CV
Education
Ph.D. in Machine Learning, Georgia Institute of Technology, 2021 - Present
M.S. in Statistics, eorgia Institute of Technology, 2024 (expected)
B.S. in Chemical Engineering, Carnegie Mellon University, 2021
- Minor in Computer Science
Experience
- Summer 2021, Research at Karsten Reuter Group, Fritz Haber Institute
- Compared Bayesian and ensemble methods of uncertainty quantification for machine-learned interatomic potentials to improve active learning framework.
- Explored uncertainty recalibration methods to improve the quality of uncertainty measures.
- Summer 2019 – Spring 2021, Research at Zachary Ulissi Group, Carnegie Mellon University
- Calculated adsorption energies of different adsorbates and surfaces with density functional theory (DFT) to find desirable catalysts for electrochemical processes.
- Organized and analyzed past DFT data to assess efficiency of current high-throughput automated workflow.
- Trained machine learning models to prioritize high-success calculations and skip futile calculations.
- Took part in the development of a flexible machine learning potential to learn from atomic simulations.
- Developed an active learning framework that learns the correction between first principle theory and simple physics-based potentials to serve as an inexpensive DFT surrogate.
- Summer 2018, Internship at BorsodChem, Hungary
- Oversaw the pipe replacement process in the toluene diisocyanate and methylenediphenyl diisocyanate production plants.
- Translated between Chinese and Hungarian to facilitate communication between colleagues to ensure daily procedures were efficiently conducted.
- Supervised the Chinese welders and pipefitters in the Hungarian work environment to comply with local work habits and safety standards.
Poster Presentations
- “Deep learning for three-dimensional semantic segmentation for periapical lesion detection on cone-beam computed tomography” at 2024 Society for Imaging Informatics in Medicine Annual Meeting
- “Accelerating Quantum Mechanical Simulations Using Physics-Based Machine Learning Potentials” at 2020 virtual AIChE Annual Meeting
- “Enhancing the Workflow Efficiency of High Throughput Surface Calculations” at 2019 Pittsburgh-Cleveland Catalysis Society Annual Symposium
Skills
- Software: MATLAB, Aspen Plus, GAMS, Linux, Conda, MongoDB, Google Search
- Programming: Python (NumPy, PyTorch, pandas, SciPy, seaborn, OpenCV), Standard ML, C, assembly language, Lua, Prolog
- Laboratory: titrations, UV/Vis spectrometry, highperformance liquid chromatography (HPLC), atomic absorption spectroscopy
- Languages: English, Mandarin, Hungarian, Spanish (intermediate)
Awards
- George Family Fellowship, 2024
- Chemical Engineering Summer Scholars, 2020
- Summer Undergraduate Research Fellowship, 2019
- Chemical Engineering Summer Scholars, 2019
Academic Awards
- Dean’s List, Fall 2017 - Spring 2021
Extracurricular
- Kiltie Band, 2020
- Tartan Wind Ensemble, 2018 - 2019