Rate My Professor Muhammad Shafique

Shafique's research specializations encompass brain-inspired computing, AI and machine learning hardware and system-level design, energy-efficient systems, robust computing, hardware security, emerging technologies, FPGAs, MPSoCs, and embedded systems, with cross-layer analysis, modeling, design, and optimization for applications in Internet-of-Things, smart cyber-physical systems, and ICT for development. He has co-authored six books, more than ten book chapters, over 300 papers in premier journals and conferences, and holds one U.S. patent, amassing over 18,850 citations on Google Scholar. Key publications include "Mapping on multi/many-core systems: Survey of current and emerging trends" (DAC, 2013), "A low latency generic accuracy configurable adder" (DAC, 2015), "Hardware and software optimizations for accelerating deep neural networks: Survey of current trends, challenges, and the road ahead" (IEEE Access, 2020), and award-winning papers such as "R2Cache: Reliability-Aware Reconfigurable Last-Level Cache Architecture for Multi-Cores" (CODES+ISSS, 2015 Best Paper Award). His accolades include the 2015 ACM/SIGDA Outstanding New Faculty Award, 2020 AI 2000 Chip Technology Most Influential Scholar Award, six gold medals in his educational career, multiple best paper awards and nominations at DAC, DATE, ICCAD, and CODES+ISSS, two IEEE Transactions on Computers "Feature Paper of the Month" Awards, DAC'14 Designer Track Best Poster Award, and Best Lecturer Award. A Senior Member of IEEE and member of ACM, SIGARCH, SIGDA, SIGBED, and HIPEAC, he has served as PC Chair, General Chair, and Track Chair for IEEE/ACM conferences and directs the eBRAIN Lab at NYU Abu Dhabi.