CV

Education

• B.S. in Electrical Engineering and Computer Science, University of California Berkeley, 2020
  • 3.8 GPA, graduated in Honors Degree program
  • Member of Tau Beta Pi Honor Society
• M.S. in Electrical Engineering and Computer Science, University of California Berkeley, 2021 (expected)

Work experience

• December 2018 - Present: Student Researcher at Lawrence Berkeley National Lab
  • worked on interdisciplinary team of staff scientists to develop interpretable inverse design algorithms for optical material design
  • used advanced mesh processing convolutional networks for design optimization
  • funded by ARPA-E DIFFERENTIATE grant
• May 2020 - August 2020: Software Engineering Intern - Machine Learning at Pure Storage
  • developed unsupervised anomaly detection time-series algorithms
  • crafted novel time-series decomposition and simulated data generation for machine learning trainig
• May 2018 - February 2019: Machine Learning Intern at Aerospace Corporation
  • utilized neural networks to create anomaly-detection algorithms for batteries in Aerospace cube satellites
  • applied machine learning for non-destructive, in-situ additive manufacturing characterization
• October 2017 - September 2018: R&D Intern at Heliotrope Technologies
  • developed deep-learning based device controller for smart windows
  • implemented automated defect detection algorithm for batch-scale manufacturing processes

Publications

Sequential Optimal Placement of Distributed Photovoltaics using Downstream Power Index

Prediction of Composite Microstructure stress-strain curves using convolutional neural network

Interpretable Forward and Inverse Design of Particle Spectral Emissivity using Common Machine Learning Models