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Join us as we celebrate this year's Brook & Beyond Challenge finalists.
The Office for Research and Innovation invites you to hear about the two-month journey in which the Brook & Beyond team supported eight cohorts in bringing their bold ideas from the lab to the marketplace. It's an energizing evening that highlights the collaboration, creativity, and entrepreneurial spirit driving discovery across the University.
Meet this year's award recipients, hear pitches from the emerging founders, and applaud their achievements.
Connect, celebrate, and be part of the momentum shaping the future of innovation at
Stony Brook University.
Refreshments will be served. Registration is required.
Register Here.
Abstract: Modern decision-making increasingly relies on complex data, imperfect models, and limited domain expertise--yet decisions must still be made with confidence and accountability. This talk presents a research perspective on visual analytics as a bridge between data, models, and human judgment. Through three case studies spanning public-health risk analysis, multivariate scientific visualization, and causal model auditing with large language models, I will show how interactive visualization can reveal structure in high-dimensional data, support reasoning under uncertainty, and help humans critically assess both statistical and AI-generated explanations. Together, these examples illustrate how visual analytics enables users not only to explore data, but to form, challenge, and refine beliefs that underpin scientific and societal decisions.

Bio: Klaus Mueller received his Ph.D. in Computer Science from The Ohio State University in 1998. He is a Professor in the Department of Computer Science at Stony Brook University and a Senior Scientist at the Computational Science Initiative at Brookhaven National Laboratory. He currently serves as the Acting Chair of the Department of Technology and Society at Stony Brook. From 2012 to 2015, he was the Founding Chair of the Computer Science Department at SUNY Korea, where he also served as Vice President for Academic Affairs and Finance for two years.
His research interests span visual analytics, explainable AI, machine learning and data science, human-centered responsible AI, fairness, belief modeling and personalized communication, virtual and augmented reality, and computational and medical imaging. Dr. Mueller received the U.S. National Science Foundation Early Career Award in 2001, the SUNY Chancellor's Award for Excellence in Scholarship and Creative Activity in 2011, and the Meritorious Service Certificate and Golden Core Award of the IEEE Computer Society in 2016. In 2018, he was inducted into the U.S. National Academy of Inventors.
To date, he has authored more than 300 peer-reviewed journal and conference papers, which have been cited over 15,000 times. He is a frequent speaker at international conferences, has organized or participated in 18 tutorials, chaired the IEEE Visualization Conference in 2009, served as elected Chair of the IEEE Technical Committee on Visualization and Computer Graphics (VGTC) from 2012-2015, and was Editor-in-Chief of IEEE Transactions on Visualization and Computer Graphics from 2019-2022. He is a Fellow of the IEEE.

Location: NCS 120

Please join us this Friday, February 13th for the CSE 600 seminar given by Associate Professor Debswapna Bhattacharya, from the Department of Computer Science at Virginia Tech.

Abstract: Building a model of a biological system that can provide actionable hypotheses to form a solid foundation for experimental and theoretical analyses is one of the key challenges in biology and medicine. In this talk, I will present my group's ongoing work in developing, evaluating, and disseminating a new generation of computational methods for biomolecular modeling powered by artificial intelligence (AI) and machine learning (ML). First, I will introduce a new generation of AI/ML methods for improved modeling and characterization of protein-nucleic acid assemblies by deep graph learning using embeddings from biological large language models (LLMs) as well as geometric attention-enabled pairing of heterogeneous biological LLMs, a previously unexplored avenue. Then, I will present a novel generative deep learning model based on equivariant flow matching for end-to-end generation of all-atom RNA 3D structural ensemble. Finally, I will outline my future research directions on attaining atomic-level accuracy in computational modeling of biomolecules and their assemblies at scale.

