Eun-Ah Kim

Eun-Ah Kim is a Cornell physicist who applies machine learning to quantum matter and materials discovery. She trained in physics in Seoul and Illinois, joined Cornell after a Stanford postdoctoral fellowship, and rose from assistant professor to professor. Her work includes Quantum Loop Topography, interpretable neural networks for quantum data, ME-AI for materials discovery and the Quantum Attention Network; she becomes a Fellow of the American Physical Society in 2020. As of October 2026, she is the Hans A. Bethe Professor of physics at Cornell and director of its NSF AI-Materials Institute.

Now at: Cornell University

Timeline

1998–2005

Physics degrees in Seoul and Illinois

She earns a B.S. in physics from Seoul National University in 1998 and an M.S. there in 2000, then completes a physics Ph.D. at the University of Illinois at Urbana-Champaign in 2005.
2005–2014

Stanford and Cornell appointments

She works as a postdoctoral scholar at Stanford University from 2005 to 2008, then joins Cornell's physics faculty as an assistant professor from 2008 to 2014.
2014–2019

Professor at Cornell

She is an associate professor of physics at Cornell from 2014 to 2018 and becomes a professor in 2019.
2017–2019

Machine learning for quantum matter

In 2017, she and Yi Zhang publish Quantum Loop Topography, which turns Hamiltonian or wave-function data into images that a neural network uses to distinguish topological phases. In 2019, she is senior author of a Nature study that trains an artificial neural network to interpret scanning-tunneling-microscopy data and tests theories of electron interactions in a high-temperature superconductor.
2020

An APS Fellowship

The American Physical Society names her a Fellow in 2020.
2021

Interpretable quantum data

In 2021, she and collaborators at Cornell and Harvard develop Correlation Convolutional Neural Networks (CCNN), an interpretable model that reveals which correlations matter in quantum-matter images.
2025

Materials discovery with ME-AI

She and collaborators publish the ME-AI model in 2025; it turns expert-curated data into descriptors that predict functional properties of quantum materials.
2025

Quantum Attention Network

In 2025, she coauthors the Quantum Attention Network (QuAN), which treats measurement snapshots as tokens and learns quantum-state complexity from noisy quantum-computer and simulator data.
As of October 2026

Director of AI-MI

As of October 2026, she is the Hans A. Bethe Professor of physics at Cornell and director of the NSF AI-Materials Institute. On October 14, 2026, she delivers a Rice University colloquium on representation learning for hard data problems in quantum matter.