Gina Adam
George Washington University, Washington, DC, United States
neuromorphic devices, ML/AI hardware, brain-inspired computing, device/algorithm co-design

Markus Buehler
Massachusetts Institute of Technology, Cambridge, MA, United States
materials, molecular modeling, fracture, proteins, biophysics

Maria K. Chan
Argonne National Laboratory, United States
Computational Modeling, AI/ML, X-ray, Electron Microscopy

Ying-Chen (Daphne) Chen
Arizona State University, United States
microelectronics, CMOS-compatible memory technology, emerging memory, neuromorphic computing

Bingqing Cheng
Institute of Science and Technology Austria (IST Austria), Austria
machine learning in Chemistry, statistical mechanics, atomistic simulations, computational materials science

Erika Covi
NaMLab gGmbH, Dresden, Germany
memristive and nanoelectronics devices, neuromorphic computing, in-memory computing, cognitive devices and systems

Volker Deringer
Oxford University, United Kingdom
computational materials chemistry, amorphous solids, machine learning interatomic potentials

Catherine Dubourdieu
Freie Universität Berlin, Germany
inorganic materials, ferroelectrics, memristive devices, neuromorphic materials and devices

Kedar Hippalgaonkar
Nanyang Technological University, Singapore
AI for materials and chemistry, optimization, generative design, inorganic materials, functional materials, solid-state physics

Rohit John
ETH Zurich, Switzerland
memristors, memtransistors, neuromorphic devices, memory, in-memory computing

Zdenka Kuncic
The University of Sydney, Sydney, Australia
neuromorphic intelligence, physics-informed machine intelligence, physical neural networks

Woei Ming (Steve) Lee
Australian National University, Canberra, Australia
computational optics, quantitative biomedical imaging, physics-informed machine learning

Can Li
The University of Hong Kong, Hong Kong
memristor, non-volatile memory, Neuromorphic computing, in-memory computing

Mingda Li
Massachusetts Institute of Technology, Cambridge, MA, United States
Materials theory, Neutron scattering, X-ray scattering, Generative models, Scientific machine learning

Danijela Markovic
Unité Mixte de Physique CNRS, Thales, Université Paris-Saclay, France
quantum neural networks, neuromorphic computing, superconducting circuits

Gianluca Milano
Istituto Nazionale di Ricerca Metrologica (INRiM), Italy
Memristive devices and systems, Artificial Neural Networks, Unconventional Computing, Nanotechnology

Nagarajan Raghavan
Singapore University of Technology and Design (SUTD), Singapore
Physics informed machine learning, inverse design, bayesian learning, multiphysics modeling and simulation, kinetic monte carlo simulations

Mary Scott
University of California Berkeley, California, United States
electron microscopy, machine learning, tomography, nanomaterials

Alex Serb
The University of Edinburgh, Edinburgh, United Kingdom
hardware, memristors, RRAM, AI, circuit design

Cory Simon
Oregon State University, Corvallis, OR, United States
machine learning to predict the properties of molecules and materials

Taylor Sparks
University of Utah, Salt Lake City, UT, United States
materials informatics, energy materials, machine learning

Sabina Spiga
CNR-IMM, Italy
memristive devices, neuromorphic materials and devices, spiking neural networks, brain inspired devices, RRAMs

Milica Todorović
University of Turku, Finland
condensed matter physics, first-principles simulations, materials informatics, organic/inorganic interfaces, machine learning

Helen Tran
University of Toronto, Toronto, Canada
polymers, synthesis, peptoids, bioelectronics

Ilia Valov
Research Centre Jülich, Germany
memristive devices, materials, nanoelectrochemistry, electrode kinetics, electrocatalysis

Xiaonan Wang
National University of Singapore (NUS), Singapore
AI for Science, Energy, Materials, Optimization

Zhongrui Wang
The University of Hong Kong, Hong Kong
in-memory computing, emerging memory, machine learning accelerator, and neuromorphic system

Qian Yang
University of Connecticut, Connecticut, United States
machine learning, model reduction, computational materials, dynamical systems

Xiaoxian Zhang
Beijing Jiaotong University, Beijing, China