This workshop synthesizes the latest research on the impact of AI usage in education so that you could make informed decisions on whether and how to use AI to facilitate your learning. You might have seen conflicting reports on whether the use of AI is good for learning. In this workshop, we are going to tease out, drawing on the latest research, which types of AI usage are beneficial or harmful for different kinds of learning. At the end of the workshop, you should walk away with more clarity on when and how to use AI for your own learning. Join PRODIG+ fellow on critical AI, Zheng Fu, in this informative workshop.
Last day of finals for 2019 Fall Semester: https://www.stonybrook.edu/commcms/registrar/registration/exams
Over the past decade, Artificial Intelligence (AI) has made stunning advances, from mastering language to solving the structure of proteins. These breakthroughs arise from more than forty years of work in neural networks, where ideas from neuroscience have inspired solutions in AI. In this lecture, Anthony Zador, MD, PhD, will explore how reverse engineering the brain's computations has driven progress in both fields, and how this back-and-forth between neuroscience and AI is set to grow even stronger -- with brain-inspired designs driving new AI advances while AI tools transform our understanding of how the brain works.
Speaker:
Dr. Zador works at the intersection of neuroscience and artificial intelligence. He is the Alle Davis Harris Professor of Biology at Cold Spring Harbor Laboratory, where he served as Chair of Neuroscience. He was named one of Foreign Policy's 100 Leading Global Thinkers and is a recipient of the Brain Research Foundation Fellowship, the Gill Symposium Transformative Investigator Award, and the Allen Distinguished Investigator Award.
Watch online at stonybrook.edu/live
Speaker:
Dr. Zador works at the intersection of neuroscience and artificial intelligence. He is the Alle Davis Harris Professor of Biology at Cold Spring Harbor Laboratory, where he served as Chair of Neuroscience. He was named one of Foreign Policy's 100 Leading Global Thinkers and is a recipient of the Brain Research Foundation Fellowship, the Gill Symposium Transformative Investigator Award, and the Allen Distinguished Investigator Award.
Watch online at stonybrook.edu/live
Abstract: At XTX Markets, we view algorithmic trading as one of the most compelling real-world frontiers for deep learning and foundation models. Every day, our systems generate forecasts for tens of thousands of financial instruments and execute over $300B in global trading volume: fully automated, with no discretionary human intervention. This domain combines massive data scale with high noise, adversarial dynamics, and frequent regime shifts, making it both scientifically challenging and commercially impactful. For machine learning researchers, it serves as a rigorous proving ground where advances in time-series modeling, large-scale optimization, representation learning, and foundation models can translate directly into measurable real-world outcomes. This talk will provide a high-level overview of our research agenda, infrastructure, and key open challenges at the intersection of large-scale AI and quantitative finance.
Speaker: Dr. Zhangyang Atlas Wang is the Research Director at XTX Markets, one of the world's leading high-frequency trading firms. He founded and leads the firm's AI Lab in New York City, focused on developing large-scale foundation models for financial time series and market data, powered by XTX's proprietary AI infrastructure. He is currently on leave from his position as the Temple Foundation Endowed Associate Professor at The University of Texas at Austin. His academic research has received numerous awards, and he has mentored a broad network of Ph.D. students and postdoctoral researchers. Many of his alumni now hold tenure-track faculty positions (eight to date) or senior research roles in industry (nineteen and counting). For more information about his group and alumni, please visit: https://www.vita-group.space/team.
Location: NCS 120
Refreshments will be served after the seminar in the first-floor atrium.
Speaker: Dr. Zhangyang Atlas Wang is the Research Director at XTX Markets, one of the world's leading high-frequency trading firms. He founded and leads the firm's AI Lab in New York City, focused on developing large-scale foundation models for financial time series and market data, powered by XTX's proprietary AI infrastructure. He is currently on leave from his position as the Temple Foundation Endowed Associate Professor at The University of Texas at Austin. His academic research has received numerous awards, and he has mentored a broad network of Ph.D. students and postdoctoral researchers. Many of his alumni now hold tenure-track faculty positions (eight to date) or senior research roles in industry (nineteen and counting). For more information about his group and alumni, please visit: https://www.vita-group.space/team.
Location: NCS 120
Refreshments will be served after the seminar in the first-floor atrium.
The Artificial Intelligence Innovation Institute (AI^3), with administrative support from the Office of the Vice President for Research (OVPR), invites applications to a seed grant program for collaborative projects in artificial intelligence, along three distinct tracks: Collaborative Research in AI, Technical Support for Discipline-Centric Research, and Seed Grants for AI Education and Service.
The program will fund projects for up to a one-year period, depending on the availability of funds. AI^3 anticipates making at least six awards on this call. A one-year, no-cost extension can be requested in the final 6 months of a project with approval subject to progress towards project goals and active participation in research themes.
Competitive applications will actively incorporate modern AI technologies into the work; integrate students; document significant potential for future funding or other growth-oriented outcomes; and highlight innovations.
The 2024 application deadline will be October 15, at 11:59 PM EST. Recipients will be notified by December 20, and projects are anticipated to commence at the start of the Spring 2025 semester.
The University's Main Commencement Ceremony will take place on Friday, May 23, 2025 at 11 am at Kenneth P. LaValle Stadium. Gates open at 10 am.
