I’m a quantitative research engineer currently in Chicago. I studied Computer Science at Dartmouth College. I’m very into cycling, playing guitar, and photography.

I enjoy learning CS theory, in particular graphics and rendering. My research was on Monte Carlo sampling theory, and I hope to continue contributing to the field. Outside of graphics, I find compilers and type theory rather interesting, and I like interesting programming languages like Rust, Haskell, and Idris.

Contact

You can reach me at afnan <at> afnan <dot> io.

I use PGP keys to sign and encrypt my emails. My public key is probably available on the usual public key servers. You can also access it here.

Experience

Professional

• Quantitative Research Engineer on Global Quantitative Strategies (GQS) at Citadel (current)
• Software engineer at Blend Labs on the infrastructure team (2019 - 2020)
• Interned at Capital One doing machine learning work with Payments API team (2018)
• Interned for Blend Labs on the infrastructure team (2017)
• Interned at Microsoft on the Windows core quality team (2015)

Academic

Works

• Orthogonal Array Sampling for Monte Carlo Rendering (EGSR/CGF 2019)
• Undergraduate thesis: Orthogonal Array Sampling for Monte Carlo Rendering (citation)
• Replacing transistors with nanomagnets for computation (2015)

Research

• Conducted research with Wojciech Jarosz of Dartmouth’s Visual Computing Lab and Pixar, researching potential ways to speed up Monte Carlo rendering using orthogonal arrays.
• Conducted research with the Department of Defense for a conceptual virtual reality simulation of the Battle of 73rd Easting with Dr. Joseph M. Rosen
• Researched using nanomagnets to replace transistors at Virginia Commonwealth University Nuclear and Mechanical Engineering Department with Professor Jayasimha Atulasimha

Relevant coursework

• Discrete Math
• Algorithms
• Mobile Programming (Android app dev)
• Software Development and Implementation (learned C, shell scripting)
• Machine Learning
• Rendering Algorithms
• Compilers
• Deep Learning
• Cognitive Computing with Watson