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. 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.

We meet every other Tuesday at noon in CDSD's Training Room (building 725, room 2-124) 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.

In addition to our speaker, we will have a number of CDS staff in attendance with expertise in AI methods and applications including image analysis, foundation models development, and inverse problem solving.

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

Please Note: Due to a funding shortfall, we are for the time being no longer able to provide pizza and sodas for these events. We will have coffee though, and all are of course welcome to bring their lunch.

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. 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.

We meet every other Tuesday at noon in CDSD's Training Room (building 725, room 2-124) 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.

In addition to our speaker, we will have a number of CDS staff in attendance with expertise in AI methods and applications including image analysis, foundation models development, and inverse problem solving.

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

Please Note: Due to a funding shortfall, we are for the time being no longer able to provide pizza and sodas for these events. We will have coffee though, and all are of course welcome to bring their lunch.

Research challenges in using computer vision in robotics systems Abstract The past decade has seen a remarkable increase in the level of performance of computer vision techniques, including with the introduction of effective deep learning techniques. Much of this progress is in the form of rapidly increasing performance on standard, curated datasets. However, translating these results into operational vision systems for robotics applications remains a formidable challenge. This talk with explore some of the fundamental questions at the boundary between computer vision and robotics that need to be addressed. This includes introspection/self-awareness of performance, anytime algorithms for computer vision, multi-hypothesis generation, rapid learning and adaptation. The discussion will be illustrated by examples from autonomous air and ground robots.
Discover how U.S. Census Bureau Tools can help you find free data for your research projects, community, and more. See how to access the latest American Community Survey and 2020 Census data for various geographies including New York City and Long Island at data.census.gov. Learn about Community Resilience Estimates and how to navigate My Community Explorer; an interactive map-based tool which highlights demographic and socioeconomic data that measure inequality. This session will involve live demonstrations and hands-on exercises for participants. Registrants will receive the Zoom link one day prior to the event.

Please Register for SBU Libraries' AI Club: Exploring Census Data here.
The 39th Annual AAAI Conference on Artificial Intelligence will be held from February 25 to March 4, 2025 in Philadelphia, Pennsylvania, USA. More information can be found here.










Abstract:
Quantifying similarity is a central notion in science and data analysis, pervading everything from phylogenetic trees to the foundation of clustering. Unfortunately, despite being examined and applied for decades, traditional similarity and distance metrics have fundamental drawbacks. The key problem is that all of them are only defined over pairs of objects, so they scale quadratically when one tries to compare N objects. The present explosion in the amount of data available to us requires new ways to process information, and while some current algorithms can handle millions of points, we need alternatives applicable to billions. This is what motivated us to develop a new framework that can compare any number of objects at the same time. With this, we achieve an unprecedented linear scaling when comparing multiple objects. Here we will discuss the main properties of this formalism, along with its applications in drug design and to the analysis of Molecular Dynamics (MD) simulations. Our indices have proven to be incredibly versatile when applied to chemical space exploration and visualization, allowing us to rigorously quantify the chemical diversity of very large molecular libraries. This has led to the creation of several algorithms to sample important regions in chemical space, including a more efficient way of identifying the prevalence of activity cliffs. Additionally, our indices provide a convenient route to sample complex MD trajectories, allowing to identify representative structures very efficiently. Moreover, we can also cluster biological ensembles in a more robust way than with standard algorithms, which has led to our group's work on MDANCE, a very flexible and efficient open-source clustering module. Drop by if you want to know how we clustered one billion molecules!


Speaker:
Assistant Professor, Department of Chemistry and Quantum Theory Project
University of Florida, Gainesville
Website: https://quintana.chem.ufl.edu/

Location:
Laufer Center Lecture Hall 101

Abstract: The development of embodied AI has largely focused on scaling data and computational power, often at the cost of energy efficiency. In contrast, biological intelligence achieves remarkable adaptability with minimal resources, inspiring a shift toward neuromorphic AI, an approach that mimics the structure and dynamics of biological neural systems. In this talk, I will explore the promises and challenges of neuromorphic computer vision from three key perspectives: algorithms, robot actions, and data. First, I will discuss algorithmic advances, including continuous visual hull reconstruction, continuous-time human motion field estimation, and unsupervised independent motion segmentation. Next, I will illustrate how neuromorphic vision enables agile robotic actions by leveraging event-based perception for real-time decision-making. Finally, I will address challenges in training data-driven models with event data, highlighting strategies to enhance data availability and efficiency. By integrating these elements, neuromorphic AI paves the way for energy-efficient, high-performance embodied intelligence in dynamic real-world environments.

