What can you learn from over seven years' worth of Twitter bios? Steven Skiena, Distinguished Teaching Professor of Computer Science and Director of SBU's Institute for AI-Driven Discovery and Innovation, will tell us.

Presenting work done with collaborators Jason Jones, Dakota Handzlik, and Xingzhi Guo, Dr. Skiena will discuss what the team learned about how people portray themselves on social media through their political identities and job status. He'll also show us what you can predict about a person based on their self-description.

If you have a disability and are requesting accommodations in order to fully participate in this event, please email libraryevents@stonybrook.edu or call 631-632-7100.

Register now: https://library.stonybrook.edu/library-events/stem-speaker-series-measuring-self-identity/

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.

Speakers

Kriti Chopra, Computing & Data Sciences (CDS)
Thomas Flynn, Computing & Data Sciences (CDS)
Wenjie Liao, Chemistry Division

Tuesday, January 7, 2025, 12:00 pm -- CDS, Bldg. 725, Training Room

Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1615289117?pwd=Hqkbj9itxWrFnkhZ8rQXHPInO2gxdF.1

Meeting ID: 161 528 9117
Passcode: 991382

Recently, large-scale language data combined with modern machine learning techniques have shown strong value as means for studying human psychology and behavior. For example, language alone has been shown predictive in mental health, personality, and health behaviors. However, many applications for such language-based assessments have readily available and important data beyond language (i.e. extra-linguistics), such as predicting the subjective well-being of a community using tweets, where one can take into account their age, education, and demographic attributes. Language may capture some characteristics while extra-linguistic variables captures others. We believe that effectively integrating linguistic and extra-linguistic data can yield benefits beyond either independently. In this thesis, we develop methods which effectively integrate extra-linguistic data with language data focused primarily on social scientific applications. The central challenge is dealing with the size and heterogeneity of, often sparse and noisy, language data versus the, often low-dimensional and non-sparse, extra-linguistic variables. First, we consider structured extra-linguistics, like socioeconomic (income and education rates) and demographics (age, gender, etc.), and propose two integration methods, named residualized controls (RC) and residualized factor adaptation (RFA), to be used in county-wise prediction tasks. Demonstrating techniques that integrate information at both the model-level and data-level, we found consistently strong improvement over naively combining features, for example, increasing county level well-being predictions by over 12%. Next, we consider unstructured extra-linguistic data. In the first part, we incorporate social network connections and language over time to propose a novel metric for quantifying the stickiness of words - their ability to spread across friendship connections in a social network over time (or in other words, stick in ones vocabulary after seeing friends use it). We obtain which language features are more probable to disseminate through friendship and show such a metric is useful for predicting who will be friends and what content will spread. In addition, we analyze language content over time by proposing a novel dynamic content-specific topic modeling technique that can help to identify different sub-domains of a thematic scope and can be used to track societal shifts in concerns or views over time.

Abstract: Generative visual models like Stable Diffusion and Sora generate photorealistic images and videos that are nearly indistinguishable from real ones to a naive observer. However, their grasp of the physical world remains an open question: Do they understand 3D geometry, light, and object interactions, or are they mere pixel parrots of their training data? Through systematic probing, I will demonstrate that these models surprisingly learn fundamental scene properties--intrinsic images such as surface normals, depth, albedo, and shading (à la Barrow & Tenenbaum, 1978)--without explicit supervision, which enables applications like image relighting. But I will also show that this knowledge is insufficient. Careful analysis reveals unexpected failures: inconsistent shadows, multiple vanishing points, and scenes that defy basic physics. All these findings suggest these models excel at local texture synthesis but struggle with global reasoning: a crucial gap between imitation and true understanding. I will then conclude by outlining a path toward generative world models that emulate global and counterfactual reasoning, causality, and physics.

Bio: Anand Bhattad is a Research Assistant Professor at the Toyota Technological Institute at Chicago. He earned his PhD from the University of Illinois Urbana-Champaign in 2024 under the mentorship of David Forsyth. His research interests lie at the intersection of computer vision and computer graphics, with a current focus on understanding the knowledge encoded in generative models. Anand has received Outstanding Reviewer honors at ICCV 2023 and CVPR 2021, and his CVPR 2022 paper was nominated for a Best Paper Award. He actively contributes to the research community by leading workshops at CVPR and ECCV, including Scholars and Big Models: How Can Academics Adapt? (CVPR 2023), CV 20/20: A Retrospective Vision (CVPR 2024), Knowledge in Generative Models (ECCV 2024), and How to Stand Out in the Crowd? (CVPR 2025). For more details, visit https://anandbhattad.github.io/


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/1611764217?pwd=asNaXHDwGLnMr9hDv3L6zAcsQaN5FX.1

Meeting ID: 161 176 4217
Passcode: 855752


Imagine machines that can see beyond human limitations--drones locating hidden survivors, cameras predicting structural failures, or medical devices detecting tumors beneath the skin. Traditional vision systems are constrained by the boundaries of human perception, missing vast information present in light interactions. This talk explores the development of advanced vision systems that capture underutilized dimensions of light, model intricate light-scene interactions, and extract hidden 3D information--around corners, beneath surfaces, and at high speeds. By jointly developing novel imaging hardware, efficient rendering models, and physics-based learning algorithms, we aim to transcend conventional vision capabilities--unlocking critical applications in autonomous navigation, structural monitoring, and non-invasive medical imaging.

