Three Texas ECE faculty, Ruochen Lu, Aryan Mokhtari, and Amy Zhang, have been selected to receive a Faculty Early Career Development (CAREER) Award from the National Science Foundation (NSF). The award is the most prestigious offered by NSF’s CAREER Program, providing up to five years of funding to junior faculty members who exemplify the role of teacher-scholars through outstanding research, excellent education and the integration of education and research within the context of their organizations’ missions.
Ruochen Lu is an Assistant Professor in the Chandra Family Department of Electrical and Computer Engineering at The University of Texas at Austin. His research primarily focuses on developing chip-scale acoustic and electromagnetic components and microsystems for RF applications. His works aim to demonstrate RF MEMS platforms, toward higher operating frequencies and more efficient transduction between the EM and acoustics. In addition, he works on ultrasonic transducers and multi-physics hybrid microsystems for signal processing, sensing, and computing applications.
Aryan Mokhtari is an assistant professor and Fellow of the Jack Kilby/Texas Instruments Endowed Faculty Fellowship in Computer Engineering in the Department of Electrical and Computer Engineering at The University of Texas at Austin. His research interests include the areas of optimization, machine learning, and artificial intelligence. His current research focuses on the theory and applications of convex and non-convex optimization in large-scale machine learning and data science problems. He has received a number of awards and fellowships, including Penn’s Joseph and Rosaline Wolf Award for Best Doctoral Dissertation in electrical and systems engineering and the Simons-Berkeley Fellowship.
Amy Zhang is an assistant professor and Texas Instruments/Kilby Fellow in the Department of Electrical and Computer Engineering at UT Austin starting Spring 2023 and an affiliate member of the Texas Robotics Consortium. Her work focuses on improving sample efficiency and generalization of reinforcement learning algorithms through bridging theory and practice, and developing new decision making algorithms for real world problems.
