Keynotes and Invited Talks

2024

  • Actor JA. “Exterior Calculus in Machine Learned Models,” ICME Seminar at Stanford University, Palo Alto, CA. January 2024. (Invited Talk)
  • Actor JA. “Data-Driven Reduced Models using Radial Basis Functions,” 9th European Congress on Computational Methods in the Applied Sciences, Lisbon, Portugal, June 2024. (Invited Talk)
  • Actor JA. “Exterior Calculus for Machine Learned Models,” Applied Mathematics Seminar Series, University of New Mexico, Albuquerque, NM. September 2024. (Invited Talk)
  • Batlle P. “Frequentist Confidence Intervals: Refuting the Burrus Conjecture,” Digital Twins for Inverse Problems in Earth Science, Marseille, France, July 22–26, 2024. (Invited Talk)
  • Bourdais T. “Computational Hypergraph Discovery,” One World Seminar Series on the Mathematics of Machine Learning, virtual, January 17, 2024. (Invited Talk)
  • Bourdais T. “Computational Hypergraph Discovery,” Differential Equations for Data Science (DEDS2024), virtual, February 19, 2024. (Invited Talk)
  • Bourdais T. “Computational Hypergraph Discovery,” Digital Twins for Inverse Problems in Earth Science Workshop, Marseille, France, July 23, 2024. (Invited Talk)
  • Cyr EC. “Exploiting “time-domain” parallelism to accelerate neural network training and PDE constrained optimization,” Pennsylvania State University, State College, PA. April 2024. (Invited Presentation).
  • Cyr EC. “Exploiting “time-domain” parallelism to accelerate neural network training and PDE constrained optimization,” University of Pennsylvania, Philadelphia, PA. April 2024. (Invited Talk)
  • Cyr EC. “Exploiting “time-domain” parallelism to accelerate neural network training and PDE constrained optimization,” University of Wuppertal, Germany. August 2024. (Invited Talk)
  • Darcy M. “Kernel Methods for Rough Partial Differential Equations,” Southern California Applied Mathematics Workshop, San Diego, CA, April 27, 2024. (Invited Talk)
  • Darcy M. “Kernel Methods and PINNs for Rough Partial Differential Equations,” Digital Twins for Inverse Problems in Earth Sciences, Marseille, France, July 22, 2024. (Invited Talk)
  • Gruber A. “Property-preserving model reduction in bracket-based dynamical systems,” Applied Mathematics Seminar Series, University of New Mexico, Albuquerque, NM. March 2024. (Invited Talk)
  • Gruber A. “Learning metriplectic systems and other bracket-based dynamics,” University of Vienna Mathematics Seminar, Vienna, Austria. June 2024. (Invited Talk)
  • Howard AA. “More of a good thing: multifidelity and stacking networks for physics-informed training,” Portland State University Applied and Computational Mathematics Seminar, Portland, Oregon. March 2024. (Invited Talk)
  • Howard AA. “More of a good thing: multifidelity and stacking networks for physics-informed training,” Sandia National Laboratory Seminar, Albuquerque, New Mexico. March 2024. (Invited Talk)
  • Howard AA. “Multifidelity stacking networks for physics-informed training,” Data Sciences for Mesoscale and Macroscale Materials Models, Chicago, Illinois. May 2024. (Invited Talk)
  • Howard AA. “Machine learning for Stokes flow: from suspensions to ice sheets,” Advancing fluid and soft-matter dynamics with machine learning and data science, Madison, Wisconsin. June 2024. (Invited Talk)
