I am currently a Postdoc @ Argonne.
Before that, I was the Cunningham fellow @ Virginia Tech.
I develop software and algorithms at the intersection of optimization, approximation theory, and machine learning for scientific applications such as material design, computational physics, and HPC tuning. My active areas of work are blackbox optimization, machine learning, computational geometry, analysis of algorithms, and open-source scientific software. Read more about my research, and check out some of my software below or on GitHub.
Don't hesitate to reach out or connect at an upcoming event!
If you are a US-based student see several opportunities to visit Argonne.
I combine techniques from optimization, approximation theory, and machine learning in order to guide scientific experimentation and model complex real-world phenomena. My work facilitates reliable, interpretable, and structure-exploiting machine learning to drive scientific discovery and engineering system design.
Some examples of my past and ongoing work include:
2023. ParMOO: Python library for parallel multiobjective simulation optimization
Release: 0.2.2
Devs: T. H. Chang (lead), S. M. Wild, and H. Dickinson
Primary Prog. Lang: Python 3
2022. VTMOP: Solver for blackbox multiobjective optimization problems
Devs: T. H. Chang (sole)
Primary Prog. Lang: Fortran 2008
2020. DelaunaySparse: Interpolation via a sparse subset of the Delaunay triangulation
Devs: T. H. Chang (lead) and T. C. H. Lux
Primary Prog. Lang: Fortran 2003
2019. QAML: Quantum annealing math library
Devs: T. C. H. Lux (lead), T. H. Chang, and S. S. Tipirneni
Primary Prog. Lang: Python 3
Jun 2022 - Aug 2022. Manisha Garg (Urbana-Champaign), NSF MSGI at Argonne
Jun 2022 - Aug 2022. Hyrum Dickinson (Urbana-Champaign), DOE SULI at Argonne
Last update: 2023-05-18
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