Connor Coley

Connor Coley is the Henri Slezynger (1957) Career Development Assistant Professor at the Massachusetts Institute of Technology (MIT) School of Engineering. His research bridges chemistry and machine learning, focusing on autonomous molecular discovery, predictive chemistry, and laboratory automation.

• Education:
  • Ph.D., MIT (2019)
  • M.S.CEP., MIT (2016)
  • B.S., Caltech (2014)

Research Interests:
Dr. Coley’s work centers on domain-informed machine learning for chemistry, computer-aided molecular design, and autonomous laboratories. Key themes include predictive modeling of chemical reactivity, optimization of synthesis pathways, and integration of AI with experimental data for drug discovery and materials science.

Publications:
His recent articles highlight advancements in AI-driven reaction prediction, molecular representation learning, and laboratory automation. Trends include applications of Bayesian optimization, contrastive learning, and diffusion models to chemical discovery.

• Scientific Awards:
  • Camille Dreyfus Teacher-Scholar Award (2025)
  • James W. Swan Outstanding Faculty (2025)
  • Schmidt Futures AI2050 Early Career Fellow (2022)
  • NSF CAREER Award (2021)
  • Forbes 30 Under 30: Healthcare (2019)

Software & Tools:
He leads the open-source ASKCOS software suite for synthesis planning, adopted by 35,000+ chemists and deployed at 15+ pharmaceutical companies. His team also develops tools for metabolomics and molecular representation learning.