AI Innovation Institute

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Meeting ID: 919 4522 7869
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George Em Karniadakis received his SM and PhD from Massachusetts Institute of Technology. He was appointed lecturer in the Department of Mechanical Engineering at MIT in 1987 and subsequently he joined the Center for Turbulence Research at Stanford/Nasa Ames. He joined Princeton University as assistant professor in the Department of Mechanical and Aerospace Engineering and as associate faculty in the program of applied and computational mathematics. He was a visiting professor at Caltech in 1993 in the Aeronautics Department and joined Brown University as associate professor of applied mathematics in the Center for Fluid Mechanics in 1994. After becoming a full professor in 1996, he continues to be a visiting professor and senior lecturer of Ocean/Mechanical Engineering at MIT. He is an AAAS fellow (2018), fellow of the Society for Industrial and Applied Mathematics (2010), fellow of the American Physical Society (2004), fellow of the American Society of Mechanical Engineers (2003) and associate fellow of the American Institute of Aeronautics and Astronautics (2006). He received the Alexander von Humboldt award in 2017, the Ralf E Kleinman award (2015), the J. Tinsley Oden Medal (2013), and the CFD award (2007) from the US Association in Computational Mechanics. His h-index is 103, and he has been cited over 52,000 times.


Abstract:
Karniadakis will present a new approach to develop a data-driven, learning-based framework for predicting outcomes of physical and biological systems, governed by PDEs, and for discovering hidden physics from noisy data. He will introduce a deep learning approach based on neural networks (NNs) and generative adversarial networks (GANs). He will also introduce new NNs that learn functionals and nonlinear operators from functions and corresponding responses for system identification. Unlike other approaches that rely on big data, here we learn from small data by exploiting the information provided by the physical conservation laws, which are used to obtain informative priors or regularize the neural networks. He will demonstrate the power of PINNs for several inverse problems in fluid mechanics, solid mechanics and biomedicine including wake flows, shock tube problems, material characterization, brain aneurysms, etc., where traditional methods fail due to lack of boundary and initial conditions or material properties. He will also present a new NN, DeepM&Mnet, which uses DeepOnets as building blocks for multiphysics problems, and he will demonstrate its unique capability in a 7-field hypersonics application.  

To register and for more information, click here.  

Abstract: Spectroscopy and imaging are two primary tools for probing material structures. However, the discovery of trends that guide the design of improved materials is often hindered by intertwined physical interactions or significant experimental noise. In this talk, I will present machine learning approaches that address both challenges. The first part focuses on the interpretation of X-ray absorption spectroscopy (XAS). We developed a controlled projection algorithm, RankAAE, which disentangles coupled structural descriptors in complex datasets and reveals analysis rules for inferring new structural information visually from spectra. The second part targets transmission electron microscopy (TEM) imaging of material structures. We developed a machine learning model capable of denoising extremely noisy images, while demonstrating strong out-of-distribution generalization. I will describe the construction of these models and demonstrate their effectiveness through representative scientific case studies.

Bio: Dr. Xiaohui Qu is a Staff Scientist in the Theory and Computation Group at the Center for Functional Nanomaterials (CFN), Brookhaven National Laboratory. His research focuses on developing interpretable machine learning and data analytics methods for materials science, with an emphasis on extracting structural insights from X-ray absorption spectroscopy and transmission electron microscopy. Dr. Qu earned his B.S. in Environmental Engineering and Ph.D. in Environmental Science from Shandong University, China, followed by postdoctoral research in Physics at Nanyang Technological University, Singapore, in Chemistry at Universidade Nova de Lisboa, Portugal, and in Materials at Lawrence Berkeley National Laboratory.

Location: IACS Seminar Room


Event Details & Calendar Link (includes zoom info): https://calendar.stonybrook.edu/site/iacs/event/iacs-seminar-speaker--xiaohui-qu-brookhaven-national-lab/

Synthetic Dreams and Ghost Machines

Dr. Steven Skiena will join the Cinema for a presentation exploring the rapidly evolving worlds of artificial intelligence and robotics.

Dr. Skiena is Distinguished Teaching Professor of Computer Science and Associate Director of the AI Innovation Institute at Stony Brook University. A leading researcher in data science and algorithms, he is the author of several influential books on AI and computation, including The Algorithm Design Manual and The Data Science Design Manual.

The presentation will be followed by a screening of Ex Machina, Alex Garland's provocative science-fiction thriller exploring the seductive and unsettling boundaries between human consciousness and artificial intelligence.

University Libraries Present: AI as Author? New Considerations When Evaluating Sources.
In this workshop, librarian Christine Fena will review some ways AI is being integrated into published work within the worlds of news and scholarly publication, and discuss how this might impact how to evaluate and understand sources during the research process.
10/2 12:30-1:30 pm on Zoom.
Register via link: https://stonybrook.campuslabs.com/engage/event/10460202

Talk Title: Knowledge-enhanced LLMs and Human-AI Collaboration Frameworks for Creativity Support

Abstract:

Large language models (LLMs) constitute a paradigm shift in Natural Language Processing and Artificial Intelligence. To build AI systems that are human-centered, I propose we need knowledge-aware models and human-AI collaboration frameworks to help them solve tasks ultimately aligning these models better with human values. In this talk, I will discuss my research agenda for human-centered AI with a case study on creativity that focuses on how to augment LMs with external knowledge, build effective human-AI collaboration frameworks as well as theoretically grounded robust evaluation protocols for measuring capabilities of NLG systems. I will begin by describing knowledge-enhanced methods for creative text generation such as metaphors. Next, I will describe how content creators can collaborate and benefit from the creative capabilities of text-to-image-based AI models. Finally, I will focus on the design and development of theoretically grounded evaluation protocols to benchmark the creative capabilities of Large Language Models in both producing as well as assessing creative text. I will end this talk by highlighting the current limitations of existing models and future directions toward building better models that will enable efficient and trustworthy human-AI collaboration systems.

