My research goal is to develop Autonomous, Efficient and Theory-Grounded machine learning algorithms, with a particular focus on advancing scientific discoveries.
For Autonomous Decision Making, I design agents powered by generative models, with an emphasis on improving reasoning and planning abilities of foundation models that could facilitate research-level abilities.
For Theory-Grounded Research, I focus on developing AI systems that are certifiably robust and provably optimal, ensuring reliability and efficiency in their decision-making processes.
For Efficient Scientific Discovery, I leverage natural language processing and generative agents to accelerate key areas of scientific progress, with a focus on drug discovery.
By bridging foundational machine learning research with real-world scientific applications, my work aims to create robust, reasoning-intensive AI systems capable of addressing critical challenges, including treating human diseases and uncovering the fundamental laws of nature.