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.

Abstract: The increasing complexity and volume of data from electron microscopy necessitates advanced computational tools for timely and accurate analysis. In this talk, I will present several machine learning (ML) models developed to interpret diverse datasets from transmission electron microscopy (TEM). First, I demonstrate segmentation models for labelling regions of interest from in situ TEM images, such as atomic column positions or reaction sites that allow atomic-level quantitative analysis of data. Second, I introduce a self-supervised CNN model for denoising of low-dose HRTEM images, enabling clearer visualization of atomic features without sacrificing temporal resolution. Finally, a transformer-based model trained to predict copper oxidation states directly from their electron energy loss spectroscopy spectra will be introduced. Together, these projects showcase the power of tailored ML solutions to extract quantitative insights from complex microscopy data.

Biography: Brian Lee is a research associate working for the Electron Microscopy group and Theory and Computation group at the Center for Functional Nanomaterials. Previously, he has received PhD in Mechanical Engineering from Duke University and worked as a postdoc at Purdue University. His research focuses on applying machine learning and simulation techniques for materials science.

Location: CDS, Bldg. 725, Training Room

Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1604383624?pwd=ffQ5cUPNxTI7nzClKQO6cnsNbhF9Vf.1

Meeting ID: 160 438 3624
Passcode: 558449

Speaker: Gary Kazantsev (Head of Quant Technology Strategy in the Office of the CTO at Bloomberg)

 

Date/Time: Friday, October 15, 2021 10:00AM-11:00AM EST

 

Title: Machine Learning in Finance

Abstract: Machine learning is changing our world at an accelerating pace. In this talk we will discuss the recent developments in how machine learning and artificial intelligence are changing finance, from a perspective of a technology company which is a key  participant in the financial markets. We will give an overview and discuss the evolution of selected flagship Bloomberg ML and AI projects, such as sentiment analysis, question answering, social media analysis, information extraction and prediction of market impact of news stories. We will discuss practical issues in delivering production machine learning solutions to problems of finance, highlighting issues such as interpretability, privacy and nonstationarity. We will also discuss current research directions in machine learning for finance. We will conclude with a Q&A session.

Bio: (https://www.techatbloomberg.com/people/gary-kazantsev/) Gary is the Head of Quant Technology Strategy in the Office of the CTO at Bloomberg. Prior to taking on this role, he created and headed the company's Machine Learning Engineering group, leading projects at the intersection of computational linguistics, machine learning and finance, such as sentiment analysis of financial news, market impact indicators, statistical text classification, social media analytics, question answering, and predictive modeling of financial markets.

Prior to joining Bloomberg in 2007, Gary had earned degrees in physics, mathematics, and computer science from Boston University.

He is engaged in advisory roles with FinTech and Machine Learning startups and has worked at a variety of technology and academic organizations over the last 20 years. In addition to speaking regularly at industry and academic events around the globe, he is a member of the KDD Data Science + Journalism workshop program committee and the advisory board for the AI & Data Science in Trading conference series. He is also a co-organizer of the annual Machine Learning in Finance conference at Columbia University.


Join Zoom Meetinghttps://stonybrook.zoom.us/j/93374426887?pwd=cE9zeW51VXFEN2R0YnNPbHF1WFp0Zz09Meeting ID: 933 7442 6887Passcode: 330347One tap mobile+16468769923,,93374426887# US (New York)+13126266799,,93374426887# US (Chicago)Dial by your location +1 646 876 9923 US (New York) +1 312 626 6799 US (Chicago) +1 301 715 8592 US (Washington DC) +1 346 248 7799 US (Houston) +1 408 638 0968 US (San Jose) +1 669 900 6833 US (San Jose) +1 253 215 8782 US (Tacoma)Meeting ID: 933 7442 6887

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@stonybrook.edu.

Register.

