resume

PhD in Electrical and Systems Engineering

M.S.E in Robotics

B.S.E in Systems Science and Engineering

B.A. in Physics

Display Technologies Intern

• Created a machine learning model for display state estimation that reduced prediction errors by 88%.
• Integrated hardware, C++, Python to collect tens of gigabytes of performance data about displays.
• Analyzed the dataset using a Dask cluster on Kubernetes to rapidly iterate on model design.
• Presented findings to senior management with a variety of technical and non-technical backgrounds.

Research Fellow

• Leveraged GNNs to improve electrical grid efficiency achieving a 5\% cost reduction and <2\% error rate.
• Developed a framework for solving large-scale special problems by training CNNs on small scenarios and leveraging transfer learning for scalability, which was presented to Congress in Washington, DC.
• Collaborated with Lockheed Martin to develop a scalable multi-sensor multi-target tracking system, which achieved a 40\% accuracy improvement from SOTA and can handle hundreds of targets.
• First author on six papers presented at IEEE conferences including ICASSP, ACSSC and GlobalSIP.

Head Teaching Assistant

• Taught five semesters of “Information and Signal Processing” and “Graph Neural Networks”.
• Mediated multiple conflicts within student project groups resulting in a resumption of collaboration.

Machine Learning Intern

• Engineered a state-of-the art CNN model for malware classification achieving 97% accuracy.

Research Assistant

• Optimized vapor deposition production process to maximize conductance, carrier mobility and density.

• Dean's Fellowship - Awarded to ESE PhD students in recognition of exceptional performance and potential for continued high achievement in graduate work.

• Stuart Eichert, Jr. Memorial Prize - Junior who best demonstrates initiative, intellectual attainment, and commitment to the professional practice of engineering.