Presentations

2025

Presentations
  • Zhang S, L Leung, BE Harrop, B Barthel, and T Sapsis. “2025: A Machine Learning Bias Correction of Large-Scale Environment of High-Impact Weather Systems Simulated by E3SM Atmosphere Model,” 2025 AMS Annual Meeting, New Orleans, LA, January 2025.

2024

Presentations
  • Actor JA. “Data-Driven Variational Reduced Models Using Learned Radial Basis Functions,” Scientific Machine Learning, Emerging Topics, Trieste, Italy, June 2024. (Poster)
  • Actor JA. “Data-Driven Reduced Models using Radial Basis Functions,” 9th European Congress on Computational Methods in the Applied Sciences, Lisbon, Portugal, June 2024.
  • Batlle P. "Frequentist confidence intervals: Refuting the Burrus conjecture," SIAM Conference in Uncertainty Quantification, Trieste, Italy, February 27–March 1, 2024.
  • Batlle P. "Frequentist confidence intervals: Refuting the Burrus conjecture," SIAM Conference in Material Sciences, Pittsburgh, Pennsylvania, May 20–24, 2024.
  • Batlle P. "Confidence intervals for functionals using optimization: from strict bounds to the Burrus conjecture disproof," STAMPS group meeting at CMU, May 24, 2024.
  • Batlle P. "Frequentist confidence intervals: Refuting the Burrus conjecture," Digital twins for inverse problems in Earth Science at CIRM, Marseille, France, July 22–26, 2024.
  • Bora A. “A Hybrid Machine Learning Approach for the Bias Correction of Global Climate Model,” Joint Applied Mathematics and MMICCs Principal Investigators Meeting, Albuquerque, New Mexico, January 8–10, 2024.
  • Bora A. “AI for Climate and Extreme Weather Events,” Subra Suresh Symposium at the Frontiers of Technology and Society, Providence, RI, September 2024.
  • Bourdais T. “Computational Hypergraph Discovery,” One world Seminar Series on the Mathematics of Machine Learning, online, January 17, 2024.
  • Bourdais T. “Computational Hypergraph Discovery,” Differential Equations for Data Science (DEDS2024), online, February 19, 2024.
  • Bourdais T. “Computational Hypergraph Discovery,” SIAM UQ, Trieste, Italy, March 29, 2024.
  • Bourdais T. “Computational Hypergraph Discovery,” Digital twins for inverse problems in Earth science workshop, Marseille, France, July 23, 2024.
  • Cyr EC, “Using Biased Gradients to Achieve Parallelism in Neural Network Training,” SIAM Parallel Processing, March 2024.
  • Cyr EC, “Progress in Layer-Parallel Neural Network Training and Inference,” Copper Mountain Conference on Iterative Methods, April 2024.
  • Cyr EC, “Reduced Basis Approximations of Parameterized Dynamic PDEs via Neural Networks,” ECOMAS, Lisbon, June 2024.
  • Cyr EC, “Inference on Neuromorphic Hardware (Loihi2): A Linear Algebra Perspective,” SIAM Annual Meeting, July 2024.
  • Darcy M. "One shot learning of SDEs with Gaussian processes," International Conference: Differential Equations for Data Science, online, February 22, 2024.
  • Darcy M. "Kernel methods for rough partial differential equations," SIAM Conference on Uncertainty Quantification, Trieste, Italy, March 1, 2024.
  • Darcy M.  “Kernel methods for rough partial differential equations," SIAM Material sciences, Pittsburgh, Pennsylvania, May 20, 2024. 
  • Darcy M. “Research overview,” Franca Hoffman group meeting, Caltech, Pasadena, CA May 31, 2024.
  • Darcy M. "Kernel methods for rough partial differential equations," Southern California Applied Mathematics Workshop, UC San Diego, San Diego, CA, April 27, 2024.
  • Darcy M. "Kernel methods and PINNs for rough partial differential equations,"SciCADE, National University of Singapore, Singapore, July 15, 2024.