Speaker Bio: Ziyun (Claude) Wang is a fifth-year Ph.D. student in the General Robotics, Automation, Sensing & Perception (GRASP) Lab at the University of Pennsylvania, advised by Professor Kostas Daniilidis. His research focuses on developing algorithms for neuromorphic computer vision and integrating them with real hardware to enable agile perception in embodied AI systems. Prior to his Ph.D., he worked at the Samsung AI Center New York, where he developed 3D reconstruction techniques for robotic applications and earned three patents. He also contributed to the Apple Vision Pro team, enhancing user comfort for AR glasses. His research work has been recognized at major computer vision, robotics, and machine learning venues including the AAAI Conference on Artificial Intelligence (AAAI), European Conference on Computer Vision (ECCV), International Conference on Learning Representations (ICLR), Conference on Computer Vision and Pattern Recognition (CVPR) workshops, and IEEE Robotics and Automation Letters (R-AL), with an oral presentation at ECCV placing in the top 2.7%. His research aims to drive the development of next-generation bio-inspired AI systems, enabling more efficient, adaptive, and intelligent embodied perception.
CSE 656 Seminars in Computer Vision - Wednesdays 11:30am-12:50pm, Room NCS 120

The overall purpose of this seminar is to bring together people with interests in Computer Vision theory and techniques and to examine current research issues. This course will be appropriate for people who already took a Computer Vision graduate course or already had research experience in Computer Vision. To enroll in this course, you must either: (1) be in the PhD program or (2) receive permission from the instructors.

Each seminar will consist of multiple short talks (around 10 minutes) by multiple people. Students can register for 1 credit for CSE656. Registered students must attend and present a minimum of 2 or 3 talks. Everyone else is welcome to attend. Fill in https://forms.gle/pCVXovgfMfQwGqG38 to subscribe to our mailing list for further announcement.
Abstract: Language offers a uniquely powerful lens for understanding the mind: one that can access latent psychological realities often missed by traditional measurement tools. However, as language models expand their ability to capture semantics through context length, expansion into deeper levels of semantics is less explored, especially with respect to understanding cognitive patterns of authors. This dissertation proposes that we can uncover deeper cognitive and affective patterns that reflect more accurate underlying mental states by analyzing language at higher levels of discourse semantics and by modeling latent states.


First, the dissertation focuses on uncovering cognitive styles or thinking patterns manifesting in language. We demonstrate that modeling language at deeper semantic levels such as discourse relations, can unveil latent psychological states and traits, including cognitive styles that influence both mental health and behavior. Introducing a novel blend of transfer and active learning, we efficiently curated a new set of linguistic data on cognitive styles like dissonance. This approach allows for more precise measurement when dealing with rare-classes and low-resource tasks. As a second contribution, effective validation methods are introduced to language-based assessments of the underlying cognitive styles. Controlled behavioral experiments and online studies show that cognitive styles detected through linguistic signals reliably predict real-world behaviors such as decision-making and engagement with extremist communities, both at the individual and community levels, sometimes months in advance

The research further moves beyond traditional measurement tools like questionnaires and expert judgments, which rely on Classical Test Theory, by establishing that language-based assessments more closely approximate true psychological states. The mechanisms by which these assessments outperform standard tools are explained, highlighting their predictive power for behaviors linked to underlying traits. Finally, a more sophisticated approach is explored by modeling psychological outcomes with Item Response Theory (IRT), an improvement over Classical Test Theory. Adaptive language-based assessments are introduced, showing that targeted, adaptive testing based on latent IRT scores can efficiently and accurately capture multiple psychological dimensions.

Taken together, these contributions argue for a shift towards language-based psychological assessments. By integrating deeper discourse-level semantics with measurement theory, this dissertation charts a path towards truer scores of mental states: ones that are more precise, and reflective of the complexity of human cognition and emotions.

Speaker: Vasudha Varadarajan

https://stonybrook.zoom.us/j/99180374682?pwd=w2zZTkQsfunrBZhHgEweR54NjKabZ2.1&jst=2