Hi, I'm Chandreyi (Zini) Chakraborty

Infrastructure @ Affirm  |  MS/BS CS @ Georgia Tech  |  AI Interp Research

I earned my Masters and Bachelors in Computer Science from Georgia Tech, both with machine learning concentrations. My research focuses on uncovering LLM inner workings through mechanistic interpretability, and leveraging those insights to improve model capability and efficiency while identifying where human judgment remains essential.

I second-authored a paper accepted to COLM 2026 that attributes social reasoning to pre-training data at the corpus level using influence functions and machine unlearning. This was in collaboration with EleutherAI as a member of the Entertainment Intelligence and Human-Centered AI Lab under Dr. Mark Riedl. I also published on how LLMs understand suspense in stories (COLM 2025).

I was a Machine Learning Teaching Assistant under Dr. Mahdi Roozbahani, where I developed a Socratic LLM agent that supports TAs in responding to student questions on EdStem with LoRA fine-tuning and dynamic RAG databases.

On the efficiency side, I have worked with different LLM architectures and GPU configurations for optimal compute and accuracy. I co-first-authored a paper on the quantization robustness of diffusion LLMs against autoregressive LLMs.

I am currently a software engineer at Affirm on an infrastructure team. Previously, I worked at Bloomberg on the Terminal, building an efficient portfolio aggregation service, and before that at Deloitte, where I worked with the Tennessee state government to enhance their Eligibility Benefits Management Portal and integrate SummerEBT Appeals.