AI Innovation Institute

Abstract: Materials used in extreme environments, such as high temperatures, irradiation, and stress, often fail due to rapid defect generation and microstructural evolution, and traditional approaches cannot explore the vast design space needed for next-generation alloys. I will present a machine learning framework powered by massive computing that links individual atomic motion to microstructural evolution. Neural network kinetics models trained on first-principles data map vacancy barrier spectra and capture correlated diffusion in multicomponent alloys, revealing design strategies to suppress radiation damage. At larger scales, simulations uncover dislocation patterning and distinguish between confined and extended slip bands, offering new insight into collective dislocation motion and deformation instabilities. By integrating AI-driven modeling, large-scale computing, and experimental validation, my research goal is to accelerate the discovery of damage-tolerant materials and advance fundamental understanding of defect physics in extreme environments.

Speaker Bio: Penghui Cao is an Associate Professor in Mechanical and Aerospace Engineering at the University of California, Irvine, with a joint appointment in Materials Science and Engineering. He received his PhD in mechanical engineering from Boston University and subsequently worked as a Postdoctoral Associate in the Department of Nuclear Science and Engineering at the Massachusetts Institute of Technology from 2014 to 2018. Dr. Cao's research focuses on understanding the fundamental mechanisms that govern radiation responses and microstructure evolution in materials, and on developing advanced alloys for high-performance nuclear energy systems. His lab advances computational and modeling algorithms, integrates advanced manufacturing techniques to tailor microstructures, and leverages state-of-the-art electron microscopy to characterize and assess underlying mechanisms. He is the recipient of the DOE Early Career Research Program Award and the UCI Samueli School's Mid-Career Award for Faculty Excellence in Research.

Location: Institute for Advanced Computational Science, Seminar Room

*This seminar will be held in-person and online. Zoom link below*

Join Zoom Meeting: https://stonybrook.zoom.us/j/96410717491?pwd=3WGMwbLYNMSbI2IF160VXkvv2JmCQ1.1

Meeting ID: 964 1071 7491
Passcode: 399333

Abstract:

Conventional approaches to scientific discovery often prioritize building larger sensors, gathering more data, and scaling up computational power. In this talk, I will present a complementary perspective: extracting insights hidden in the data we already have. The key lies in using AI not as a black-box predictor, but as a tool for interpreting data through its underlying physical process.

I will demonstrate how AI, when integrated with the physics of light propagation, can serve as a computational lens to overcome fundamental limitations in fields ranging from biomedicine to astrophysics. Specifically, I will showcase two compelling applications: non-invasive imaging through scattering biological tissues, and detecting faint exoplanets against the overwhelming brightness of their host stars.

These methods represent a departure from traditional learning-based approaches that rely on fitting models to training labels and hoping for generalization. Instead, with physics-informed strategies that decode how light propagates, we can transform raw measurements into scientifically meaningful insights--without requiring costly hardware upgrades or human-annotated datasets. Finally, I will outline future directions for combining AI with physical principles, enabling us to unlock more phenomena once considered hidden and accelerating discoveries in healthcare, astronomy, and beyond.

Short Bio:

Brandon Y. Feng is a Postdoctoral Associate at MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and a Visiting Scientist at the Harvard-Smithsonian Center for Astrophysics. His research bridges artificial intelligence and physics to expand the limits of human and machine vision. He develops AI-driven methods that reveal hidden patterns in complex visual data, driving breakthroughs in areas such as exoplanet detection and imaging through scattering tissues. His work has been published in top venues, including Science Advances, CVPR, ICCV, ECCV, and NeurIPS, and has been featured in Science.org, New Scientist, and Phys.org. He holds a Ph.D. in Computer Science from the University of Maryland, along with a B.A. in Computer Science and Statistics and an M.S. in Statistics from the University of Virginia.

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.

Abstract: Two-dimensional (2D) materials such as graphene, hBN, and TMDs offer atomically sharp interfaces and unprecedented tunability when vertically assembled into van der Waals heterostructures. These stacks have enabled discoveries ranging from moiré superconductivity and correlated insulators to quantum emitters and next-generation nanoelectronic devices. Yet constructing high-quality heterostructures remains largely artisanal: researchers manually identify exfoliated flakes, align a polymer stamp by eye, and finely adjust temperature and contact geometry through tacit skill. This manual workflow is difficult to reproduce, scales poorly, and prevents systematic exploration of the enormous combinatorial space of materials, twist angles, and interfacial conditions. AutoLab is an autonomous platform that translates this tacit human expertise into programmable, feedback-driven control. Instead of pressing flakes with predefined trajectories, AutoLab uses machine vision to detect polymer-wafer contact, dynamically regulates contact evolution through closed-loop actuation and temperature control, and captures high-quality flakes with the cleanliness and precision of expert manual fabrication. The system integrates perception, decision making, and motion planning into a single robotic framework, enabling reproducible stacking, wafer-level coverage, and accelerated discovery. Beyond 2D materials, AutoLab illustrates a broader paradigm for AI-native scientific automation: codifying human experimental reasoning into algorithms that interrogate data in real time, adaptively adjust instrumentation, and generate scalable, high-fidelity datasets. Such platforms could generalize to diverse research domains--quantum device fabrication, optical alignment, surface science, autonomous microscopy, and other workflows where expert intuition currently limits throughput and reproducibility. By bridging artisanal manipulation and robotic autonomy, AutoLab points toward a future where scientific discovery is accelerated by machines that not only execute instructions, but learn, respond, and collaborate with human scientists.

Biography: Dr. Yutao Li is a research associate from Department of Condensed Matter Physics and Material Science, Brookhaven National Laboratory. He has 8 years of experience in 2D material sample fabrication, and investigation in their electronic transport, optical and mechanical properties.

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.

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

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/

The SUNY AI Symposium brings together AI experts from across the state, in Western New York and around the country.

This two-day event showcases AI thought leaders, SUNY researchers, students and companies of all sizes who leverage AI to produce positive outcomes--with scientific discovery, business innovation and economic impact. Come curious, explore the fascinating world of AI and leave with connections to those at the forefront of innovation.

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

Speakers

Sanket Jantre
Tao Zhang
Xi Yu

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

Meeting ID: 161 528 9117
Passcode: 991382