ABSTRACT
This presentation will cover the highlights of research of Professor Nandi from his early days to the current time, including particle physics, signal processing, communications, biomedical signal processing, fault classification, gene signal processing and brain signal processing.
Until recently analyses of functional magnetic resonance imaging data and gene expression data started from the point of view of testing certain hypothesis. While there have been some successes in such an approach, it clearly ignores the possibility of discovering many unknown things about human brains and genes which remain largely unexplored. This lecture will develop the rationale for the need of data driven methods and present some results from analysing real data using such methods.
Another important issue relates to multiple datasets, which may have been generated either in the same laboratory or different laboratories at different times and with different settings though trying to conduct the similar experiments. In such a scenario, the challenge is how to reach consensus conclusions from a selection of heterogeneous datasets from similar experiments. This presentation will outline how Bi-CoPaM and UNCLES can solve both the issues. This presentation will include examples of results from some bioinformatics and brain signal processing, although these can be applied to all applications areas involving clustering.
BIOGRAPHY
Professor Asoke K. Nandi received the degree of Ph.D. in Physics from the University of Cambridge (Trinity College), Cambridge (UK). He held academic positions in several universities, including Oxford (UK), Imperial College London (UK), Strathclyde (UK), and Liverpool (UK) as well as Finland Distinguished Professorship in Jyvaskyla (Finland). In 2013 he moved to Brunel University (UK), to become the Chair and Head of Electronic and Computer Engineering. Professor Nandi is a Distinguished Visiting Professor at Tongji University (China) and an Adjunct Professor at University of Calgary (Canada).
In 1983 Professor Nandi contributed to the discovery of the three fundamental particles known as W+, W− and Z0 (by the UA1 team at CERN), providing the evidence for the unification of the electromagnetic and weak forces, for which the Nobel Committee for Physics in 1984 awarded the prize to two of his team leaders for their decisive contributions. His current research interests lie in the areas of signal processing and machine learning, with applications to communications, gene expression data, functional magnetic resonance data, and biomedical data. He has made many fundamental theoretical and algorithmic contributions to many aspects of signal processing and machine learning. He has much expertise in “Big Data”, dealing with heterogeneous data, and extracting information from multiple datasets obtained in different laboratories and different times. He has authored over 550 technical publications, including 220 journal papers as well as four books, entitled Automatic Modulation Classification: Principles, Algorithms and Applications (Wiley, 2015), Integrative Cluster Analysis in Bioinformatics (Wiley, 2015), Blind Estimation Using Higher-Order Statistics (Springer, 1999), and Automatic Modulation Recognition of Communications Signals (Springer, 1996). The h-index of his publications is 67 (Google Scholar) and ERDOS number is 2.
Professor Nandi is a Fellow of the Royal Academy of Engineering (UK) and also a Fellow of seven other institutions including the IEEE and the InstP. Among the many awards he received are the Institute of Electrical and Electronics Engineers (USA) Heinrich Hertz Award in 2012, the Glory of Bengal Award for his outstanding achievements in scientific research in 2010, the Water Arbitration Prize of the Institution of Mechanical Engineers (UK) in 1999, and the Mountbatten Premium, Division Award of the Electronics and Communications Division, of the Institution of Electrical Engineers (UK) in 1998.