Alex Pappachen James [email@example.com]
Neuromorphic Computing - Circuits and Systems; Pattern recognition and machine learning in hardware
"Artificial Intelligence - Be afraid of the evolution of stupidity exhibited by some natural brains; not the intelligent silicon brain"
Alex Pappachen James received the Ph.D. degree in 2 years from the Queensland Micro and Nanotechnology Centre, Griffith University, Brisbane, QLD, Australia. He works on brain-inspired circuits as well as algorithms and systems. Currently, he is chairing the Electrical Engineering Department and leads the Circuits and Systems Group at Nazarbayev University. He is actively engaged in research commercialization and startups. He has several years experience of managing industry projects and academic projects in board design and pattern recognition circuits, and data and business analytics consulting for IT and semiconductor industry. He is a mentor to several tech startups and co-founded companies in machine learning and computer vision. He has authored several peer-reviewed publications and is a reviewer to international journals and conferences such as IEEE ISCAS, IEEE ICECS, TCAS, TVLSI, TCAD, TCyb, TEC, and TIP. Dr. James has been the founding chair for IEEE Kerala Section Circuits and Systems Society and Executive Member of IET Vision and Imaging Network. He is the founding chair of IEEE Kazakhstan subsection, and mentor to IEEE NU Student Branch. He is a member of IEEE CASS Technical committee on Nonlinear Circuits and Systems. He was an editorial member of Information Fusion, Elsevier, and is an Associate Editor for HCIS, Springer, IEEE ACCESS, IEEE Transactions on Emerging Topics in Computational Intelligence (2017-18) (Guest associate editor), and IEEE Transactions on Circuits and Systems 1 (2018-present). He also received a certificate of appreciation and award from the President of the Republic of Kazakhstan for excellence in education in 2017. He is a Senior Member of IEEE, Life Member of ACM, and Senior Fellow of HEA.
Latest News! - Deep Learning Classifiers with Memristive Networks
Deep Learning Classifiers with Memristive Networks
This book introduces readers to the fundamentals of deep neural network architectures, with a special emphasis on memristor circuits and systems. At first, the book offers an overview of neuro-memristive systems, including memristor devices, models, and theory, as well as an introduction to deep learning neural networks such as multi-layer networks, convolution neural networks, hierarchical temporal memory, and long short term memories, and deep neuro-fuzzy networks. It then focuses on the design of these neural networks using memristor crossbar architectures in detail. The book integrates the theory with various applications of neuro-memristive circuits and systems. It provides an introductory tutorial on a range of issues in the design, evaluation techniques, and implementations of different deep neural network architectures with memristors.
Latest news! - 6 papers in ISCAS 2019
IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS 2019), Special Session: Emerging Memory Technologies for Neuromorphic Circuits and Systems; Chairs: Jason Eshraghian (University of Western Australia), Alex James (Nazarbayev University), Herbert Ho-Ching Iu (University of Western Australia)
EHETL Talk - Infinite professors - Higher education leaders forum - in a rat race https://youtu.be/5DFhfoLN_NQ
Latest news! - 3 papers in ISCAS 2018
Memristor and Memristive Neural Networks
This book covers a range of models, circuits and systems built with memristor devices and networks in applications to neural networks. It is divided into three parts: (1) Devices, (2) Models and (3) Applications. The resistive switching property is an important aspect of the memristors, and there are several designs of this discussed in this book, such as in metal oxide/organic semiconductor nonvolatile memories, nanoscale switching and degradation of resistive random access memory and graphene oxide-based memristor. The modelling of the memristors is required to ensure that the devices can be put to use and improve emerging application. In this book, various memristor models are discussed, from a mathematical framework to implementations in SPICE and verilog, that will be useful for the practitioners and researchers to get a grounding on the topic. The applications of the memristor models in various neuromorphic networks are discussed covering various neural network models, implementations in A/D converter and hierarchical temporal memories.
IEEE Transactions on Emerging Topics in Computational Intelligence
Guest editors: Alex James, Nazarbayev University; Khaled Salama, KAUST; Hai Li, Duke University; Biolek Dalibor, Univerzita Obrany, Brno; Giacomo Indiveri, ETH, Zurich; Leon Chua, University of California, Berkeley
Submission Deadline: November 30, 2017 (revised)
IET Cyber-Physical Systems: Theory & Applications
Guest editors: Alex James, Nazarbayev University; Martin Wäny, Austria Microsystems; Bhaskar Choubey, University of Oxford; Aleksej Makarov, Vlatacom; Sergio A Velastin, University Carlos III Madrid; Claudio Salvadori, New Generation Sensors srl
Submission Deadline: October 30, 2017 (revised)