  • Jalalian Y. “Data-Efficient Kernel Methods for PDE Identification,” International Conference of Differential Equations for Data Science (DEDS), virtual, February 21, 2024. (Invited Talk)
  • Karniadakis GE. “Physics-Informed Machine Learning: Blending data and physics for fast predictions,” Intel, February 2024. (Invited Talk)
  • Karniadakis GE. “Recent Advances in PINNs and Deep Neural Operators,” Stanford University, Stanford, CA. March 2024. (Invited Talk)
  • Karniadakis GE. “From Physics-Informed Machine Learning to Physics-Informed Machine Intelligence: Quo Vadimus?” IIT, April 2024. (Invited Talk)
  • Karniadakis GE. “Physics-Informed Machine Learning in Engineering and Sciences,” University of Central Florida, May 2024. (Invited Talk)
  • Karniadakis GE. “From Physics-Informed Machine Learning to Physics-Informed Machine Intelligence: Quo Vadimus?” Purdue University, May 2024. (Invited Talk)
  • Karniadakis GE. “Hidden Fluid Mechanics: Learning from any (sparse) data,” Society of Engineering Science, August 2024 (GI Taylor Medal). (Invited Talk)
  • Karniadakis GE. “Recent Advances in PINNs and Deep Neural Operators,” September 2024. (Invited Talk)
  • Owhadi H. “Computational Hypergraph Discovery,” SIAM UQ24, Feb 27–Mar 1, 2024. (Plenary Lecture)
  • Owhadi H. “Co-discovering Graphical Structures and Functional Relationships Within Data: a Gaussian Process framework for Connecting the Dots,” SIAM UQ24, February 27–March 1, 2024. (Plenary Lecture)
  • Owhadi H. “Overview of Gaussian Process Techniques for Bridging Scales through Applications to Fluid Dynamics, Rough PDEs, Arbitrary Nonlinear PDEs, and Finding Functional Dependencies and Graphical Structures Within Data,” Workshop on Scale Bridging in Numerical Simulation, Los Alamos National Laboratory, Los Alamos, NM, Apr 22–26, 2024. (Invited Talk)
  • Owhadi H. “A GP/Kernel Perspective on Digital Twins,” Digital Twins for Inverse Problems in Earth Science, CIRM, Marseille, France, July 22–26, 2024. (Invited Talk)
  • Owhadi H. “Overview of Gaussian Process Techniques for Bridging Scales through Applications to Fluid Dynamics, Rough PDEs, Arbitrary Nonlinear PDEs, and Finding Functional Dependencies and Graphical Structures Within Data,” Workshop on Statistical Aspects of Non-Linear Inverse Problems, Cambridge, United Kingdom, September 16–18, 2024. (Invited Talk)
  • Owhadi H. “Overview of Gaussian Process Techniques for Bridging Scales through Applications to Fluid Dynamics, Rough PDEs, Arbitrary Nonlinear PDEs, and Finding Functional Dependencies and Graphical Structures Within Data,” Colloquium at CMOR Department, Rice University, Houston, TX, October 7, 2024. (Invited Talk)
  • Owhadi H. “Overview of Gaussian Process Techniques for Bridging Scales through Applications to Fluid Dynamics, Rough PDEs, Arbitrary Nonlinear PDEs, and Finding Functional Dependencies and Graphical Structures Within Data,” UC Riverside Department of Mechanical Engineering Seminar, Riverside, CA, October 17, 2024. (Invited Talk)
  • Owhadi H. “Overview of Gaussian Process Techniques for Bridging Scales through Applications to Fluid Dynamics, Rough PDEs, Arbitrary Nonlinear PDEs, and Finding Functional Dependencies and Graphical Structures Within Data,” Yale Foundations of Data Science (FDS) Colloquium, New Haven, CT, Oct 30, 2024. (Invited Talk)
  • Panda P. “Rethinking AI Algorithm and Hardware Design with Neuromorphic Computing”, VLSID Conference, Kolkata, India. January 2024 (Invited Keynote).
