Aashish's Homepage
This is my homepage on the web!
Hi! I’m Aashish Khubchandani!
Welcome to my homepage!
I’m a Computer Science graduate student at Cornell Tech, where I focus on machine learning and methods for causal inference. My recent work includes building a Python package that implements performant matrix completion using novel nearest-neighbor estimators that adapt to noise and bias across settings. It’s been tested on standard benchmarks and is pending release for use by researchers and industry practitioners.
From 2022 to 2024, I worked as a quantitative software developer at Goldman Sachs, on the Fundamental Equities Strategies team within Goldman Sachs Asset Management (GSAM). I built financial tools and engineered data pipelines to drive investment decisions and risk management.
From 2018 to 2022, I studied Physics and Computer Science at New York University, where I applied machine learning to real-world problems in epidemiology, occasionally working with terabyte-scale datasets. I also served as a course assistant and department-appointed tutor in both Physics and Computer Science.
I’m proficient in Python, Java, and C, and I adapt quickly to new tools and frameworks. Outside of work, I enjoy hiking and roller skating.
You can find me at:
LinkedIn: https://www.linkedin.com/in/aashish-k/
GitHub: https://github.com/aashish-khub
E-mail: hello [ at ] aashish [ dot ] tech