  • Darcy M. "Kernel methods and PINNs for rough partial differential equations," Digital Twins for inverse problems in Earth sciences, CIRM Marseille, France, July 22, 2024.
  • Dreisbach M. “Convolutional feature-enhanced physics-informed neural networks for the spatio-temporal reconstruction of two-phase flows,” 7th Annual Meeting of the Division of Fluid Dynamics, Salt Lake City, UT, November 2024.
  • Gruber A. “Learning metriplectic systems and other bracket-based dynamics,” Mathematics for machine learning minisymposium at CMS Summer meeting, Saskatoon, Canada, June 2024.
  • Gruber A. “Learning metriplectic systems from full and partial state information,” Geometric mechanics formulations and structure-preserving discretizations minisymposium at 16th World Congress on Computational Mechanics, Vancouver, Canada, July 2024.
  • Gruber A. “Property-preserving machine learning of metriplectic systems”. Oden Institute workshop on scientific machine learning, Austin, TX, October 2024.
  • Howard AA. “More of a good thing: stacking deep operator networks,” SIAM Annual Meeting, Spokane, WA, July 2024.
  • Howard AA. “Uncertainty quantification for multifidelity operator networks,” WCCM, Vancouver, Canada, July 2024.
  • Jalalian Y. “Data-efficient kernel methods for PDE  Identification,” International Conference of Differential Equations for Data Science (DEDS), online, February 21, 2024.
  • Kahana A. “Leveraging Large Language Models for Scientific Machine Learning across Domains,” Workshop on The industrialization of Scientific Machine Learning, ICERM, Providence, Rhode Island, March 23–24, 2024.
  • Karniadakis G.E. “Physics-Informed Machine Learning: Blending data and physics for fast predictions," Intel, February 8, 2024. 
  • Karniadakis G.E. “Recent Advances in PINNs and Deep Neural Operators," Stanford University, March 11, 2024.
  • Karniadakis G.E. “From Physics-Informed Machine Learning to Physics-Informed Machine Intelligence: Quo Vadimus?" Illinois Institute of Technology, April 8, 2024.
  • Karniadakis G.E. “Physics-Informed Machine Learning in Engineering and Sciences," University of Central Florida, May 19, 2024.
  • Karniadakis G.E. “From Physics-Informed Machine Learning to Physics-Informed Machine Intelligence: Quo Vadimus?" Purdue University, May 22, 2024.
  • Karniadakis G.E. “Hidden Fluid Mechanics: Learning from any (sparse) data," Society of Engineering Science, August 22, 2024 (GI Taylor Medal).
  • Karniadakis G.E. “Recent Advances in PINNs and Deep Neural Operators," September 20, 2024.
  • Lee J. “Gaussian Processes and the Cole-Hopf Transformation,” Southern California Applied Maths Symposium, San Diego, California, April 27, 2024. (Poster)
  • Lee J. “Learning dynamical systems with kernels: irregularly sampled time series,” SIAM Conference on Applications of Dynamical Systems, Portland, Oregon, May 14–18, 2023.
  • Lee J. “Kernels Linearize Partial Differential Equations,” International Conference on Computational Science, Málaga, Spain, July 2–4 2024.
  • Lee J. “Gaussian Processes and the Cole-Hopf Transformation,” Fourth Symposium on Machine Learning and Dynamical Systems, Fields Institute, Toronto, Canada, July 8–12 2024. (Poster)
  • 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, New Mexico, April 22–26, 2024. 
  • Propp A. “Towards costless model selection in contextual bandits: A bias-variance perspective,” Artificial Intelligence and Statistics (AISTATS), Barcelona, Spain, May 2024.
  • Propp A. “Discovery of Dirichlet-to-Neumann maps on graphs via Gaussian processes,” Scientific Machine Learning: Emerging Topics, Trieste, Italy, June 2024.
