Monisha Gopalan

Data Science ∩ Quantitative Finance ∩ Theoretical Physics

I am a Quant-leaning Data Scientist, based in Milan, Italy, working at Advanced Risk and Portfolio Management (ARPM) where I translate rigorous quantitative finance concepts into Python code and visual analytics.

I’m driven by the challenge of taking the elegance of mathematical research and applying it to drive smarter, more grounded decisions in the finance industry.

My Story: a Brownian Motion

Born and raised in India, I completed my Bachelor of Science (Research) and Master’s degree in Materials Science from the esteemed Indian Institute of Science, a university consistently ranked #1 in India for its research contributions. Securing a place in their highly interdisciplinary program stands as a significant achievement, considering that only 120 out of 1.5 million high school graduates attain this distinction. Seeking to deepen my theoretical understanding, I moved to Italy to pursue a Master’s in Physics at the University of Padova.

My career took a pivotal turn following an internship as an AI Scientist at a startup in Milan, where I worked on a Energy-Based Models for Portfolio Optimization (ICAIF’24: Hopfield Networks for Asset Allocation). This experience got me really excited about the opportunities to use my skills in quantitative finance and data science, shaping my current role at Advanced Risk and Portfolio Management (ARPM).

Why Brownian Motion? Beyond the metaphor for life’s unpredictability, it is a concept that has run through every stage of my career: from modeling particles in Materials Science to pricing assets in Finance.

How I Think

  • I am inherently curious and quick to grasp new concepts, and I consider my ability to delve to the core of a problem/concept my greatest strength.
  • I find satisfaction in understanding phenomena through mathematical explanations, shaped by my studies in abstract mathematical topics like Topology and Group Theory and advanced physics courses like Quantum Field Theory and General Relativity.
  • I adopt a critical approach to learning by posing questions to myself and exploring alternatives.
  • I value interdisciplinary thinking and enjoy combining ideas from different fields to solve problems.
  • I am fascinated by the dynamic world of programming and it has been a consistent companion throughout my academic journey.

Beyond Work

I like getting creative with art and crafts, experimenting with cuisines from around the world, and spending time outdoors in nature. I also enjoy dancing, going to the gym, and appreciating a good meme or pun. Check out my AI Could Never page for more random quests.