Alex Pappachen James [firstname.lastname@example.org]
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"
Dr. Alex James works in the area of memristive neuromorphic systems, VLSI and image processing. He completed his Ph.D. in a short 2-year duration from Queensland Micro-and Nanotechnology center, Griffith University. He is currently chairing the Electrical and Computer Engineering Department at Nazarbayev University. He has been a faculty member in Nazarbayev University since 2013. He is the founding chair of IEEE Kazakhstan subsection, mentor to IEEE Student branch and CASS Chapter at Nazarbayev University and a member of IEEE CASS technical committee on Nonlinear Circuits and Systems. He was the founding chair of IEEE CASS Kerala chapter and was a member of IET Vision and Imaging Network. He was an editorial board member of Information Fusion (2010-2014), and currently serving associate editor of IEEE Access, HCIS and IEEE Transactions on Circuits and System 1 journal. He has several years of experience in managing industry consulting projects and academic projects in board design and pattern recognition circuits, and data and business analytics consulting for IT and semiconductor industry. 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.
26th IEEE International Conference on Electronics Circuits and Systems (ICECS 2019) , Special Session: Reliability, robustness and signal integrity challenges for Processing-In-Emerging-Memory (PIEM) systems and neural architectures; Chairs: Alex James (Nazarbayev University), Bhaskar Choubay (Fraunhofer/Seigen), Jai Narayan Tripathi (STMicroelectronics), Helen Li (Duke University), Georgios Ch. Sirakoulis (DUTH)
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)