Speaker: Debswapna Bhattacharya is an Associate Professor in the Department of Computer Science at Virginia Tech. He received his Ph.D. in Computer Science from the University of Missouri-Columbia in 2016. Before joining Virginia Tech in 2022, he was an Assistant Professor at Auburn University from 2017 to 2021. His research interests lie at the intersection of computational biology and machine learning, with a particular focus on artificial intelligence for computational structural biology, specifically in modeling and characterization of biomolecular structures and interactions. His research group has been developing novel computational and data-driven methods, software, and information systems for diverse biomolecular modeling problems, ranking among the best methods in community-wide blind assessments and serving the worldwide community of biomedical users. He received various research awards (NSF CAREER Award, NIH Maximizing Investigators' Research Award, NSF National AI Research Resource Award) and numerous institutional honors (National Distinction and Outstanding Contributor at Virginia Tech, Ginn Faculty Fellowship at Auburn University, Outstanding Engineering Faculty Award at Auburn University).
Location: NCS 120

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


Reception to follow.

Abstract:
In this talk, I will present our journey of developing diverse, adaptive, uncertainty-calibrated AI planning agents that can robustly communicate and collaborate for multi-agent reasoning (on math, commonsense, coding, etc.) as well as for interpretable, controllable multimodal generation (across text, images, videos, audio, layouts, etc.). In the first part, we will discuss improving reasoning via multi-agent discussion among diverse LLMs and structured distillation of these discussion graphs (ReConcile, MAGDi), adaptively learning to balance abstraction, decomposition, refinement, and fast+slow thinking in LLM-agent reasoning (ReGAL, ADaPT, MAgICoRe, System-1.x), as well as confidence calibration in LLMs via speaker-listener pragmatic reasoning and making LLMs better teammates via multi-agent positive-negative persuasion balancing (LACIE, PBT). In the second part, we will discuss interpretable and control-lable multimodal generation via LLM-agents based planning and programming, such as layout-controllable image generation (and evaluation) via visual programming (VPGen+VPEval), consistent multi-scene video generation via LLM-guided planning (VideoDirectorGPT), interactive and composable any-to-any multimodal generation (CoDi, CoDi-2), as well as feedback-driven multi-agent interaction for adaptive environment/data generation via weakness discovery (EnvGen, DataEnvGym).
Bio:
Dr. Mohit Bansal is the John R. & Louise S. Parker Distinguished Professor and the Director of the MURGe-Lab (UNC-NLP Group) in the Computer Science department at UNC Chapel Hill. He received his PhD from UC Berkeley in 2013 and his BTech from IIT Kanpur in 2008. His research expertise is in natural language processing and multimodal machine learning, with a particular focus on multimodal generative models, grounded and embodied semantics, faithful language generation, and interpretable, efficient, and generalizable deep learning.
Abstract: Astronomers slowly made sense of the cosmos by following the stars night after night. I suggest we examine human identity in a similar way. Let's observe the words individuals use to describe themselves day after day. In this presentation, I will introduce ipseology - a new approach to studying human selves. Ipseology is the systematic, empirical study of ipseity: selfhood, individuality and the elements of identity. The primary idea is that we can learn a lot about people from their self-authored self-descriptions - especially if we follow their revisions over time. I will discuss results from sampling millions of social media bios over more than a decade and present new approaches for observation in the Post-API age.

Bio: Dr. Jason Jeffrey Jones is a computational social scientist whose expertise includes online experiments, social networks, high-throughput text analysis and machine learning. He is interested in humans' perceptions of themselves and the developing role of artificial intelligence in society.

Dr. Jones is the director of CSSERG (pronounced sea surge): the Computational Social Science of Emerging Realities Group. CSSERG is a team of scholars committed to cross-disciplinary collaboration, united by common computational methodologies and always with eyes on the near future. CSSERG has studied the effectiveness of virtual reality in evoking empathy, the dynamics of gender stereotypes in language over decades and temporal trends in personally expressed identity.

This seminar will take place in person and online (zoom link below):

Join Zoom Meeting
https://stonybrook.zoom.us/j/93686609778?pwd=KdHVyIbU3ymML6hTchXsm6JLYKLSru.1

Meeting ID: 936 8660 9778
Passcode: 638699