ARINDAM BASU
Professor, Department of Electrical Engineering
City University of Hong Kong
Address: YEUNG-G6410
Phone: +852 34426077
arinbasu@cityu.edu.hk
ORCID iD: 0000-0003-1035-8770
Scopus Author ID: 21833638600
Arindam Basu received the B.Tech and M.Tech degrees in Electronics and Electrical Communication Engineering from the Indian Institute of Technology, Kharagpur in 2005, the M.S. degree in Mathematics and PhD. degree in Electrical Engineering from the Georgia Institute of Technology, Atlanta in 2009 and 2010 respectively. Dr. Basu received the Prime Minister of India Gold Medal in 2005 from I.I.T Kharagpur.
He is currently a Professor in City University of Hong Kong in the Department of Electrical Engineering and was a tenured Associate Professor at Nanyang Technological University before this.
He is currently an Associate Editor of Neuromorphic Computing and Engineering, IEEE Sensors journal, Frontiers in Neuroscience and IEEE Transactions on Biomedical Circuits and Systems. He has served as IEEE CAS Distinguished Lecturer for 2016-17 period. Dr. Basu received the best student paper award at Ultrasonics symposium, 2006, best live demonstration at ISCAS 2010 and a finalist position in the best student paper contest at ISCAS 2008. He was awarded MIT Technology Review's TR35 Asia Pacific award in 2012 for being among the top 12 innovators under the age of 35 in SE Asia, Australia and New Zealand and was inducted in Georgia Tech Alumni Association's 40 under 40 list in 2021.
He is a technical committee member of the IEEE CAS societies of Biomedical Circuits and Systems, Neural Systems and Applications (Chair) and Sensory Systems.
Research Interest
Neuromorphic circuits and systems, Brain-machine interface, Spiking Neural Networks and HW Security, Non-volatile memory In-memory Computing
Current Projects
Neuromorphic v2.0: AI for Edge Computing
NEUCOME: Neuromorphic Compression and Memory Management for next‐generation Implantable Brain‐machine Interfaces
Memristive Halide Perovskites for Next Generation Embedded Neuromorphic Computing
Reconfigurable Nonlinear In-memory Computations: A Pathway to Low-power, Recurrent Neural Networks
Service
Editorial Board Member, IOP Neuromorphic Computing and Engineering
Associate Editor, IEEE Trans. On Biomedical Circuits and Systems
Associate Editor, IEEE Sensors
Associate Editor, Frontiers in Neuroscience
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