Saee Paliwal
About Saee Paliwal
I am a highly trained interdisciplinary researcher with 10+ years of experience developing state-of-the- art machine learning algorithms, spanning variational methods, reinforcement learning, recommender systems, and generative AI. As an AI Scientist at BenevolentAI, I have had the opportunity to help build BAI’s foundational AI stack, including tensor factorization models for graph-based link prediction, causal inference algorithms using Pseudo-Riemannian manifolds, and, most recently, large language models for biomedical Q&A. I also helped develop BAI’s core AI evaluation framework, to optimally adapt AI innovations to multi-modal biomedical data. As Director of AI Science at BAI, I lead the data science function within the AI function, and set the company’s data science strategy and roadmap. Formerly, in my doctorate, I focused on developing reinforcement learning algorithms of human decision-making under uncertainty, with the aim of creating a mechanistic, computational assay of mental illnesses, specifically, of behavioral addictions. I am passionate about working side-by-side with domain experts to help foster trust between humans and algorithms, in order to deliver impactful solutions to complex, interdisciplinary problems. I deeply value sharing my skills and knowledge in order to help develop others, and am committed to creating inclusive, diverse workplaces that celebrate and foster every individual’s unique skill set.