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

18th Annual Engineering Ball Flowerfield, St. James, NY Thursday April, 2nd, 7:00 to 10:00 pm Pick up your tickets in 231 Engineering (Monday - Friday, 10:00 am to 4 pm) Presenting Partner: L3Harris
Abstract: Large language models are prone to memorizing some of their training data. Memorized (and possibly sensitive) samples can then be extracted at generation time by adversarial or benign users. There is hope that model alignment---a standard training process that tunes a model to harmlessly follow user instructions---would mitigate the risk of extraction. However, we develop two novel attacks that undo a language model's alignment and recover thousands of training examples from popular proprietary aligned models such as OpenAI's ChatGPT. Our work highlights the limitations of existing safeguards to prevent training data leakage in production language models.

Speaker: Pegah Alipoormolabashi

Location: CS2311
The Renaissance School Of Medicine Department of Scientific Affairs and its Single Cell Genomics facility are excited to host a special seminar and discussion on AI and single cell genomics analysis:

With the decreasing cost of sequencing, many biobanks and large research cohorts have moved to whole genome sequencing (WGS) and single-cell RNA-seq. However, making use of this deluge of data remains a challenge. I will discuss statistical and deep learning approaches that we are exploring to address the challenge of noncoding variant interpretation, including our work as part of the Alzheimer's disease sequencing project.

Speaker: David A. Knowles, PhD. Asst. Professor of Computer Science, Interdisciplinary Appointee in Systems Biology, Columbia University Core Faculty Member, New York Genome Center

Join us in person: Health Science Tower Level 3, Lecture Hall 5
Making sense of Twitter @ Bloomberg presented by Daniel Preotiuc-Pietro

ABSTRACT: The Bloomberg Terminal has provided ways for investors and journalists to sift through and understand the immense volume of tweets and discover financially-relevant content ever since the SEC approved the use of Twitter for company disclosures back in 2013.

In the first part of the talk, I will showcase how tweets impact financial markets and how Bloomberg is using Natural Language Processing methods to identify financially relevant tweets that move the markets. Our processing pipeline feeds directly to clients, journalists in the newsroom and powers several news analytic products offered by the company including trending companies and consumer sentiment for publicly traded equities.

However, understanding user pragmatic intent in individual tweets would allow us to gain deeper insights and enable new applications. I will present several recent research studies focused on understanding intent including identifying complaints and the roles with which vulgarity is used in social media and how these can help improve applications such as sentiment analysis and hate speech detection.

BIO: Daniel Preotiuc-Pietro is a Senior Research Engineer and Team Lead at Bloomberg LP, where he works on analyzing and building models for real-world large scale social media and news mining and information extraction. His research interests are focused on understanding the social and temporal aspects of text, especially from social media, with applications in domains such as Social Psychology, Law, Political Science and Journalism. Several of his research studies were featured in popular press including the Washington Post, BBC, New Scientist, Scientific American or FiveThirtyEight. He is a co-organizer of the Natural Legal Language Processing workshop series. Prior to joining Bloomberg LP, Daniel was a postdoctoral researcher at the University of Pennsylvania with the interdisciplinary World Well Being Project and obtained his PhD in Natural Language Processing and Machine Learning at the University of Sheffield, UK.
AI Seminar: Video Architecture Search - Michael Ryoo Abstract: Video understanding is a challenging problem. Because a video contains spatio-temporal data, its feature representation is required to abstract both appearance and motion information. This is not only essential for automated understanding of the semantic content of videos, such as Web-video classification or sport activity recognition, but is also crucial for robot perception and learning. Previously, convolutional neural networks (CNNs) for videos were normally built by manually extending known 2D architectures such as Inception and ResNet to 3D or by carefully designing two-stream CNN architectures that fuse together both appearance and motion information. However, designing an optimal video architecture to best take advantage of spatio-temporal information in videos still remains an open problem. In this talk, we discuss recent progress in neural architecture search for videos, obtaining more optimal network architectures for video understanding.
Abstract: Sea ice is crucial to Earth's climate, Arctic communities, and ecosystems, yet climate change is driving significant losses, threatening polar stability. Quantifying the long-term impacts of a declining sea ice cover requires tools which improve climate-timescale prediction and bring new understanding of climate interactions. In this talk, I discuss how meeting this challenge requires a multi-disciplinary approach. Climate models, while essential, suffer from systematic biases due to missing or inaccurate physics, leading to uncertainty in future projections. I show how data assimilation (DA) offers a statistical framework for integrating satellite observations with climate models to quantify systematic sea ice model errors. Using convolutional neural networks (CNNs), we can learn these errors based on the model's atmospheric, oceanic, and sea ice conditions--what I term a state-dependent representation of the error. This approach enables real-time corrections to subsequent model simulations, which systematically reduces global sea ice biases. I highlight key successes and challenges in developing this hybrid ML+climate modeling framework, including transfer learning to enhance online generalization of ML models, and new methods for integrating Python-based ML frameworks with Fortran climate model code. Finally, I introduce GPSat, a scalable Gaussian process-based tool for reconstructing complete sea ice fields from sparse satellite altimetry data. Together, the DA+ML framework and GPSat offer future opportunities for improving targeted model physics errors for more robust climate simulation.


IACS Seminar Speaker: William Gregory, Princeton University

Location: IACS Seminar Room




https://stonybrook.zoom.us/j/91775729097pwd=Qlc5Nks0NmlyKzJwMjR0S0hrdVZ3QT09

Meeting ID: 917 7572 9097
Passcode: 555459


Abstract: As the saying goes, there are many ways to skin a cat.
While we don't want to go around skinning cats, the world of
optimization is rich with different problems, problem formulations,
and methods and approaches, each with different guarantees and
computational benefits. In this talk we will take a tour down the
problem of structured sparsity in sensing to see how one simple
problem can inspire a wide range of analysis and tools. First, I will
present the optimality conditions for a generalized structured sparse
problem, which can be geometrically visualized as alignment of vectors
and matrices. Then I will introduce three approximation methods for
the problem of phase retrieval, which are a twist on stochastic
gradient and coordinate descent methods. These methods leverage
fundamental numerical linear algebra concepts to give fast approximate
solutions to large-scale problems, which then after postprocessing can
produce more reliable sensing results.

Bio: Yifan Sun received her PhD in Electrical Engineering from the
University of California Los Angeles in 2015, with research focusing
on convex optimization and semidefinite programming. She was then
Technicolor Research and Innovation, focusing on machine learning and
data science applications. More recently, she completed two postdocs,
at the University of British Columbia in Vancouver, Canada and
L'Institut National de Recherche en Informatique et Automatique
(INRIA) in Paris, France.
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
The Provost's Lecture Series features talks by SUNY Distinguished Academy faculty members at Stony Brook University, showcasing the outstanding research and scholarship that is taking place at our institution.

Joe Mitchell

SUNY Distinguished Professor, Applied Mathematics and Statistics
Chair, Department of Applied Mathematics and Statistics, College of Engineering and Applied Sciences

A Case for Algorithms: A Computational Geometer's Perspective

Algorithms are all around us in every smart device and technology that has consumed our daily lives. As a computational geometer, I study algorithms to solve problems that involve a geometric perspective on data. I have observed that practically every technology and field of study has a need for effective algorithms involving geometric data. I reflect on some favorite algorithmic problems that are easy to visualize, but challenging to solve, and argue that the formal study of algorithms remains essential in the age of AI.

Reception to follow immediately after the talks.

Register here.