Five College of Engineering faculty members have been awarded the Dean’s Early Career Fellowship in recognition of their exemplary contributions to their respective fields. The fellowship is awarded to untenured faculty members who have been nominated by their department heads. The recipients will receive discretionary funds for a three-year period or until they have been promoted to full professor.
This year’s recipients are Assane Gueye, Gauri Joshi, Aaron Johnson, Tze Meng Low, and Zachary Ulissi.
Assane Gueye is an associate teaching professor at Carnegie Mellon University Africa and co-director of CyLab-Africa. His research focuses on two main areas: performance evaluation and security of large-scale communication systems, and information and communication technologies for development. He is a 2016 fellow of the Next Einstein Forum and was nominated as a member of the European Alliance for Innovation inaugural fellow class.
Gauri Joshi is an associate professor of electrical and computer engineering. Her research involves designing scalable algorithms for distributed machine learning and large-scale parallel computing, allowing users to use high-quality machine learning techniques even with limited computational resources. Joshi works with the Parallel Data Lab and CyLab at CMU. She has been selected as one of MIT Technology Review’s 35 Innovators Under 35 for 2022 and has received multiple awards from the National Science Foundation.
Aaron Johnson is an associate professor of mechanical engineering. His interests include novel robot design and dynamics including bio-inspired robots and robot ethics. He has tested robots in power plants, coal mines, and the Mojave desert. Johnson is the director of the Robomechanics Lab at CMU. He has received an NSF Career Award in 2020 and the Young Investigator Award from the Army Research Office in 2019.
Tze Meng Low
Tze Meng Low is an associate research professor of electrical and computer engineering. His research focuses on the systematic derivation and implementation of high-performance algorithms with the goal of achieving performance portability across both architectures and domains by understanding and capturing the interaction between software algorithms and hardware features through analytical models. He has previously won the SIAM Activity Group on Supercomputing Best Paper Prize in 2020 and has participated and won awards in the MIT Graph Challenge.
Zachary Ulissi is an associate professor of chemical engineering. His research involves using machine learning to simplify complex catalyst reaction networks and discover and design new catalysts. His group uses various methods to develop materials at the atomistic scale. Ulissi was named one of AIChE’s 35 Under 35 for his work on the development and application of high-throughput simulation methods, active learning methods, and machine learning models for surface science and catalysis.