer's disease, and big data science in biomedicine. He has authored over 280 peer-reviewed articles (h-index 57) in these fields. Dr. Shen's work has been continuously supported by the NIH and NSF, and he is presently the PI of multiple NIH and NSF grants on developing computational methods for various biomedical applications including brain imaging genomics, genetics of Alzheimer's disease, genetics of human connectome, mining drug effects from the EHR data, and big data mining in brain science. He is co-leading the NIA Alzheimer's Disease Sequencing Project AI4AD Consortium and oversees the imaging genomics aspect of this landmark project. Dr. Shen served as the Executive Director of the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society Board of Directors during 2016-2019. He has chaired and co-chaired various professional meetings in medical image computing and bioinformatics. He is an Associate Editor of BioData Mining and Frontiers in Radiology (Section of AI in Radiology), and serves on the Editorial Board of Medical Image Analysis and Brain Imaging and Behavior.
Abstract: Brain imaging genetics is an emerging data science field, where integrated analysis of brain imaging and genetics data, often combined with other biomarker, clinical and environmental data, is performed to gain new insights into the genetic, molecular and phenotypic characteristics of the brain as well as their impact on normal and disordered brain function and behavior. Many methodological advances in brain imaging genetics are attributed to large-scale landmark biobank projects such as the Alzheimer's Disease Sequencing Project, the Alzheimer's Disease Neuroimaging Initiative, and the UK Biobank. Using the study of Alzheimer's disease as an example, we will discuss fundamental concepts, state-of-the-art statistical and machine learning methods, and innovative applications in this rapidly evolving field. We show that the wide availability of brain imaging genetics data from various large-scale biobanks, coupled with advances in biomedical statistics, informatics and computing, provides enormous opportunities to contribute significantly to biomedical discoveries in brain science and to impact the development of new diagnostic, therapeutic and preventative approaches for complex brain disorders such as Alzheimer's disease.
More details:
https://bmi.
From Discovery to Impact
An Interdisciplinary Conference on the Science of Science and Entrepreneurship
March 26-27, 2027 · Charles B. Wang Center, Stony Brook University
Stony Brook University invites paper and poster submissions for From Discovery to Impact, a focused, high-engagement conference at the nexus of the Science of Science and Entrepreneurship. The two fields share a common object of study -- the lifecycle of an idea -- yet have largely developed in isolation. The Science of Science (SoS) uses large-scale data and computational models to understand how knowledge is produced; entrepreneurship research explains how that knowledge is commercialized.
By convening scholars from both traditions, the conference takes up questions that neither field can answer alone:
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Two Days, Two Tracks
Day 1 · March 26
The Creation of Knowledge
Science of Science. Keynotes, paper sessions, an interdisciplinary panel, and a poster session.
Day 2 · March 27
The Commercialization of Knowledge
Entrepreneurship. Paper sessions and a grant-incubation workshop where participants form multi-PI teams.
Read more and register.