Muhammad Shahbaz

Dr. Muhammad Shahbaz is the Kevin C. and Suzanne L. Kahn New Frontiers Assistant Professor in Computer Science at Purdue University. He specializes in designing domain-specific abstractions, compilers, and architectures for emerging workloads such as machine learning and self-driving networks. His research bridges networking, machine learning, and computer architecture to create high-performance, scalable systems.

Shahbaz holds a Ph.D. and M.A. in Computer Science from Princeton University and a B.E. in Computer Engineering from the National University of Sciences and Technology (NUST). Before joining Purdue, he conducted postdoctoral research at Stanford University and worked as a Research Assistant at Georgia Tech and the University of Cambridge.

His research interests include Networking and Operating Systems, Artificial Intelligence, Machine Learning, Computer Architecture, Distributed Systems, and Programming Languages/Compilers. He has developed influential open-source systems like Pisces, SDX, and NetFPGA-10G, which are widely adopted in industry and academia.

Shahbaz has received prestigious awards including the Facebook, Google, and Intel Research Awards; IETF/IRTF ANRP Prize; ACM SOSR Systems Award; and APNet Best Paper Award. His work focuses on advancing edge computing, smartNICs, in-network machine learning, and scalable distributed systems.

His research portfolio includes contributions to network caching, hardware acceleration, and AI-driven network optimization. He leads projects like CAREER (per-packet AI on heterogeneous data planes) and EdgeScaler (smart auto-scaling for 5G edge networks). His systems address challenges in tail latency, resource harvesting, and scalable multicast in modern networks.