Pejmon Shariati is a Computational Research Scientist at Princeton University working as part of a collaborative research effort between the Levin Lab and the Wang Lab. He develops computational methods to investigate electoral systems, representation, and collective decision-making through large-scale simulation and data-driven modeling. His research combines mathematical modeling, statistical inference, optimization, and machine learning to study complex systems. 

He received a B.S. in Mathematics with a minor in Computer Science from Tufts University, graduating summa cum laude, where his senior honors thesis focused on Topological Data Analysis. He also earned an M.A. in Mathematics from the University of Southern California, where his graduate studies emphasized differential geometry and topology. 

More broadly, his research interests include mathematical modeling, statistical inference, optimization, machine learning, and complex systems.