  • Panda P. “Energy-Efficient Intelligence with Neuromorphic Computing: From Algorithms to Hardware Design,” ECE Seminar, Duke University, January 2024. (Invited Talk)
  • Panda P. “On-device Intelligence with Spiking Neural Networks,” EE Seminar, Harvard University, March 2024. (Invited Talk)
  • Panda P. “Hardware-Aware Low-Precision Federated Learning,” DATE, Valencia, Spain, March 2024. (Invited Talk)
  • Panda P. “Neuromorphic Computing for Energy-Efficient Edge Intelligence,” VLSI-DAT, Hsinchu, Taiwan, April 2024. (Invited Keynote)
  • Panda P. “Are SNNs truly efficient?- A Hardware Perspective,” ICASSP, Seoul, S. Korea, April 2024. (Invited Talk)
  • Panda P. “A Co-Design Approach to Efficient and Deployable In-Memory Computing,” Design Automation Conference, SFO, June 2024. (Invited Talk)
  • Perego M. "Hybrid finite-element /neural operator modeling for ice-sheet dynamics," World Congress on Computational Mechanics, Vancouver, Canada, July 24, 2024.
  • Stinis P. “When big neural networks are not enough,” University of California Santa Cruz, Applied Mathematics Seminar, Santa Cruz, California, April 2024. (Invited Talk)
  • Stinis P. “Multifidelity scientific machine learning,” Michigan State University, Applied Mathematics Seminar, East Lansing, Michigan, March 2024. (Invited Talk)
  • Tartakovsky D. “Use and Abuse of Machine Learning in Scientific Discovery,” EAISI lecture, Eindhoven Artificial Intelligence Systems Institute, Eindhoven University of Technology, Eindhoven, Netherlands, March 2024. (Invited Talk)

2023

  • Actor JA. "Data-Driven Structure Preservation for Scientific Machine Learning," 3rd Sandia Machine Learning and Deep Learning Conference (MLDL), Virtual, July 17–20, 2023. (Invited Talk)
  • Actor JA. "Structure-Preserving Machine Learning via Whitney Forms," S. Scott Collis Advanced Modeling and Simulation Seminar Series, University of Texas at El Paso, El Paso, TX (virtual), October 2023. (Invited Talk)
  • Actor JA, A Huang, N Trask. "Machine-Learned Finite Element Exterior Calculus for Linear and Nonlinear Problems," ICERM Mathematical and Scientific Machine Learning Workshop, Providence, RI, June 5–9, 2023. (Invited Talk)
  • Actor JA, A Huang, N Trask. "Machine-Learned Whitney Forms for Structure Preservation," 10th International Congress on Industrial and Applied Mathematics (ICIAM), Waseda University, Tokyo, Japan, August 20–25, 2023. (Invited Talk)
  • Aimone B. “The Pursuit of the Brain’s Ubiquitous Stochasticity," 2023 International Conference on Neuromorphic, Natural and Physical Computing, October 2023 (Keynote Talk)
  • Batlle P. “Frequentist Confidence Intervals: Refuting the Burrus Conjecture,” NASA JPL UQ Seminar, online, September 2023. (Invited Talk)
  • Batlle P. “Optimization-Based Frequentist Confidence Intervals for Functionals in Constrained Inverse Problems: Resolving the Burrus Conjecture,” JPL UQ for Remote Sensing Workshop, online, September 28, 2023. (Invited Talk)
  • Batlle P. “Transparent and Well-Calibrated Uncertainty Quantification for Ill-Posed Inverse Problems,” Center for Advanced Systems Understanding Seminar, Gorlitz, Germany, December 6, 2023. (Invited Talk)
  • Cyr EC. “Exploiting “time-domain” parallelism to accelerate neural network training and PDE constrained optimization,” CCAM seminar at Purdue, West Lafayette, IN, April 2023. (Invited Talk)
  • Cyr EC. “A Layer-Parallel Approach for Training Deep Neural Networks,” SIAM CSE, Amsterdam, Netherlands, February 2023. (Invited Talk)