Bio:

Tuhin Chakrabarty is a final-year Ph.D. candidate in the Natural Language Processing group within the Computer Science department at Columbia University. His research is supported by the Columbia Center of Artificial Intelligence & Technology (CAIT) & an Amazon Science Ph.D. Fellowship. He was also a Computational Journalism fellow at NYTimes R&D and an intern at the Allen Institute of Artificial Intelligence, Salesforce Research, and Deepmind. His research interests are broadly in Natural Language Processing, Computer Vision, and Human-Computer Interaction with a special focus on Human-Centered Methods for Understanding, Generation, and Evaluation of Creativity. His work has been recognized at top natural language processing and human-computer interaction conferences and journals such as ACL, NAACL, EMNLP, TACL, and CHI. He has been involved in organizing several workshops and tutorials at NLP conferences such Figurative Language Processing workshop at EMNLP 2022, NAACL 2024, and the tutorial on Creative Text Generation at EMNLP 2023. His work on AI and creativity has been mentioned in mainstream news media such as The Hollywood Reporter and more recently The Washington Post.

Join Zoom Meeting https://stonybrook.zoom.us/j/97103601583?pwd=TnpGMXdpeEd1N0hZcXppS1BLNFJhZz09 (ID: 97103601583, passcode: 004031) Join by phone (US) +1 646-931-3860 (passcode: 004031) Joining instructions: https://www.google.com/url?q=https://applications.zoom.us/addon/invitation/detail?meetingUuid%3DILacj94mRvSXgTYt0Cqs1w%253D%253D%26signature%3D9f2f1e7e603bbcb9034724d084eea8846c19a38b7436180170dfc3f1d718b425%26v%3D1&sa=D&source=calendar&usg=AOvVaw3MsNgLSPMRl8L5i6BosYrB Meeting host: H.Andrew.Schwartz@stonybrook.edu

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The Natural Language Processing Reading Group at Stony Brook University meets weekly to discuss recent research papers in NLP and related fields.
Join the Google Group here.

Title: Don't Just Fix it in Post: A Science of AI Must Study Training Dynamics

Abstract: What would it mean to have a scientific understanding of AI? Models are not static objects: they are snapshots of time-evolving processes shaped by data, objectives, architectures, and optimization dynamics. Yet much of AI research treats models as fixed artifacts, analyzing behaviors after training rather than asking why they emerge. This position paper argues that a science of AI must move beyond post hoc fixes and study the training dynamics that produce model behavior. Such a science should support progressively stronger forms of understanding: predicting outcomes from early training signals, intervening when trajectories go wrong, and ultimately designing training procedures that more reliably produce desired properties. Scaling laws have made prediction routine for loss; the challenge is extending this success to capabilities, biases, robustness, and safety-relevant behaviors. We articulate requirements for such theories grounded in the history and philosophy of science, examine progress in mechanistic interpretability, fairness, memorization, and simplicity bias, and identify concrete open problems.

Location: NCS 220


Zoom Link: https://stonybrook.zoom.us/j/92942833294?pwd=927683

Topic: AI Seminar: Stanley Bak
Time: Monday Nov 1, 2021 12:00 PM Eastern Time (US and Canada)

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Abstract: The field of formal verification has traditionally looked at proving properties about finite state machines or software programs. The surge in deep learning has been accompanied by a surge of progress in trying to apply mathematical and algorithmic techniques to prove things about the function being computed by a neural network.

This talk formalizes the neural network verification problem and describes technical methods for neural network verification based on reachability analysis. Improvements to analysis efficiency will be given, as well as research directions for further exploration. We also include an objective comparison performed this last summer trying to evaluate the best existing verification methods in terms of speed and network size. The competition was performed on common hardware and involved the participation of twelve international teams (the tool authors) on a common set of benchmarks. 

Biography: Stanley Bak is an assistant professor in the Department of Computer Science at Stony Brook University investigating the verification of autonomy, cyber-physical systems, and neural networks. He strives to develop practical formal methods that are both scalable and useful, which demands developing new theory, programming efficient tools and building experimental systems.
Stanley Bak received a Bachelor's degree in Computer Science from Rensselaer Polytechnic Institute (RPI) in 2007 (summa cum laude), and a Master's degree in Computer Science from the University of Illinois at Urbana-Champaign (UIUC) in 2009. He completed his PhD from the Department of Computer Science at UIUC in 2013. He received the Founders Award of Excellence for his undergraduate research at RPI in 2004, the Debra and Ira Cohen Graduate Fellowship from UIUC twice, in 2008 and 2009, and was awarded the Science, Mathematics and Research for Transformation (SMART) Scholarship from 2009 to 2013. From 2013 to 2018, Stanley was a Research Computer Scientist at the US Air Force Research Lab (AFRL), both in the Information Directorate in Rome, NY, and in the Aerospace Systems Directorate in Dayton, OH. He helped run Safe Sky Analytics, a research consulting company investigating verification and autonomous systems, and performed teaching at Georgetown University before joining Stony Brook University as an assistant professor in Fall 2020.