ICE seminar: « Time-series representations of polynomials leading to better understanding and accurate estimations »

NANDI seminar Data Modelling November 2025Abstract

Data modelling goes back to 19th century when Gauss and Legendre invented the least-squares method for polynomials. Time-series representations followed later. Data modelling is a fast-growing topic in 21st century. This presentation will offer novel, insightful, and recent results. For example, all polynomials of degree q can be represented by a time-series of the same order with a constant. One major distinction of this formulation lies in its ability to describe any qth degree polynomial data with only one unknown in stark contrast to the (q + 1) unknowns needed in a polynomial model. From noisy data, the degree of a polynomial, the additive noise as well as the polynomial coefficient corresponding to the highest polynomial degree can be estimated more accurately than the current methods, without using a polynomial model.

Bio

Professor Nandi received his PhD in Physics from the University of Cambridge (Trinity College). He held academic positions in several universities, including Oxford, Imperial College London, Strathclyde, and Liverpool as well as Finland Distinguished Professorship. In 2013 he joined Brunel University of London.
In 1983 Professor Nandi co-discovered the three fundamental particles known as W+, W− and Z0, 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 machine health monitoring, functional magnetic resonance data, gene expression data, communications, and biomedical data. He made fundamental theoretical and algorithmic contributions to many aspects of signal processing and machine learning. He has much expertise in “Big Data”. Professor Nandi has authored over 650 technical publications, including 320 journal papers as well as six books, entitled Image Segmentation: Principles, Techniques, and Applications (Wiley, 2022), Condition Monitoring with Vibration Signals: Compressive Sampling and Learning Algorithms for Rotating Machines (Wiley, 2020), 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 91 (Google Scholar) and ERDOS number is 2.
Professor Nandi is a Fellow of the Royal Academy of Engineering and a Fellow of six other institutions including the IEEE. In 2023, he has been honoured by the Academia Europaea and the Academia Scientiarum et Artium Europaea. He has received many awards, including the IEEE Transactions on Radiation and Plasma Medical Sciences Best Paper Award in 2025, the IEEE 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 in 1999, and the Mountbatten Premium of the Institution of Electrical Engineers in 1998. Professor Nandi was an IEEE Distinguished Lecturer (EMBS, 2018-2019).