All guests need a valid ticket to enter LaValle Stadium - no exceptions. Children age 1 and older require a ticket. Seating is first-come, first-served.
Register here.
Abstract: Recent advances in Spatial Transcriptomics (ST) pair histology images with spatially resolved gene expression profiles, enabling predictions of gene expression across different tissue locations based on image patches. This opens up new possibilities for enhancing whole slide image (WSI) prediction tasks with localized gene expression. However, existing methods do not fully leverage the interactions between different tissue locations, which are crucial for accurate joint prediction. To address this, we introduce MERGE (Multi-faceted hiErarchical gRaph for Gene Expressions), which combines a multi-faceted hierarchical graph construction strategy with graph neural networks (GNN) to improve gene expression predictions from WSIs. By clustering tissue image patches based on both spatial and morphological features, and incorporating intra- and inter-cluster edges, our approach fosters interactions between distant tissue locations during GNN learning. As an additional contribution, we evaluate different data smoothing techniques that are necessary to mitigate artifacts in ST data, often caused by technical imperfections. We advocate for adopting gene-aware smoothing methods that are more biologically justified. Experimental results on gene expression prediction show that our GNN method outperforms state-of-the-art techniques on multiple metrics such as mean squared error (MSE), mean absolute error (MAE), and pearson correlation coefficient (PCC). Qualitative analysis establishes the effectiveness of MERGE in capturing cancer marker genes, thus consolidating its utility in diagnostics. As an extension of this work, we use MERGE in a setting with an uncertainty calibration branch to perform robust gene expression smoothing. We show that using patch-wise uncertainty from an uncertainty calibration model and the gene expression predictions from MERGE to enrich the ground truth gene expression matrix, results in better alignment with pathologist annotations, thus establishing that the smoothing is biologically informed.
Speaker: Aniruddha Ganguly
Location: Virtual Zoom Meeting
https://stonybrook.zoom.us/j/
Meeting ID: 547 484 7973
Passcode: 206739
Speaker: Aniruddha Ganguly
Location: Virtual Zoom Meeting
https://stonybrook.zoom.us/j/
Meeting ID: 547 484 7973
Passcode: 206739
Join us to share your thoughts about teaching, learning, and AI!
The landscape of higher education is rapidly evolving with the integration of Artificial Intelligence (AI). Through the Institute on AI, Pedagogy, and the Curriculum with AAC&U, we are exploring ways that we can better address AI in teaching and learning. We want to hear your experiences, your concerns, and your ideas.
This is an open discussion for all faculty and staff to share their perspectives on the opportunities and challenges AI presents in our academic environment.
We'll be exploring critical questions like:
Time: 12:30pm-1:45pm
Location: West Campus - Melville Library, Special Collections Seminar Room (the room is to the left at the top of the first flight of stairs from the Melville lobby)
or
Date: Wednesday, December 3rd
Time: 10:30am-11:45am
Location: East Campus - HSC 2-154
Please register in advance so we can confirm the room.
Note: Videos will not be shared publicly and comments will only be shared in aggregate.
Your input is vital. From pedagogy to assessment, your insights will be critical. We look forward to a thoughtful and productive conversation!
The landscape of higher education is rapidly evolving with the integration of Artificial Intelligence (AI). Through the Institute on AI, Pedagogy, and the Curriculum with AAC&U, we are exploring ways that we can better address AI in teaching and learning. We want to hear your experiences, your concerns, and your ideas.
This is an open discussion for all faculty and staff to share their perspectives on the opportunities and challenges AI presents in our academic environment.
We'll be exploring critical questions like:
- In the age of AI, what are the opportunities you see for enriching the classroom and curriculum? How can it enhance student learning or your professional practice?
- What are the most significant challenges and concerns that AI raises for you regarding academics, student integrity, or your workload?
- What resources (tools, training, technical support, policy guidance, etc.) do you need to feel confident and successful in the age of AI?
Time: 12:30pm-1:45pm
Location: West Campus - Melville Library, Special Collections Seminar Room (the room is to the left at the top of the first flight of stairs from the Melville lobby)
or
Date: Wednesday, December 3rd
Time: 10:30am-11:45am
Location: East Campus - HSC 2-154
Please register in advance so we can confirm the room.
Note: Videos will not be shared publicly and comments will only be shared in aggregate.
Your input is vital. From pedagogy to assessment, your insights will be critical. We look forward to a thoughtful and productive conversation!
- Dr. Rose Tirotta-Esposito (Assistant Provost; Director of CELT)
- Dr. Elizabeth Hewitt (Associate Professor in the Department of Technology and Society (DTS) in the College of Engineering and Applied Sciences)
- Chris Kretz (Associate Librarian and Head of Academic Engagement at SBU Libraries)
- Prof. Rajiv Lajmi (Assistant Professor in the School of Health Professions and Chair of Applied Health Informatics)
- Dr. Matthew Salzano (Assistant Professor in the Department of Communication in the School of Communication and Journalism)
You are cordially invited to attend the biweekly Brookhaven AI Mixer (BAM). BAM includes three short talks 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.
Tuesday, December 10, 2024, 12:00 pm -- CDS, Bldg. 725, Training Room
Speakers
Esther Tsai, CFN
Yugang Zhang, CFN
Sanket Jantre, CDS
Join Zoom Meeting
https://bnl.zoomgov.com/j/
Meeting ID: 161 176 4217
Passcode: 855752