Connor Blake

Massachusetts Institute of Technology

Cambridge, MA

I’m a PhD student in MIT EECS in the Engineering Quantum Systems (EQuS) group. I earned a B.S. in Quantum Molecular Engineering with a minor in CS from the University of Chicago (June 2026).

I’ve previously worked with Prof. Liang Jiang at UChicago PME on quantum optics theory and optimal control for hybrid qubit-oscillator systems. Previously, I worked with Prof. Jörn Dunkel at MIT on diffusion models and learning coarse-grained dynamics.

I interned at Atlantic Quantum (later acquired by Google) working on circuit simulation and design automation. I also conducted research with Prof. Shuolong Yang on machine learning-aided thin-film deposition systems, and at Argonne National Laboratory on computational physics of quasicrystals.

I am a Barry Goldwater Scholar (2025) and Stamps Scholar (2024).

You can contact me at cjblake@mit.edu

news

Jun 06, 2026	Graduated from the University of Chicago with a B.S. in Quantum Engineering and a minor in Computer Science.
Nov 05, 2025	Paper published in npj Computational Materials: A self-driving physical vapor deposition system making sample-specific decisions on the fly.
Jun 15, 2025	Started summer research at MIT with Prof. Jörn Dunkel on variational diffusion models and learned coarse-grained dynamics.

selected publications

npj Comput Mater
A self-driving physical vapor deposition system making sample-specific decisions on the fly

Y.B. Zheng, C. Blake, L. Mravac, and 1 more author

npj Computational Materials, Nov 2025

Abs DOI Bib HTML

We present a self-driving physical vapor deposition system that uses a custom Bayesian machine learning algorithm to learn recipes for parametric beamsplitter fabrication, making sample-specific decisions on the fly.
@article{zheng2025selfdriving, title = {A self-driving physical vapor deposition system making sample-specific decisions on the fly}, author = {Zheng, Y.B. and Blake, C. and Mravac, L. and others}, journal = {npj Computational Materials}, volume = {11}, pages = {327}, year = {2025}, month = nov, day = {5}, doi = {10.1038/s41524-025-01805-0}, }