  • Propp A. “Graph neural operators for quantification of geometric uncertainty,”, World Congress on Computational Mechanics / Pan American Congress on Computational Mechanics (WCCM-PANACM), Vancouver, British Columbia, Canada, July 2024.
  • Propp A. “Learning Dirichlet-to-Neumann maps on graphs with Gaussians,” SIAM Mathematics of Data Science (MDS), Atlanta, GA, October 2024.
  • Stinis P. “Multifidelity scientific machine learning,” Georgia Tech Workshop on Foundation of Scientific AI for Optimization of Complex Systems, Atlanta, GA, January 2024.
  • Stinis P. “When big neural networks are not enough,” ICERM Industrialization of SciML workshop, Providence, RI, March 2024.
  • Stinis P. “Multifidelity scientific machine learning,” American Mathematical Society Central Sectional Meeting, Milwaukee, WI, April 2024.
  • Stinis P. “Multifidelity scientific machine learning,” North American High Order Methods Conference (NAHOMCon), Hanover, NH, June 2024.
  • Stinis P. “Multifidelity scientific machine learning,” European Congress on Computational Methods in Applied Sciences and Engineering ECCOMAS, Lisbon, Portugal, June 2024.
  • Stinis P. “Multifidelity scientific machine learning,” SIAM Annual Meeting, Spokane, WA, July 2024.
  • Walker EA. “A new parametrization of DAGs and causal Markov kernels for scientific feature discovery,” American Causal Inference Conference, Seattle, WA, May 2024.
  • Walker EA. “A new parametrization of DAGs and causal Markov kernels for scientific feature discovery,” 16th World Congress on Computational Mechanics, Vancouver, Canada, July 2024.
Organized Conferences & Workshops
  • Organized minisymposium: Actor JA, A Gruber, et al. “Advances in machine learning on graphs for physical sciences and data analysis,” SIAM Mathematics of Data Science, Atlanta, GA, October 2024.
  • Organized minisymposium: Actor JA, E Walker, and Y Yu. "Causal Discovery and Graphical Causal Models," 16th World Congress on Computational Mechanics and 4th Pan American Congress on Computational Mechanics, July 21–26, 2024.
  • Organized minisymposium: Cyr EC and D Smith D. “Mathematical Advances in Algorithms Design Enabling Emerging Energy Efficient Computing,” SIAM Annual Meeting, Spokane, WA, July 2024.
  • Organized workshop: D' Elia M, GE Karniadakis. “The industrialization of Scientific Machine Learning.” ICERM, Providence, Rhode Island, March 23–24, 2024.
  • Organized minisymposium: Howard AA and P Stinis. “Advances in neural operators and uncertainty quantification for scientific modeling,” SIAM Annual Meeting, Spokane, WA, July 2024.
  • Organized minisymposium: Howard A, P Stinis, M Perego. "Advances in neural operators for scientific modeling," World Congress on Computational Mechanics, Vancouver, Canada, July 21–26, 2024.
  • Organized session: Jadhao V, A Howard, S Mahammad. "Data-Driven Rheology," Society of Rheology Meeting, October, 2024.
  • Organized workshop: Owhadi, H. “A GP/kernel perspective on Digital Twins," CIRM, Marseille, France, July 22–26, 2024.
  • Organized workshop: Owhadi H. “Digital Twins for Inverse Problems in Earth Science,” CIRM, Marseille, France, July 22–26, 2024.
  • Organized workshop: Panda P and A Moitra, “Energy-Efficient Intelligence with Neuromorphic Computing: From Algorithms to Hardware Design,” VLSID Conference, Kolkata, India, January 2024.
  • Organized minisymposium: Perego M, AA Howard, and P Stinis. “Advances in neural operators for modeling mechanics applications,” World Congress on Computational Mechanics, Vancouver, Canada, July 21–26, 2024.
  • Organized minisymposium: Propp A. “Machine Learning on Graphs for Physical Sciences and Data Analysis,” SIAM Mathematics of Data Science (MDS), Atlanta, GA, October 2024.
