Sumana Basu is a machine learning researcher with 9 years of experience, recently completing a PhD in Reinforcement Learning at Mila under Doina Precup and Adriana Romero-Soriano, and now applying her expertise to personalize financial products at RBC Borealis. Her doctoral work, supported by an IVADO scholarship, focused on reinforcement learning for personalized healthcare, bridging rigorous theory with high-impact applications. At Scale AI she evaluated and benchmarked large language models, developing rubric-based benchmarks, probing LLM-as-judge biases, and prototyping rubric-driven optimization to boost reasoning. She has industry research experience from Microsoft and Meta, including an RL project that accelerated MRI acquisition, demonstrating a track record of moving research into practical systems. Based in Vancouver, she combines deep RL knowledge with AI evaluation skills to design robust, responsible decision-making systems. An uncommon strength is her dual focus on individualized decision policies and the meta-evaluation of AI agents, enabling both better personalization and more reliable model assessment.
9 years of coding experience
Doctor of Philosophy - PhD, Artificial Intelligence, Doctor of Philosophy - PhD, Artificial Intelligence at Mila - Quebec Artificial Intelligence Institute
Doctor of Philosophy - PhD, Reinforcement Learning, Doctor of Philosophy - PhD, Reinforcement Learning at McGill University
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