Institute for Pure & Applied Mathematics (IPAM)

Tim Germann - Molecular Dynamics 1 - IPAM at UCLA

Dorsa Sadigh: "Interaction-Aware Planning: A Human-Centered Approach toward Autonomous Driving"

Liming Xiong - Prediction of Microstructure Evolution in Plastically Deformed Heterogeneous Alloys

Kaie Kubias: "Rank-one tensor completion"

Deborah Bard - The Superfacility Model for Connected Science - IPAM at UCLA

Matthias Christandl: "Asymptotics of Ranks of Tensors"

Justin Smith - The state of neural network interatomic potentials - IPAM at UCLA

Michele Ceriotti - Machine learning for atomic-scale modeling - potentials and beyond - IPAM at UCLA

Gábor Orosz: "Conflict analysis for decision making and control of connected automated vehicles"

Maxime Gasse: "Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization Solvers"

Juan Carrasquilla: "Simulating quantum dynamics with neural machine translation"

Alejandro Rodriguez - Physical bounds on wave phenom as quadratically constrained quadratic programs

Jose Perea - LatMath 2022 - IPAM's Latinx in the Mathematical Sciences Conference

James Kermode - Multiscale and data-driven methods for the simulation of material failure

Frederic Legoll - Parareal algorithms for molecular dynamics simulations - IPAM at UCLA

Stephan Hoyer: "Improving PDE solvers and PDE-constrained optimization with deep learning and di..."

Aurora Clark - high-dimension perspective on extracting & encoding information in chemical systems

Anders Nikklasson - Quantum-Mechanical Molecular Dynamics for Distributed Computing and AI-hardware

Aidan Thompson - LAMMPS simulation: physics models, machine-learning potentials, exascale computing

Konstantin Matveev: "Positivity for symmetric functions and vertex models"

Vikram Gavini - DFT 2 - Density functional theory - IPAM at UCLA

Ivan Oleynik - Materials at Extremes: Discovery Science with Exascale Computers and Experiment

Elias Khalil - Neur2SP: Neural Two-Stage Stochastic Programming - IPAM at UCLA

Ralf Drautz - From electrons to the simulation of materials - IPAM at UCLA

Richard Hennig & Jason Gibson - AI-driven workflows for the discovery of novel superconductors

Jan Janssen - pyiron – Rapid-prototyping and Up-scaling Workflows for the Exascale - IPAM At UCLA

Marisol Koslowski - Surrogate Hot-spot Models for Simulations of Detonation in Energetic Materials

Stan Moore - Optimizing GPU Performance: Case Study Using Chain Benchmark in LAMMPS

Abigail Hunter - Mesoscale Investigation of Dislocation-Grain Boundary Interactions in Metal & Alloy

Christoph Ortner - Atomic Cluster Expansion with and Without Atoms - IPAM at UCLA

Danny Perez - Molecular Dynamics 2 - IPAM at UCLA

Shenglin Huang - Data-Driven Model Discovery for Non-equilibrium Processes - IPAM at UCLA

Samuel Blau - High-Throughput DFT and Monte Carlo for Reaction Networks and Machine Learning

Thomas Hudson - Multiscale Modeling - IPAM at UCLA

David Bowler - Large-scale and linear scaling DFT: why we need it, and how we do it - IPAM at UCLA

Ian Robinson - Progress towards Machine Learning Phasing for Bragg Coherent Diffractive Imaging

Dr. Andrea M. Ghez - From the Possibility to the Certainty of a Supermassive Black Hole - IPAM UCLA

Jaafar El-Awady - dislocation in high thermomechanical condition in Additive Manufacturing of Alloys

Tim Germann - Molecular Dynamics 1 - IPAM at UCLA

Lin Lin - Interacting models for twisted bilayer graphene: quantum chemistry approach - IPAM at UCLA

Phebe Vayanos - Integer optimization for predictive & prescriptive analytics in high stakes domains

Tina Eliassi-Rad - The Pitfalls of Using ML-based Optimization - IPAM at UCLA

Florin Bobaru - Peridynamic fracture across scales: large scale computations with fast methods

Timo Berthold - Machine Learning inside MIP solvers - IPAM at UCLA

Vikram Gavini - DFT 1 - Density functional theory - IPAM at UCLA

Ellen Vitercik - Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty

Petar Veličković - Reasoning Algorithmically: from Toy Experiments to AGI Modules - IPAM at UCLA

Andreas Savin - Beyond density functional approximations by lessons from density functional theory

Priya Donti - Optimization-in-the-loop AI for energy and climate - IPAM at UCLA

Pascal Van Hentenryck - Fusing Machine Learning and Optimization - IPAM at UCLA

Furong Huang: "Understanding, Interpreting & Designing NN Models Through Tensor Representations"