  • Organized minisymposium: Tartakovsky D. “Data Assimilation for Uncertainty Reduction,” 1st SIAM Northern and Central California Sectional Conference (NCC24), University of California, Merced, CA, October 2024.
  • Organized conference: Tartakovsky D. The Conference on Computational Methods in Water Resources (CMWR 2024), Tucson, AZ, September 2024.
  • Organized Minisymposium: Walker E, JA Actor, T Shilt, and A Shrivastava. “Machine Learning Algorithms for Accelerating Material Characterization, Discovery, Design, and Manufacturing Processes," 16th World Congress on Computational Mechanics and 4th Pan American Congress on Computational Mechanics, July 2024, Vancouver, Canada.

2023

Presentations
  • Actor JA. "Machine-Learned Finite Element Exterior Calculus for Linear and Nonlinear Problems," ICERM 2023 Meeting on Mathematical and Scientific Machine Learning, Providence, Rhode Island, June 5–9, 2023.
  • Actor JA. “Data-Driven Structure Preservation for Scientific Machine Learning,” 3rd Sandia Machine Learning and Deep Learning Conference, Albuquerque, NM, July 2023.
  • Actor JA. "Machine-Learned Whitney Forms for Structure Preservation," 10th International Conference on Industrial and Applied Mathematics, Tokyo, Japan, August 20–25, 2023.
  • Batlle P. "Decision theoretical Uncertainty Quantification and Applications," SIAM Conference in Computational Science and Engineering, Amsterdam, Netherlands, February 27–March 3, 2023.
  • Batlle P. "Frequentist confidence intervals: Refuting the Rust-Burrus conjecture," AFOSR Meeting, Alexandria, Virginia, August 7–11, 2023.
  • Batlle P. "Kernel methods are competitive for Operator learning" International Conference in Industrial and Applied Mathematics (ICIAM), Tokyo, Japan, August 20–25, 2023.
  • Batlle P. "Frequentist confidence intervals: Refuting the Burrus conjecture," NASA JPL UQ Seminar, Online, September 2023.
  • Batlle P. "Introduction to Decision Theoretical Uncertainty Quantification: motivation and current methods," 2nd IACM Mechanistic Machine Learning and Digital Engineering for Computational Science Engineering and Technology, online, September 22–27, 2023.
  • Batlle P. “Optimization-based frequentist confidence intervals for functionals in constrained inverse problems: Resolving the Burrus conjecture,” UQ for Remote Sensing Workshop, NASA Jet Propulsion Laboratory, September 28, 2023.
  • Batlle P. "Transparent and well-calibrated Uncertainty Quantification for ill-posed inverse problems," Center for Advanced Systems Understanding (CASUS) Seminar, Gortlitz, Germany, December 6, 2023.
  • Chen P. “Leveraging multi-time Hamilton-Jacobi PDEs for certain scientific machine learning problems,” Mathematical and Scientific Machine Learning, Providence, Rhode Island, June, 2023.
  • Cyr EC. “A 2-Level Domain Decomposition Preconditioner for KKT Systems with Heat-Equation Constraints,” Copper Mountain Conference on Multigrid Methods, April 2023.
  • Cyr EC. “A Layer-Parallel Approach for Training Deep Neural Networks,” SIAM CSE, Amsterdam, February 2023.
  • Darbon J. “Leveraging multi-time Hamilton-Jacobi PDEs for certain scientific machine learning problems,” 4th AFOSR Monterey Training Workshop on Computational Issues in Nonlinear Control, Monterey Bay Seaside, California, May 22, 2023.
  • Darcy M. "One shot learning of stochastic differential equations with Gaussian Processes," SIAM Computational Sciences and Engineering, Amsterdam, Netherlands, March 6, 2023.
  • Darcy M. "Benchmarking Operator Learning with Simple and interpretable Kernel Methods," Workshop on Establishing Benchmarks for Data-Driven Modeling of Physical System, USC, Los Angeles, California, April 6, 2023.