  • Cyr EC. “Exploiting “time-domain” parallelism to accelerate neural network training,” Banff Workshop on Scientific Machine Learning, Banff, Alberta, Canada, June 18–June 23, 2023. (Invited Talk)
  • Darcy M. “Benchmarking Operator Learning with Simple and Interpretable Kernel Methods,” Workshop on Establishing Benchmarks for Data-Driven Modeling of Physical Systems, University of Southern California, Los Angeles, CA, April 6, 2023. (Invited Talk)
  • Darcy M. “Kernel Methods are Competitive for Operator Learning,” DataSig Rough Path Interest Group, online, May 11, 2023. (Invited Talk)
  • Darcy M. “Kernel Methods are Competitive for Operator Learning,” Argonne National Lab LANS Seminar, online, August 2, 2023. (Invited Talk)
  • Darcy M. “Kernel Methods for Operator Learning,” OneWorld Mathematics of Machine Learning Seminar, online, October 18, 2023. (Invited Talk)
  • Gruber A. “Property-preserving model reduction for conservative and dissipative systems,” Numerical analysis of Galerkin ROMs seminar series, INRIA Bordeaux, France, October 2023. (Invited Talk)
  • Gruber A. “Data-driven dynamical systems with structural guarantees,” S. Scott Collis advanced modeling and simulations virtual seminar series, Rio Grande Consortium for Advanced Research on Exascale Simulation, November 2023. (Invited Talk)
  • Gruber A. “Data-driven dynamical systems with structural guarantees,” Applied mathematics and machine learning seminar at Texas Tech University, Lubbock, TX, November 2023. (Invited Talk)
  • Howard, A. “High Performance Computing for Multiphase Flows,” Spelman College Senior Seminar, Atlanta, GA, 2023. (Invited Talk)
  • Howard A. "Multifidelity Deep Operator Networks," ICERM Mathematical and Scientific Machine Learning Workshop, Providence, RI, June 5–9, 2023. (Invited Talk)
  • Jalalian Y. “Forecasting Hamiltonian Dynamics with Computational Graph Completion (CGC),” International Conference of Differential Equations for Data Science (DEDS), online, February 20, 2023. (Invited Talk)
  • Karniadakis GE. "Physics-Informed Machine Learning: Blending data & physics for fast predictions," Chemical Engineering Colloquium, Lehigh University, February 2023. (Invited Talk)
  • Karniadakis GE. "BINNs: Biophysics-Informed Neural Networks," IBS Biomedical Mathematics, April 2023. (Invited Talk)
  • Karniadakis GE. "From Physics-Informed Machine Learning to Physics-Informed Machine Intelligence: Quo Vadimus?" FAU MoD Lecture Series, May 2023. (Invited Talk)
  • Karniadakis GE. "NeuralUQ: A Comprehensive Library for Uncertainty Quantification in Scientific Machine Learning," 5th International Conference on Uncertainty Quantification in Computational Science and Engineering, June 2023. (Plenary Lecture)
  • Karniadakis GE. "Neural PDEs and Neural Operators for Fluid Mechanics and Reactive Transport," Stanford FLAME AI Workshop, September 2023. (Invited Talk)
  • Karniadakis GE. “Interfacing physics-informed neural networks and neural operators for accelerated FEM simulations of multiscale problems,” ANSYS, Boston, September 2023. (Invited Talk)
  • Karniadakis GE. "Scaling up Physics-Informed Machine Learning to Real World Applications," HPC Day, University of Massachusetts Dartmouth, October 2023. (Plenary Lecture)
  • Karniadakis GE. "Neural PDEs and Neural Operators for Physics-Informed Learning," AI/ML in Physics 2023, October 2023. (Invited Talk)
  • Karniadakis GE. “From Physics-Informed Machine Learning to Physics-Informed Machine Intelligence: Quo Vadimus?”, Georgia Tech, October 2023. (Invited Talk)
