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: Monocular surface normal estimators trained on large-scale RGB-normal data often perform poorly in the edge cases of reflective, textureless, and dark surfaces. Polarization encodes surface orientation independently of texture and albedo, offering a physics-based complement for these cases. Existing polarization methods, however, require multi-view capture or specialized training data, limiting generalization. We introduce Poppy, a training-free framework that refines normals from any frozen RGB backbone using single-shot polarization measurements at test time. Keeping backbone weights frozen, Poppy optimizes per-pixel offsets to the input RGB and output normal along with a learned reflectance decomposition. A differentiable rendering layer converts the refined normals into polarization predictions and penalizes mismatches with the observed signal. Across seven benchmarks and three backbone architectures (diffusion, flow, and feed-forward), Poppy reduces mean angular error by 23-26% on synthetic data and 6-16% on real data. These results show that guiding learned RGB-based normal estimators with polarization cues at test time refines normals on challenging surfaces without retraining.

Speaker: Irene Kim

Location: NCS 115
Hyperscale Verification in Microsoft Azure talk by Nikolaj Bjorner

Abstract: Cloud providers are increasingly embracing network verification for managing complex datacenter network infrastructure. Microsoft's Azure cloud infrastructure integrates the SecGuru tool, which leverages the Z3 Satisfiability Modulo Theories solver, for checking network access
control lists. It also integrates a verifier that uses both custom verification algorithms and Z3 that checks correctness of forwarding tables in Azure data-centers. These tools assure that the network is configured to preserve desired intent over hundreds of thousands of network devices. We describe our experiences building and running SecGuru for network verification in Azure.

Finally we mention recent advances in Z3, including a distributed version of Z3 that scales with Azure's elastic cloud. It integrates recent advances in lookahead and distributed SAT solving for Z3's
engines for SMT. A different recent advance includes integration of DNNs to learn variable branching strategies for high-performance SAT solvers, including MiniSAT, Glucose and Z3's SAT solver.

Bio: Nikolaj Bjorner is a Principal Researcher at Microsoft Research, Redmond, working in the area of Automated Theorem Proving and Software Engineering. His current main line of work is around the state-of-the art theorem prover Z3, which is used as a foundation of several software engineering tools. Z3 received the 2015 ACM SIGPLAN Software System award and most influential tool paper in the first 20 years of TACAS in 2014, and test of time award at ETAPS 2018. Together with Leonardo de Moura received the CADE 2019 Herbrand award for contributions to SMT and applications. Previously, he developed the DFSR, Distributed File System - Replication, and Remote Differential
Compression protocols, RDC, part of Windows Server since 2005 and before that worked on distributed file sharing systems at a startup, and program synthesis and transformation systems at the Kestrel Institute. He received his Master's and PhD degrees in computer science from Stanford University.
The Empirical Methods in Natural Language Processing (EMNLP) conference is a premier international academic conference in the field of artificial intelligence and natural language processing (NLP). Organized annually by the Association for Computational Linguistics (ACL) special interest group on linguistic data (SIGDAT), it focuses on research that uses empirical methods to solve language processing problems.

For more information, and registration, visit the official website.
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University Libraries Presents: The Library AI Club is a welcoming space for students, faculty, and staff to explore AI in a supportive, low-pressure environment. Meeting every two weeks, the club features discussions, collaborative projects, guest speakers, and hands-on experiments. Join us to learn, share ideas, and engage with AI responsibly and creatively. We'd love to see you at an upcoming meeting! Location: Melville Library, Scholarly Communication Seminar Room