CV

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Education

• Ph.D. Student in Machine Learning, Georgia Institute of Technology, 2021 - Present

• 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

• “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)

Scholarships

• 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