Rajiv V. Joshi (Fellow, IEEE) received the B.Tech. degree from IIT Bombay, Mumbai, India, in 1977, the M.Eng. degree from the Massachusetts Institute of Technology (MIT), Cambridge, MA, USA, in 1981, and the Dr.Eng.Sc. degree from Columbia University, New York City, NY, USA, in 1983., He has successfully led predictive failure analytic techniques for yield prediction and also the technology-driven SRAM at the IBM Server Group. His statistical techniques are tailored for machine learning and artificial intelligence (AI) which are licensed and commercialized.
He is currently a Research Staff Member and a Key Technical Lead with the IBM T. J. Watson Research Center, Yorktown Heights, NY. He has authored or coauthored over 220 articles and given over 60 invited/keynote talks and several seminars. He holds 68 invention plateaus and over 278 U.S. patents and over 415 including international patents.
Dr. Joshi was a recipient of three outstanding technical achievements (OTAs), three highest corporate patent portfolio awards for licensing contributions, NY IP Law Association “Inventor of the Year” Award in February 2020, and prestigious IEEE Daniel Noble Award in 2018 for contributions to predictive analytics, circuits, and technology. He received Industrial Pioneer Award in 2014 from IEEE Circuits and Systems Society and the Best Editor Award from IEEE Transactions on Very Large Scale Integration ( VLSI) Systems (TVLSI) journal. He was inducted into the New Jersey Inventor Hall of Fame in August 2014. He received Mehboob Khan Award two times, From Semiconductor Research Corporation. He Won Several Best Paper Awards From Isscc 1992, Iccad 2012, Isqed, And Vmic.
He is a member of IBM Academy of technology and a master inventor. He serves in the board of governors for IEEE CAS as industrial liaison. He served as a distinguished lecturer for IEEE CAS, CEDA, and EDS society. He is a fellow of ISQED and world technology network and a distinguished alumnus of iit Bombay.
SRAM chips, low-power electronics, MOSFET, integrated circuit design, analogue-digital conversion, neural nets, resistive RAM, CMOS integrated circuits, cryogenic electronics, logic design, memory architecture, memristors, quantum computing, random-access storage, statistical analysis, CMOS memory circuits, deep learning (artificial intelligence), energy conservation, error correction, ferroelectric devices, field effect transistors, integrated circuit reliability, phase noise, power aware computing, Boolean functions
