I aim to advance our understanding of market dynamics and quantitative trading strategy by challenging established practices and assumptions. My passion for mathematics, decision making, and building software, combined with deep learning and data analysis experience and a genuinely inquisitive nature, positions me exceptionally well for accomplishing my goal.
I demonstrated and applied my relentless curiosity and capability to learn quickly over the course of three research projects throughout my Computer Science undergraduate degree. The projects spanned a broad range of domains, from quantifying public transportation usage using temporal GPS data to formalizing the time complexities of various string manipulation algorithms, and culminated in my undergraduate research thesis of developing an end-to-end convolutional neural network-based classification pipeline for images of blood clots.
Prior to joining MSCF at CMU, I had the fantastic opportunity of being a member of the AI infrastructure and site reliability engineering teams at Vendasta Technologies, where I was able to challenge, grow, and prove my interpersonal and technical leadership skills. I led several organization-wide initiatives at Vendasta, ranging from the adoption of a nascent AI communication protocol to increasing overall system performance, availability, and observability. Each initiative was accompanied by giving technical presentations to the entire R&D group.
I am confident that my intellectual humility and curiosity, eagerness to challenge the status quo, my teamwork and leadership capabilities, as well as technical expertise in the machine learning and software engineering domains, will allow me to meaningfully contribute to the quantitative trading field.
