Samson Qian


"Driving the AI revolution in modern financial markets."

Samson holds a Master of Finance degree from the MIT Sloan School of Management and a B.S. degree from the University of California, San Diego in data science. At MIT, he was the president of Sloan’s Quantitative Finance Club and hosted the 2022 MIT Sloan AI & Quant Conference. He currently works in systematic and quantitative trading with a focus on US equity and cryptocurrency markets.



Google Scholar


Research Publications

Samson’s MIT thesis “Multi-Agent Deep Reinforcement Learning and GAN-Based Market Simulation for Derivatives Pricing and Dynamic Hedging” explores how GANs can be used as an alternative non-parametric approach to simulate and generate market data, as opposed to traditional Monte-Carlo methods that rely on assumptions about underlying distributions. This systematic framework becomes applied to deep hedging algorithms to train agents to find the optimal hedging policy. This deep reinforcement learning-based approach makes the dynamic hedging strategies more robust and precise compared to traditional greek hedging.

My MIT Thesis


Samson Qian discusses a shift away from traditional financial industry practices and a revolution towards the practical uses of AI in active portfolio and risk management to quickly and robustly detect and hedge against changing market regimes.

Watch my TED Talk


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