  • Karniadakis GE. “Interfacing physics-informed neural networks and neural operators for accelerating simulations of multiscale problems,” Simulia, December 2023. (Invited Talk)
  • Karniadakis GE. "Physics-Informed Neural Networks (PINNs) & Deep Operator Network (DeepOnet)," SciML@Simula Workshop, December 2023. (Invited Talk)
  • Owhadi H. “Solving/learning PDEs with GPs and Computational Graph Completion,” AI for Science Workshop, Caltech, Pasadena, CA, February 9, 2023. (Invited Talk)
  • Owhadi H. “Solving/learning PDEs with GPs and Computational Graph Completion,” Differential Equations for Data Science (DEDS2023), online, February 20–22, 2023. (Invited Talk)
  • Owhadi H. “Solving/learning PDEs with GPs and Computational Graph Completion,” Data-driven Modeling of Physical Systems, University of Southern California, Los Angeles, CA, April 6–7, 2023. (Invited Talk)
  • Owhadi H. “Kernel Mode Decomposition,” MaSAG Conference, Rome, Italy, May 19–20, 2023. (Invited Talk)
  • Owhadi H. “Solving/learning PDEs with GPs and Computational Graph Completion,” Inaugural CAMDA Conference, College Station, TX, May 22–25, 2023. (Invited Talk)
  • Owhadi H. “Kernel Mode Decomposition,” ECCOMAS-IACM Thematic Conference on Emerging Technologies in Computational Science for Industry, Sicily, Italy, May 30–June 1, 2023. (Invited Talk)
  • Owhadi H. “Computational Hypergraph Discovery and Completion,” ICERM Workshop: Mathematical and Scientific Machine Learning, Providence, RI, June 5–9, 2023. (Invited Talk)
  • Owhadi H. “Solving/learning PDEs with GPs and Computational Graph Completion,” Mathematical and Statistical Foundation of Future Data-Driven Engineering, Isaac Newton Institute for Mathematical Sciences, Cambridge, UK, June 10-18, 2023. (Invited Talk)
  • Owhadi H. “Kernel/GP methods for Surrogate Modeling,” Data Science and Machine Learning Summer School, Emilia Romagna, Italy, June 14, 2023. (Invited Talk)
  • Owhadi H. “Solving/learning PDEs with GPs and Computational Graph Completion,” ICIAM 2023: Machine Learning in Infinite Dimensions, Tokyo, Japan, August 20–25, 2023. (Invited Talk)
  • Owhadi H. “Computational Hypergraph Discovery,” Boeing Applied Mathematics Colloquium Series, University of Washington, Seattle, WA, November 2, 2023. (Invited Talk)
  • Owhadi H. “Computational Hypergraph Discovery,” International Workshop on Multiscale Model Reduction and Scientific Machine Learning, Chinese University of Hong Kong, Hong Kong, China, Dec 4–6, 2023. (Invited Talk)
  • Panda P. AAAI 2023 Workshop on Practical Deep Learning in the Wild, February 2023. (Keynote)
  • Panda P. “Algorithm-Hardware Co-design with Neuromorphic Computing,” Rhine-Westphalia Technical University of Aachen, March 2023. (Invited Talk)
  • Panda P. “Bio-plausible Algorithm-Hardware Co-Design with Spiking Neural Networks,” Brown University, April 2023. (Invited Talk)
  • Panda P. “Bio-plausible Algorithm-Hardware Co-Design with Spiking Neural Networks,” Princeton University, April 2023. (Invited Talk)
  • Panda P. “Neuromorphic Computing: Opportunities and Challenges for Edge Intelligence,” Eindhoven University of Technology, April 2023. (Invited Talk)
  • Panda P. "Bio-plausible Algorithm-Hardware Co-Design for Efficient and Robust AI,” Talk Link, May 2023. (Invited Talk)
  • Panda P. “Spiking Neural Networks: Opportunities and Challenges,” ICERM 2023 Meeting on Mathematical and Scientific Machine Learning, Providence, RI, June 2023. (Invited Talk)
  • Panda P. "Hardware Accelerators for Spiking Neural Networks for Energy-efficient Edge Computing," 4th ROAD4NN Workshop: Research Open Automatic Design for Neural Networks, 60th Annual Design Automation Conference, San Francisco, CA, July 2023. (Invited Talk)
