01 / Solid-state physics
Solid-state thermodynamics
Making accurate thermodynamic predictions accessible, from quantum effects at low temperature to strongly anharmonic solids.
Computational scientist · DOE CSGF fellow
Computational scientist
I’m a mechanical engineering PhD researcher at Carnegie Mellon. I use physics, machine learning, and high-performance computing to understand the world, from heat transfer to protein binding.
I’m exploring AI-for-science research roles, with a focus on scientific discovery through machine learning and high-performance computing.

01 / Solid-state physics
Making accurate thermodynamic predictions accessible, from quantum effects at low temperature to strongly anharmonic solids.
02 / Scientific computing
Multi-GPU numerical computing through familiar Julia arrays, with automatic kernel fusion and support for existing scientific libraries.
Illustration: PINDER paper
03 / AI for biology
Building richer datasets and models to study protein binding, with explicit attention to intrinsic disorder.

My essay on atomic motion and modeling solids, published in DEIXIS. Honorable Mention in the 2026 Communicate Your Science & Engineering (CYSE) contest.

David and I presented a very early version of cuNumeric.jl at JuliaCon 2025 in Pittsburgh, before the work in our current manuscript. I also served as a local liaison, helping coordinate spaces and reservations with CMU and Pitt.
Watch our talk
David Krasowska and I discuss how we started cuNumeric.jl and our collaboration with NVIDIA on Science in Parallel.
Listen to the episode