Adeesh Kolluru

I am currently leading AI at Radical AI, where we are working on combining AI, computational chemistry, and autonomous labs to accelerate materials discovery. Previously, I was a Ph.D. student advised by John Kitchin and Zachary Ulissi at Carnegie Mellon University. My work broadly focuses on building efficient machine learning methods and frameworks to discover novel materials. My research interests span multiple areas including diffusion models, graph neural networks, multi-task learning, and more recently LLMs. You can find more detailed information in publications and CV.

During my PhD, I primarily contributed to the Open Catalyst Project in collaboration with Meta AI, developing large-scale models and datasets for computational chemistry. I also gained valuable industry experience through internships at Samsung Semiconductors working on battery materials discovery, Orbital Materials developing generative models for porous materials, and Meta AI advancing foundation models for atomic systems. These experiences have shaped my understanding of different perspectives on AI-driven materials discovery.

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Personal Interests

Beyond my research activities, I maintain diverse interests that contribute to my well-rounded perspective. During my undergraduate years at IIT Delhi, I was actively involved in sports and cultural activities, including playing water polo for the college team and participating in the Debating Club. These experiences have enhanced my teamwork and communication skills, which prove valuable in collaborative research environments.

In my personal time, I enjoy reading non-fiction literature on topics ranging from cognitive science and human behavior to economics and technology. I also stay engaged with current developments through documentaries and maintain an active lifestyle through running and swimming. As a chess enthusiast, I regularly play online (feel free to challenge me on chess.com) and enjoy playing piano as a creative outlet.

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