Our Team

Tristan Bepler


Tristan Bepler is the Group Leader of the Simons Machine Learning Center. Tristan received his undergraduate degree from Duke University in Computer Science and Biology. He received his PhD from MIT in the Computational and Systems Biology Program under the supervision of Bonnie Berger. He joined NYSBC to lead the SMLC after a postdoc with Tim Lu at MIT. His research interests broadly encompass machine learning methods for better understanding protein sequence, structure, and function as well as machine learning methods for protein structure determination especially with cryoEM and cryoET.

Alireza Nasiri


Ali is a scientist at Simons Machine Learning Center. He completed his undergraduate degree in computer engineering at Isfahan University of Technology. Ali received his Masters and PhD in computer science from University of South Carolina. His main research interests are machine learning and deep learning methods in computer vision, audio signal processing, and bioinformatics. He is particularly passionate about the application of machine learning methods in understanding complex biological phenomena and cognitive science.

Paul Kim


Paul is a Software Engineer at the SMLC. Paul did his undergrad in Statistics with a minor in Chemistry at UC Berkeley. He then worked as an ML Researcher at Bayer’s Machine Learning Research team in Berlin, headed by Djork-Arn√© Clevert. He is taking a year off from the Carnegie Mellon Comp. Bio. MS program to work on computer vision and automation problems in Cryo-EM at the SMLC, and is broadly interested in the intersection of machine learning and biology, especially as it pertains to proteins and to drug discovery.


Alex Noble


Alex Noble is a Group Leader at SEMC. He received two bachelors’ degrees from UC San Diego in Physics and Applied Mathematics. He received his MS and PhD from FSU in Physics under the supervision of Scott Stagg. He joined SEMC as a postdoc turned postdoc fellow with Bridget Carragher and Clint Potter. His research interests include hardware/software methods development and application in cryoEM, cryoET, cryo-FIB/SEM, and deep learning.