cv

M.S. in Computer Science

• Advised by Stefano Ermon and Andrew Ng
• Concentration - Artificial Intelligence

B.A.Sc. in Electrical Engineering

• Ranked 1^st out of over 100 students in department (2015, 2020)

Graduate Research Assistant

• Uncertainty quantification and diffusion models with Stefano Ermon
• Machine Learning for healthcare with Andrew Ng and Sharon Zhou

Research Assistant

• Active learning and image segmentation with Andrew Ng and Ram Rajagopal

Undergraduate Researcher

• Monocular depth estimation with Andreas Moshovos

Software Engineer (Intern)

• Worked on LLVM compiler to analyze and accelerate deep learning tasks on FPGAs
• Improved Intel’s OpenCL memory management, reducing runtime and memory overhead by an order of complexity

ASIC Design Engineer (Intern)

• Design and verification of processors for deep learning

Stanford CS 229 (Machine Learning) TA

Stanford CS 236G (Generative Adversarial Networks) TA

Measuring and Reducing Bias in LLMs introduced by RLHF (Code | Poster | Paper)

• Awarded “Best Project” in Stanford’s CS 224R for identifying and mitigating bias in LLMs due to RLHF
• Identified and mitigated increased bias in LLMs using a post-hoc self-debiasing method

Data Augmentation for Speech Recognition (Code | Paper)

• Established the project as the leading implementation of MaskCycleGAN-VC on GitHub, evidenced by being the most starred repository in its category
• Generated African American Vernacular English utterances from generic American English to address data scarcity

Best Project Award

• CS 224R (Deep Reinforcement Learning)

Outstanding Student Award

• Awarded to the top 3 students in Electrical Engineering

W.S. Wilson Medal

• Awarded to student with highest academic standing in the graduating year

Baptie Scholarship

• Awarded to students with high academic standing (top 1%)