Shubham Singh

I am a Quantitative Researcher specializing in statistical modeling, machine learning, and deep learning. I have developed and backtested predictive pricing models and algorithmic trading strategies; built robust trading systems and risk frameworks; and engineered scalable ML pipelines from data ingestion and feature engineering to AI model training and real-time deployment.

I hold an M.S. in Computer Engineering from New York University (Sep 2023 – May 2025) and a B.S. in Computer Science from Bharati Vidyapeeth University (Jul 2019 – Jun 2023).

Currently working as Quant Research Lead at GoQuant, where I direct alpha research initiatives, deploy systematic futures and options strategies, and architect execution engines and quantitative pricing models. I design and implement intelligent smart-order-routing systems that optimize venue selection, minimizing market impact, slippage, and transaction fees.

My research interests include quantitative finance, machine learning, statistical modeling, and algorithmic trading. I am also interested in interdisciplinary topics that integrate methodologies in multiple fields such as applied probability, statistics, and optimization, along with their applications in addressing high-stake decision-making problems in modern large-scale systems, such as financial and economic systems. Some of the topics that I have been working on recently:

• mathematical foundation of Generative AI,
• optimal stopping and dynamic information acquisition,
• stochastic control, stochastic games, and mean-field games,
• reinforcement learning theory,
• and their applications in market microstructure and risk management

Please find my CV here.

Email: shubham.singh (at) nyu (dot) edu