Abstract: Many scientific and engineering challenges, such as the design of materials or molecules or the control of experimental systems, rely on the existence of fast predictive models that can evaluate potential designs or control policies. Traditionally this has been accomplished through numerical simulation; more recently data-driven machine learning methods have been applied. However, both approaches leave gaps: physical modeling can be accurate and extrapolates well to previously-unstudied conditions, but it is often computationally expensive and relies on physics approximations that may not be valid. Machine learning can generalize from massive amounts of real-world or simulation data, but suffers from physical grounding and extrapolation into new regimes, as well as in settings where large data sets do not exist.
In this talk I explore an intermediate regime, which is hybrid reduced order models: fast simplified physics approximations where some of the unknown or approximated equations are replaced with data-driven machine learning components. Examples include coarse-grained models where the full macroscopic equations cannot be derived from first-principles microscopic equations, multiscale models with unknown closure terms or sub-grid parameterization schemes, and low-order or latent dynamical systems that learn governing equations on a low-dimensional reduced state space. I discuss how such reduced systems can be identified from very limited data, much less than is often needed in traditional machine learning but at much lower time-to-solution than traditional numerical modeling. This facilitates not only system design and control but also uncertainty quantification approaches that search the space of possible equations for predictive models that can explain the data. I will focus on an example from materials science concerning the design of self-assembling block copolymer nanomaterials.

Speaker: Dr. Nathan Urban, Applied Mathematics Department, Brookhaven National Laboratory

Location: Laufer 101

Zoom: https://stonybrook.zoom.us/j/96090260834?pwd=mw8QTHbMOw9oeU9hazZeoq8bN4VIfH.1
Meeting ID: 960 9026 0834 Passcode: 374969

Abstract: The remarkable success of large foundational models, such as LLMs and diffusion models, is built on their learning over vast amounts of static data from the Internet. However, human learning and problem-solving are fundamentally interactive processes--humans learn by engaging with their environment, tools, search engine, and feedback loops, iteratively refining their understanding and decisions. This gap between the interactivity of human learning and the static nature of model training raises a critical question: how can we imbue foundational models with the capacity for meaningful interaction?

In this talk, I will explore methods to enhance foundational models by incorporating interaction with the external environment. I will discuss strategies such as leveraging external tools, compilers, function calls to provide dynamic feedback to enhance foundation models. By drawing inspiration from human's interactive learning processes, I demonstrate how interaction-driven learning can lead to models that are not only more accurate but also more adaptable to real-world applications.

This work bridges the gap between static training paradigms and the dynamic, iterative nature of human intelligence, paving the way for a new generation of interactive AI systems.

Bio: Wenhu Chen has been an assistant professor at the Computer Science Department in University of Waterloo and Vector Institute since 2022. He obtained the Canada CIFAR AI Chair Award in 2022 and CIFAR Catalyst Award in 2024. He has worked for Google Deepmind as a part-time research scientist since 2021. Before that, he obtained his PhD from the University of California, Santa Barbara under the supervision of William Wang and Xifeng Yan. His research interest lies in natural language processing, deep learning and multimodal learning. He aims to design models to handle complex reasoning scenarios like math problem-solving, structure knowledge grounding, etc. He is also interested in building more powerful multimodal models to bridge different modalities. He received the Area Chair Award in AACL 2023, the Best Paper Honorable Mention in WACV 2021, the Best Paper Finalist in CVPR 2024, and the UCSB CS Outstanding Dissertation Award in 2021.
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.
Towards Saving Lives with Natural Language Processing Andrew Schwartz Dept. of Computer Science Stony Brook Analyzing language use patterns is proving to be a valuable and unique approach to understanding the psychological, social, and health factors of people. On the individual level, Facebook and Twitter have been found predictive of mental health, personality, demographics, and occupational class (among others). At the community or county-level, Twitter has been found predictive of flu and allergy outbreaks, life satisfaction, atherosclerotic heart disease mortality, health behavioral risk factors, excessive drinking, and HIV prevalence. While these techniques have shown robust links over a plethora of important aspects of human life, it is not clear whether any lives have been saved, at least directly, by such work. At their core, some barriers to improving health care and saving lives are likely not NLP or even AI problems, but others are perhaps technical in nature and suggest changing the way we model data. This seminar will have two parts: a presentation and a discussion. I will start by going over recent and on-going work toward predicting mental health outcomes --- depression, addiction relapse, future psychological distress --- from human language use patterns. Then, I will present an imperfect vision of a future where NLP helps to save lives and open the floor for discussion of technical barriers and whether such a vision is practical. Biography: Andrew Schwartz received his PhD in Computer Science from the University of Central Florida in 2011 with research on acquiring lexical semantic knowledge from the Web. He then joined the University of Pennsylvania where he was a Postdoctoral Research Fellow and later Visiting Assistant Professor in Computer & Information Science. He is Lead Research Scientist for the World Well-Being Project, a multidisciplinary group of Computer Scientists and Psychologists studying physical and psychological well-being based on language in social media.