Jan‐Chi Yang

About

Jan‐Chi Yang is a pioneering researcher at the intersection of materials science and artificial intelligence, specializing in the development of active learning frameworks for autonomous experimentation. Their major contribution lies in designing dynamic Bayesian optimized recommender systems that integrate curiosity-driven algorithms with human-in-the-loop strategies, enabling more efficient and adaptive materials synthesis and characterization. By optimizing experimental workflows—from synchrotron diffraction measurements to automated chemical synthesis—Yang’s work reduces the time and resources needed to discover novel materials. Their most cited paper (2024, 28 citations) showcases a partially automated system that balances exploration and exploitation, while earlier iterations (2023, 6 and 3 citations) laid the groundwork for this approach. Yang’s research has significant implications for accelerating discovery in combinatorial materials science, bridging the gap between human expertise and machine-driven optimization. Their innovative use of Bayesian methods to guide experimental decisions marks a notable achievement in the growing field of self-driving laboratories, positioning them as a key contributor to the future of automated scientific discovery.

Research Focus

Key Achievements

Top Papers

1. 1
   A dynamic Bayesian optimized active recommender system for curiosity-driven partially Human-in-the-loop automated experiments

   28 citations · 2024
2. 2
   A dynamic Bayesian optimized active recommender system for curiosity-driven Human-in-the-loop automated experiments

   6 citations · 2023
3. 3
   A dynamic Bayesian optimized active recommender system for curiosity-driven partially “Human-in-the-loop” automated experiments

   3 citations · 2023

Key Collaborators

Contact & Links