Ph.D. Students

Mohammad Al-Jarrah

I’m a Ph.D. Student in the department of aeronautics and aeronautics at University of Washington (UW) since January 2023. I received my master degree in 2020 from the same department. In 2018, I completed my undergrad degree at the top of my class and received the Geda and Phil Condit Endowed Fellowship for two years at UW. In 2024, I was the recipient of the A&A Student Excellence Award doctoral student and the UW SPEEA Aerospace Career Enhancement (ACE) Fellowship. My current research focuses on developing a new algorithm to solve the nonlinear filtering problem. Our approach relies heavily on the theory of optimal transport and deep learning. My research goal is to establish a novel framework to enable making decision under uncertainty.

Yuhang Mei

I am a first-year PhD student in Aeronautics and Astronautics Department at University of Washington advised by Prof. Amir Taghvaei. Besfore joining UW, I completed my master’s in Mechanical Engineering and Electrical & Computer Engineering at University of Michigan advised by Prof. Necmiye Ozay. I obtained my Bachelor degree in Mechanical Engineering at Huazhong University of Science and Technology. My research focus on the intersection of machine learning and control.

Jenny Jin (co-advised)

I’m Niyizhen(Jenny) Jin a 1st year PhD student in Applied Math at the University of Washington. I work on problems in optimal transport with machine learning applications. My area of interests include uncertainty quantification and data science.




Michele Martino (co-advised)

I am a Ph.D. student in Applied Mathematics at the University of Washington from Rome, Italy. I received a B.S. Honors in Mathematics from the University of Rochester and I am currently working under the supervision of Professors Bamdad Hosseini and Amir Taghvaei on transport methods for conditional sampling. During the reminder of my Ph.D., I hope to deepen my research in the fascinating field of optimal transport and its many applications in machine learning, uncertainty quantification, and data science