  • Panda P. “Computational Needs for Lifelong Learning," DARPA 2023 Electronics Resurgence Initiative 2.0 Summit, August 2023. (Keynote)
  • Panda P. “Computational Needs for Lifelong Learning,” DARPA ERI Summit, September 2023. (Invited Talk)
  • Panda P. “Rethinking AI Algorithm and Hardware Design with Neuromorphic Computing,” ECE Seminar, University of California, Santa Barbara, October 2023. (Invited Talk)
  • Panda P. “Edge Intelligence with Neuromorphic Computing: From Algorithms to Hardware Design,” ECE Seminar, University of California, Berkeley, November 2023. (Invited Talk)
  • Perego M. “Computational Aspects of Ice-Sheet Modeling,” Pitt Mathematics-Naval Nuclear Laboratory Joint Seminar, Pittsburgh, PA, March 14, 2023. (Invited Talk)
  • Perego M. “A Hybrid Operator Network/Finite Element Method for Ice-Sheet Modeling,” 17th U. S. National Congress on Computational Mechanics, Albuquerque, NM, July 23-27, 2023. (Invited Talk)
  • Perego M. “Machine Learning modeling for accelerated uncertainty quantification in projections of ice sheets' mass change,” American Geophysical Union Fall Meeting, San Francisco, CA, Dec 10-15, 2023. (Invited Talk)
  • Propp A. “Transfer Learning on Multifidelity Data,” Mathematical and Scientific Machine Learning (MSML), Providence, Rhode Island, June 2023. (Invited Talk)
  • Stinis P. “Machine-learning custom-made basis functions for partial differential equations,” University of Arizona Applied Mathematics Colloquium, Tucson, AZ, March 2023. (Invited Talk)
  • Stinis P. “Spiking Neural Network Representation of Partial Differential Equation Evolution Maps,” SIAM Conference on Computational Science and Engineering, Amsterdam, Netherlands, March 2023. (Invited Talk)
  • Stinis P. “Multi-fidelity Scientific Machine Learning,” BIRS Scientific Machine Learning Workshop, Banff, Canada, June 2023. (Invited Talk)
  • Stinis P. “Multi-fidelity Scientific Machine Learning,” Georgia Tech Applied & Computational Math Seminar, Atlanta, GA, November 2023. (Invited Talk)
  • Tartakovsky D. “Use and Abuse of Machine Learning in Scientific Discovery,” Argyris Lecture 2023, University of Stuttgart, Stuttgart, Germany, October 2023. (Invited Talk)

2022

  • Actor JA, Huang, A, Trask, N. "Polynomial-Spline Networks with Exact Integrals and Convergence Rates," 2022 IEEE Symposium Series on Computational Intelligence (SSCI), Singapore, December 4–7, 2022. (Invited Talk)
  • Howard A. “Multifidelity Machine Learning Methods.” CMIT Seminar, Liverpool, UK, November 2022. (Invited Talk)
  • Karniadakis G. "From PINNs to Deep Neural Operators to Digital Twins: Quo Vadimus?" Digital Twins Information Gathering Session #1, Joint National Academies on the Foundational Research Gaps and Future Directions for Digital Twins. December 2022. (Invited Talk)
  • Owhadi H. “Solving/learning PDEs with GPs and Gaussian Process Hydrodynamics,” International Conference on New Trends of Computational and Data Sciences, Caltech, Pasadena, CA, December 19–21, 2022. (Invited Talk)
  • Panda P. “Exploring Robustness and Efficiency in Neural System with Spike-based Machine Intelligence,” ICCAD HALO 2022. November 2022. (Invited Talk)
  • Panda P. “Algorithm-Hardware Co-design for Efficient and Robust Spiking Neural Networks,” SNUF 2022. November 2022. (Invited Talk)
  • Panda P. “TCS Forum: Thought Leadership Conversations: Neuromorphic Computing for Transformation of the Industrial Future,” TCS Thought Forum on Neuromorphic. November 2022. (Invited Talk)