Speaker Bio:


Akshat Dave is a Postdoctoral Associate at MIT Media Lab in the Camera Culture group working with Prof. Ramesh Raskar. He received his Ph.D. from Rice University ECE Department in 2023 where he was advised by Prof. Ashok Veeraraghavan. His research lies at the intersection of applied optics, computer graphics, and computer vision. His research focuses on developing vision systems that go beyond human perception. His work has been recognized by Rice University's Best Thesis Award, OSA Best Paper Prize, and fellowships by Texas Instruments and Qualcomm.
Date: March 11, 2022
Time: 2:40PM EST

Title: Towards Scalable and Efficient Machine Learning as a Service (MLaaS)

Abstract:
Driven by the explosive growth of big data, the sustained advances of
Machine Learning (ML), and the fast evolving of computer system
techniques, the past few years have witnessed a surging demand for
Machine Learning as a Service (MLaaS). MLaaS is an emerging computing
paradigm that facilitates ML model design, training, inference serving
and provides optimized executions of ML tasks in an automated,
scalable, and efficient manner. In this talk, I will demonstrate how
to integrate ML algorithm research and system research in synergy to
address the pressing challenges in MLaaS. I will first share a story
about how our system experience led to a novel large batching
algorithm design that revolutionizes large-scale training. Then I will
tell another story about how our gradient compression algorithm
research helped us to discover overlooked critical features of modern
ML systems and thereby build a compression-aware distributed ML
system. I will also briefly discuss a promising future of harnessing
serverless computing for MLaaS model inference serving. I will
conclude my talk with a discussion of interdisciplinary research and
future plans.

Bio:
Dr. Feng Yan is an Assistant Professor of Computer Science and
Engineering at University of Nevada, Reno (UNR) and director of the
Intelligent Data and Systems Lab (IDS Lab). Dr. Yan received M.S. and
Ph.D. degrees in Computer Science from the College of William and Mary
and worked at Microsoft Research and HP Labs. Dr. Yan's research
bridges the fields of big data, machine learning, and systems. The
focus of his research is on developing methodologies and building
systems that are automated, high-performing, efficient, robust, and
user-centric. Some of his recent research topics include large-scale
distributed deep learning, machine learning as a service (MLaaS),
federated learning, AutoML, serverless computing, and broad topics in
cloud and HPC. Dr. Yan is also dedicated to interdisciplinary research
and has established fruitful collaborations with domain experts in
areas such as health, physics, geography, material science, mechanical
engineering, civil engineering, and innovated big data and AI-driven
approaches for these domains. Dr. Yan and his team are actively
publishing at the most prestigious venues in computer system area
(such as SOSP, SC, HPDC, USENIX ATC, EuroSys, FAST, VLDB, etc.) and
machine learning area (such as NIPS/NeurIPS, KDD, AAAI, etc.). Dr. Yan
and his students are the recipients of the Best Student Paper Award of
IEEE CLOUD 2018, the Best Paper Award of CLOUD 2019, and the Best
Student Paper Award of ITNG 2021. Dr. Yan is the recipient of the NSF
CAREER Award, the NSF CRII Award, the CSE Best Researcher Award, and
has been nominated for the Regents' Rising Researcher Award. Dr. Yan
serves as Social Media Chair of ACM SIGMETRICS. To learn more
information, please visit Dr. Yan's homepage:
https://www.cse.unr.edu/~fyan.
Join librarian Christine Fena for an interactive workshop that invites you to explore AI tools firsthand, not just as users, but as critical investigators. Through playful experimentation and collaborative discovery, you'll uncover inherent biases, probe algorithmic flaws, and gain a deeper understanding of AI's limitations and societal impacts.

Register for the Zoom workshop here.

Abstract: The landscape of machine learning evolves rapidly and the complexity of the networks and their architectures defies easy comprehension. AI is touted as the next scientific revolution by allowing the processing and pattern-finding in increasingly massive data sets. One potential end results could be AI enhanced measurement technologies, but what does that mean? This talk will give examples of how classical tools indicate the technical obstacles to this vision in terms of understanding training processes, model comparisons, and feature embeddings. While the results in this talk are largely empirical, they point to interesting directions for (infomation?) theoretical investigation.

Bio: Anand D. Sarwate is an Associate Professor in the Electrical and Computer Engineering Department at Rutgers, The State University of New Jersey. He received B.S. degrees in math and electrical engineering from MIT and a Ph.D. in electrical engineering from UC Berkeley. Prior to joining Rutgers he was a Research Assistant Professor at TTI-Chicago and a postdoc at the ITA Center at UC San Diego. His research interests include information theory, machine learning, signal processing, optimization, and privacy and security.
Location: Light Engineering 250