Soumyadip Sarkar

Papers

Total Citations

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About

Soumyadip Sarkar is a rising researcher at the intersection of machine learning and quantitative finance, with a primary focus on reinforcement learning (RL) applications. His most cited work, "Quantitative Trading using Deep Q Learning" (2023), pioneers the use of deep RL agents for autonomous trading strategies, demonstrating how algorithms can learn to maximize profits by interacting with market environments. This foundational paper has garnered early citations, signaling growing interest in his approach to bridging RL theory with practical financial decision-making. Sarkar’s contributions extend to exploring how RL—traditionally successful in robotics and game playing—can be adapted to the dynamic, noisy domain of financial markets. His work addresses key challenges in state representation, reward design, and policy stability for trading systems. As an emerging voice in this niche, Sarkar is helping to shape a new generation of data-driven trading models. His research holds promise for both academic understanding of sequential decision-making under uncertainty and for industry applications in algorithmic trading. With a clear trajectory, Sarkar is positioned to make lasting impacts on the future of AI-driven finance.