报 告 人： Professor Asoke K. Nandi (IEEE Fellow)
Brunel University London, United Kingdom
邀 请 人： 岳晓冬 副教授
Clustering algorithms are often used to extract information from large datasets. They represent model-free or data-driven approaches. They have been developed and applied in many areas for several decades. In particular, they have been used for gene clustering over the last two decades in bioinformatics and in brain signal processing. New algorithms are being developed and applied to address many different problems. However, in applications with real data with little a priori knowledge, it is often difficult to select an appropriate clustering algorithm and evaluate the quality of clustering results due to the unknown ground truth. It is also the case that conclusions based on only one specific algorithm might be biased, since each algorithm has its own assumptions of the structure of the data, which might not correspond to the real data. 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 yet trying to conduct the similar experiments. In such a scenario, one has essentially a collection of heterogeneous datasets from similar experiments. The challenge is how to reach consensus conclusions in such scenarios. This presentation will address these issues and report on the results from applying Bi-CoPaM and UNCLES recently to analyse fMRIdata and gene data.
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 co-discovered 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 signal processing and machine learning, with applications to functional magnetic resonance data, gene expression data, communications, and biomedical data. He has made 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. Professor Nandi 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.
Fellow, Royal Academy of Engineering, U.K. (2014).
Fellow, Institute of Electrical and Electronics Engineers (IEEE), U.S.A. (2011).
Fellow, British Computer Society, U.K. (2007).
Fellow, Institution of Mechanical Engineers, U.K. (2007)
Fellow, Royal Society for the encouragement of Arts, Manufactures and Commerce, U.K. (2002).
Fellow, Institution of Engineering and Technology [formerly The Institution of Electrical Engineers, U.K. (1996)].
Fellow, Institute of Physics, London, U.K. (1992).
Fellow, Institute of Mathematics and its Applications, U.K. (1989).
Fellow, Cambridge Philosophical Society, Cambridge, U.K. (1979).
2018 - IEEE Distinguished Lecturer (EMBS, 2018-2019)
2014 - Academician of the Royal Academy of Engineering, U.K.
2012 - IEEE Communications Society Heinrich Hertz Award for Best Communications Letter.
2010 - Finland Distinguished Professor Award (2010-2014).
2010 - Glory of Bengal Award for “outstanding achievement in scientific research”.
2006 - Distinguished International Research Fellow at the Schulich School of Engineering, University of Calgary, Canada.
2000 - Freedom of the City of London.
2000 - Award from the Society for Machinery Failure Prevention Technology, a Division of theVibration Institute, U.S.A.
1999 - The Water Arbitration Prize, Mechanical Sciences and Technologies Division Award, ofthe Institution of Mechanical Engineers, U.K.
1998 - The Mountbatten Premium, Division Award of the Electronics & Communications Division, of the Institution of Electrical Engineers, U.K.
1984 - A five-year Advanced Fellowship awarded by the Science and Engineering Research Council, U.K.
1983 - Co-discoverer of the three particles known as W+, W-, and Z0 - three of the four quanta of the electroweak force. This discovery verified the unification of the electromagnetic force and the nuclear weak force. In recognition of this the 1984 Nobel Prize for Physics was awarded to two of his colleagues (Professors C Rubbia and S van der Meer) for their decisive roles in this project.