  • Darcy M. "Kernel methods are competitive for Operator Learning," DataSig Rough Path Interest Group, online, May 11, 2023.
  • Darcy M. "One Shot Learning of Stochastic Differential Equations with Gaussian Processes," SIAM conference on the Application of Dynamical Systems, Portland, Oregon, May 18, 2023.
  • Darcy M. "Kernel Methods are Competitive for Operator Learning," Argonne National Lab LANS Seminar, online, August 2, 2023.
  • Darcy M. "Kernel Methods are Competitive for Operator Learning," 10th International Congress on Industrial and Applied Mathematics, Tokyo, Japan, August 22, 2023.
  • Darcy M.  “Kernel Methods are Competitive for Operator Learning,” 2nd IACM Mechanistic Machine Learning and Digital Engineering for Computational Science Engineering and Technology, University of El Paso, Texas, and online, September 23, 2023. 
  • Darcy M.  “Kernel methods for operator learning,” One World Mathematics of Machine Learning seminar, online, October 18, 2023. 
  • Galanti T, M Xu, L Galanti, and T Poggio. “Norm-based Generalization Bounds for Compositionally Sparse Neural Networks.” 37th Conference on Neural Information Processing Systems. New Orleans, USA. December 10–16, 2023.
  • Goswami S. “On the Geometry Transferability of the Hybrid Iterative Numerical Solver for Differential Equations,” 21st Copper Mountain Conference on Multigrid Methods, Copper Mountain, Colorado, USA, April 16, 2023.
  • Gruber A. “Reversible and irreversible bracket-based dynamics for deep graph neural networks,” NeurIPS 2023 New Orleans, LA, December 2023. (poster)
  • Gruber A. “Data-driven surrogate models for bracket-based dynamical systems,” Data-driven methods for circuits and devices minisymposium at MMLDE-CSET, El Paso, TX, September 2023.
  • Howard A. “Multifidelity deep operator networks with applications to ice sheet modeling,” Computational Fluids Conference, Cannes, France, April 25–28, 2023.
  • Howard, A. “Multifidelity Deep Operator Learning," 2nd ICAM Mechanistic Machine Learning and Digital Engineering for Computational Science Engineering and Technology, El Paso, TX, September 24–27, 2023.
  • Jalalian Y. “Forecasting Hamiltonian Dynamics with Computational Graph Completion (CGC),” International Conference of Differential Equations for Data Science (DEDS), online, February 20, 2023.
  • Jalalian Y. “Equation Learning with Sparse Data and Kernels with a priori Error Estimates,” Advances in Computational Mechanics (ACM), UT Austin, Austin, Texas, October 24, 2023.
  • Jalalian Y. “Error estimates for learning PDEs with kernel methods,” Sparse and Data-driven Modeling in Computational Mechanics, Advances in Computational Mechanics (ACM 2023), Austin, Texas, Oct 22–25, 2023.
  • Kahana A. “Using Spiking Neural Networks for Scientific Computations,” SIAM Conference on Computational Science and Engineering (CSE23), Amsterdam, Netherlands, March 2, 2023.
  • Kahana A, Q Zhang, GE Karniadakis and P Stinis. “Spiking Neural Network Representation of Partial Differential Equation Evolution Maps,” SIAM Conference on Computational Science and Engineering (CSE23), Amsterdam, Netherlands, March, 2023.
  • Kahana A. “Preconditioning for Large Scale Systems based on the HINTS,” 21st Copper Mountain Conference on Multigrid Methods, Copper Mountain, Colorado, USA, April 17, 2023.
  • Kahana A. “Using Spiking Neural Networks for Scientific Computations,” Mathematical and Scientific Machine Learning, Providence, Rhode Island, June 8, 2023.
  • Kahana A. “On the Geometry Transferability of the Hybrid Iterative Numerical Solver for Differential Equations,” 17th U. S. National Congress on Computational Mechanics, Albuquerque, New Mexico, USA, July 23, 2023.
