Amir Haji-Akbari, PhD

Amir Haji-Akbari’s research group specializes in developing and utilizing advanced path sampling techniques to probe the kinetics and mechanisms of rare events. These are processes that occur very swiftly, but only after very long latency times. The separation of timescales between the long latency time and the short duration of the transition arises from the activated nature of a rare event, which can only occur due to the emergence of unlikely fluctuations in the system. They use molecular dynamics (MD) simulations, advanced path sampling techniques, theoretical descriptions based on thermodynamics and statistical mechanics, and machine learning to study the kinetics and mechanisms of a wide variety of rare events, such as crystal nucleation, ionic and molecular transport across synthetic nanoporous membranes and biological channel proteins, folding and aggregation or proteins, and conformational transformations in biomolecules

computational modeling ;  deep learning ;  dimensionality reduction ;  high-performance computing ;  neural networks ;  optimization ;  simulation ;  software development  

algorithms ;  artificial intelligence ;  computational biology ;  machine learning ;  neuromorphic computing ;  neurons ;  predictive modeling  

Haji-Akbari is an Associate Professor of Chemical and Environmental Engineering. He obtained his BSc in Biotechnology from the University of Tehran and his PhD in Chemical Engineering from the University of Michigan, Ann Arbor, under the supervision of Sharon Glotzer. After finishing his PhD, he joined Pablo Debenedetti’s Group at Princeton University as a postdoctoral researcher. His group develops and applies advanced sampling techniques to study rare events in soft matter systems. He has won several awards, such as the NSF CAREER Award, the AIChE COMSEF Young Investigator Award, the Ackerman Teaching Award, and the Sloan Research Fellowship.