#### KEYNOTE SPEAKERS

#### Dr. Sundeep Prabhakar Chepuri (Assistant Professor at the Department of ECE at the Indian Institute of Science (IISc), Bangalore, India.)

**Short Bio:** Sundeep Prabhakar Chepuri received his M.Sc. degree (cum laude) in electrical engineering and Ph.D. degree (cum laude) from the Delft University of Technology, The Netherlands, in July 2011 and January 2016, respectively. He has held positions at Robert Bosch, India, during 2007-2009, Holst Centre/imec-nl, The Netherlands, during 2010-2011. He was a Postdoctoral researcher at the Delft University of Technology, The Netherlands, a visiting researcher at University of Minnesota, USA, and a visiting lecturer at Aalto University, Finland. Currently, he is an Assistant Professor at the Department of ECE at the Indian Institute of Science (IISc) in Bangalore, India.

Dr. Chepuri was a recipient of the Best Student Paper Award at the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) in 2015. He is currently the Associate Editor of the EURASIP Journal on Advances in Signal Processing (JASP) and a member of the EURASIP Signal Processing for Multisensor Systemsâ€™ Special Area Team. His research interests include mathematical signal processing, statistical inference and learning, applied to communication systems, network sciences, and computational imaging.

**“Sparse Sampling and Learning on Graphs”**

Abstract : Ubiquitous sensors generate prohibitively large datasets. Large volumes of data that we collect nowadays are complex in nature as they are collected on manifolds, irregular domains, networks, or point clouds. Extending classical signal processing concepts and tools to represent, interpret, and analyze signals defined on irregular graph domains is an emerging area of research known as graph signal processing.

In this talk, to begin with, we present near-optimal greedy methods to sparsely sample signals defined over irregular graph domains. We discuss how the underlying geometrical structure of the domain on which the data is defined can be exploited for sampling.

Most of the graph signal processing algorithms assume that the graph is given or can be appropriately defined. For scenarios where there is no initial graph available or we desire to modify a known graph as new data becomes available, we present methods to learn a sparse graph that best explains the available data. Using the observations recorded at a single node and a known excitation signal, algebraic algorithms to estimate the graph structure will be presented.

#### Tom J Moir (Associate Professor at Auckland University of Technology (AUT) New Zealand)

**Short Bio:** Tom Moir is originally from Aberdeenshire in Scotland but now works as an associate professor at Auckland University of Technology (AUT) New Zealand. He obtained his first degree in Control-Engineering in 1979 and a Ph.D in 1983. Since then he has explored the areas of control-systems, signal-processing and communications engineering. He has published over a hundred journals papers in these areas. He is mostly interested in adaptive algorithms as applied to noise-reduction but has an all-round interest in cross-disciplinary theory.

**“Some insight into feedback as used in signal, systems and classical mathematic algorithms”**

Abstract: The practice of negative-feedback is usually credited to early engineers in the industrial revolution. For example the work of James Watt in the 18^{th} century and his steam-engine governor invention, and the even older work of Christiaan Huygens who applied a similar idea to millstones in windmills as long ago as the 17^{th} century. The theory did not arise until the work of James-Clark Maxwell in the 19^{th} century who was the first to analyse a centrifugal governor and in doing so unwittingly started the science of control-systems. In more modern days of the 20^{th} century, Oliver Heaviside used operator calculus and transfer-functions to revolutionise the area of electrical engineering. The use of block-diagrams became commonplace when replacing differential equations. This much simplified and clearer approach is popular to the present day. This talk will uncover other contributions in the mathematical and signal processing sciences which have remained buried. It will be shown that many mathematicians and engineers have unwittingly been using feedback without even recognising it. Most adaptive algorithms in signal processing for example use feedback and new insight can be gained from revisiting old problems from a new perspective. The talk will look at many examples of problems in signals, systems, mathematics and electronics to illustrate these ideas.