  • Karniadakis G.E. “Interfacing physics-informed neural networks and neural operators for accelerated FEM simulations of multiscale problems," Ansys, Boston, September 20, 2023.
  • Karniadakis G.E. “From Physics-Informed Machine Learning to Physics-Informed Machine Intelligence: Quo Vadimus?” Georgia Institute of Technology, October 18, 2023.
  • Karniadakis G.E. “Interfacing physics-informed neural networks and neural operators for accelerating simulations of multiscale problems,” Simulia, December 1, 2023.
  • Maxey M. “Mesoscopic simulations of cavitation and vapor bubble development”, 76th Annual Meeting of the Division of Fluid Dynamics, American Physical Society, Washington D.C., November 21, 2023.
  • Owhadi H. “Solving/learning PDEs with GPs and Computational Graph Completion,” AI for Science Workshop, Caltech, Feb 9, 2023.
  • Owhadi H. “Solving/learning PDEs with GPs and Computational Graph Completion,” Differential Equations for Data Science 2023 (DEDS2023), Feb 20, 2023.
  • Owhadi H. “Solving/learning PDEs with GPs and Computational Graph Completion,” Data-Driven Middling of Physical Systems, USC, April 6–7, 2023.
  • Owhadi H. “Kernel Mode decomposition,” MaSAG Conference, Rome, Italy, May 19–20, 2023.
  • Owhadi H. “Solving/learning PDEs with GPs and Computational Graph Completion,” Inaugural CAMDA Conference, College Station, Texas, May 22–25, 2023.
  • Owhadi H. “Kernel Mode Decompositon,” ECCOMAS-IACM thematic conference: Math 2 Product (M2P): Emerging Technologies in Computational Science for Industry, Sustainability and Innovation, Sicily, May 30–Jun 1, 2023.
  • Owhadi H. “Computational Hypergraph Discovery and Completion," ICERM Mathematical and Scientific Machine Learning Workshop, Providence, RI, June 5–9, 2023.
  • Owhadi H. “Kernel/GP methods for surrogate modeling,” Data Science and Machine Learning Summer School, Emilia Romagna, Italy, June 14, 2023.
  • Owhadi H. “Solving/learning PDEs with GPs and Computational Graph Completion,” Isaac Newton Institute for Mathematical Sciences Mathematical and Statistical Foundation of Future Data-Driven Engineering Programme, June 10–18, 2023. 
  • Owhadi H. “Solving/learning PDEs with GPs and Computational Graph Completion,” ICIAM 2023, Machine Learning in Infinite Dimensions, Tokyo, August 20–25, 2023.
  • Owhadi H. “Computational Hypergraph Discovery,” Boeing Applied Mathematics Colloquium Series, University of Washington, Nov 2, 2023.
  • Owhadi H. “Computational Hypergraph Discovery,” International Workshop on Multiscale Model Reduction and Scientific Machine Learning, Chinese University of Hong Kong (CUHK), Dec 4–6, 2023.
  • Stinis P. “Spiking Neural Network Representation of Partial Differential Equation Evolution Maps,” SIAM Conference on Computational Science and Engineering (CSE23), Amsterdam, Netherlands, March 2023.
  • Stinis P. “Multifidelity scientific machine learning” BIRS Scientific Machine Learning Workshop, Banff, Canada, June 2023.
  • Walker E and T. Shilt. "Physics-informed machine learning for feature discovery, cross-modal inference, and causal inference," CSRI Summer Seminar Series, Sandia National Laboratories, Albuquerque, NM, August 23, 2023.
  • Walker E, J. Actor, C. Martinez, and N. Trask. "Causal disentanglement of multimodal data," ICERM 2023, Providence, RI, June 12, 2023.
  • Xu, M. “Hyperbolic graph convolutional networks: A novel approach to discover aging trajectories and signatures of cognitive decline,” MIT-MGB AI Cures Conference, MIT Samberg Center, Cambridge, Massachusetts, April 2023. 
