Bipin Rajendran (King’s College London, United Kingdom)

In-memory computing – The key to efficient AI accelerators

Bipin Rajendran is a Professor of Intelligent Computing Systems and EPSRC Fellow at King’s College London (KCL). He received a B. Tech degree from I.I.T. Kharagpur in 2000, and MS and PhD degrees in Electrical Engineering from Stanford University in 2003 and 2006, respectively. He was a Master Inventor and Research Staff Member at IBM T. J. Watson Research Center in New York from 2006-’12 and has held faculty positions in India and the US. His research focuses on building algorithms, devices, and systems for brain-inspired computing. He has co-authored over 100 papers in peer-reviewed journals and conferences, one monograph, one edited book, and 59 issued U.S. patents. He is a recipient of the IBM Faculty Award (2019), IBM Research Division Award (2012), and IBM Technical Accomplishment (2010). He was elected a senior member of the US National Academy of Inventors in 2019.

Marco Chiani (University of Bologna, Italy)

Quantum error correction for communications and computing

Marco Chiani is a Professor in telecommunications at the University of Bologna. He received the Dr. Ing. degree in electronic engineering and the Ph.D. degree in electronic and computer engineering from the University of Bologna, Italy, in 1989 and 1993, respectively. Since 2003, he has been a frequent visitor at the Massachusetts Institute of Technology (MIT), Cambridge, MA, USA, as Research Affiliate. He is an IEEE Fellow, and has been appointed Distinguished Visiting Fellow of the Royal Academy of Engineering, U.K. He received the 2011 IEEE Abraham Prize, the 2012 IEEE Ellersick Prize, and the 2012 IEEE Rice Prize in the Field of Communications Theory. He has been the Chair (2002–2004) of the Radio Communications Committee of IEEE CommSoc. His research interests are in the areas of information theory, wireless systems, statistical signal processing and quantum information. His contributions include also exponential bounds for the Gaussian error function, and the statistical distribution of the eigenvalues of random matrices.

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