  • Xu, M. “Temporal stochastic graph embedding based on transformers,” SEA-CROGS Webinar, May 2023.
  • Xu, M. “Dynamics in Deep Classifiers Trained with the Square Loss: Normalization, Low Rank, and Generalization,” MSML Conference, ICERM, Providence Rhode Island, June 2023. 
  • Xu, M. “Learning temporal graph embeddings using transformers,” ICIAM Workshop on Mathematics of Geometric Deep Learning, 10th International Congress on Industrial and Applied Mathematics, Waseda University, Tokyo, Japan, August 20–25, 2023.
  • Xu, M. “Adaptive time-stepping for learning temporal graph embeddings using transformers,” Annual SIAM-NNP Meeting, New Jersey Institute of Technology, Newark, New Jersey, October 20–22, 2023.
  • Xu, Mengjia. “Adaptive time-stepping for learning temporal graph embeddings using transformers,” Alan Turing Institute, U.K. November 30, 2023.
  • Zhang E. “A Hybrid Iterative Numerical Transferable Solver (HINTS) for PDEs Based on Deep Operator Network and Relaxation Methods,” 21st Copper Mountain Conference on Multigrid Methods, Copper Mountain, Colorado, USA, April 16, 2023.
Organized Conferences & Workshops
  • Organized Minisymposium: Actor JA and EA Walker. “Beyond Fingerprinting: AI Approaches to Unearthing Process-Structure-Property Correlations in Additive Manufacturing,” U.S. National Congress on Computational Mechanics, Albuquerque, NM, July 2023.
  • Organized Minisymposium: Actor JA, EA Walker, et al. “AI/ML Algorithms for Accelerating Material Discovery, Design, and Manufacturing Processes,” 2nd IACM Mechanistic Machine Learning and Digital Engineering for Computational Science Engineering and Technology, El Paso, TX, September 2023.
  • Co-organized conference: Karniadakis G. ICERM 2023 Mathematical and Scientific Machine Learning Conference, Providence, RI, June 8, 2023.
  • Organized workshop: Panda P. SPIKES Workshop, ICERM 2023 Mathematical and Scientific Machine Learning Conference, Providence, RI, June 8, 2023.
  • Co-organized tutorial: Panda P. "Hardware and Software Co-Design for Edge AI," Design Automation Conference, San Francisco, CA, July 10, 2023.
  • Co-organized session: Panda P. Neuromorphic Computing Session, Asilomar Conference on Signals, Systems and Computers, October 30. 2023.
  • Organized minisymposium: Walker E. "Beyond Fingerprinting: AI Approaches to Unearthing Property Correlations in Additive Manufacturing," 17th U. S. National Congress on Computational Mechanics, Albuquerque, NM, July 23–27, 2023.
  • Organized minisymposium: Walker E. and T Shilt. "AI/ML Algorithms for Accelerating Material Discovery, Design, and Manufacturing Processes," 2nd IACM Mechanistic Machine Learning and Digital Engineering for Computational Science Engineering and Technology (MMLDE-CSET), El Paso, TX, September 24–27, 2023.

2022

Presentations
  • Kahana A. “Hybrid Iterative Method based on Deep Operator Networks for Solving Differential Equations,” International Multigrid Conference, Universitat De Svizzera Italiana, Lugano, Switzerland, August 25, 2022.
  • Owhadi H. “Solving/learning PDEs with GPs and Gaussian Process Hydrodynamics," International Conference on New Trends of Computational and Data Sciences, Caltech, Dec 19–21, 2022.
  • Panda P. “Neuromorphic Computing,” Yale Foundations of Data Science Launch Event, October 2022.
  • Panda P. “Spiking Neural Networks and their Relevance to AI,” Yale Foundations of Data Science Seminar, November 2022.
  • Xu M. “Learning temporal graph embeddings using transformers,” ICIAM Workshop on Mathematics of Geometric Deep Learning, 10th International Congress on Industrial and Applied Mathematics Waseda University, Tokyo, Japan, August 20-25, 2023.