Model-Based Deep Learning For Sensor Selection
Speaker
Host
Abstract
Deep learning (DL) has emerged as a ubiquitous tool, tackling diverse challenges from distinguishing between cats and dogs to autonomous driving. Its efficacy often hinges on vast training datasets, yet concerns persist regarding interpretability, particularly in applications critical to human safety. Furthermore, labeled data scarcity complicates its application. In contrast, conventional signal and image processing methods leverage physical principles without necessitating extensive data, albeit requiring iterative convergence over numerous cycles.
This talk explores an alternative approach bridging these extremes: unrolling-based learning. This method constructs a neural network from iterative algorithms, combining the advantages of reduced data dependency, accelerated convergence, accuracy, and, crucially, interpretability. To illustrate these concepts practically, we delve into the sensor selection problem. Here, the objective is to identify a subset of sensors from a large pool. We discuss the significance of this problem, methodologies for resolution, and the application of model-based learning approaches.
About the Speaker
Dr. Satish Mulleti received the B.Eng. degree from the Electronics and Communication Engineering Department, Jalpaiguri Government Engineering College, India, in 2005, and the M.Eng. degree in electrical engineering from the Department of Electrical Engineering, Indian Institute of Technology Kanpur, India, in 2009. Subsequently, he worked as a Researcher with the Indian Space Research Organization (ISRO), India, and Tata Consultancy Services (TCS) Innovation Labs, Mumbai, India.
In 2011, he joined the Spectrum Lab, Department of Electrical Engineering, Indian Institute of Science, Bangalore, for his Ph.D. From 2017 to 2021, he was a Postdoctoral Fellow with the Department of Electrical Engineering, Technion Israel Institute of Technology, and Mathematics and Computer Science, Weizmann Institute of Science, Israel. Currently, he is an assistant professor at the Department of Electrical Engineering, Indian Institute of Technology (IIT) Bombay, India.
His research interests include sampling theory, particularly finite-rate-of-innovation signal sampling, compressive sensing, machine learning, blind deconvolution, sparse array signal processing, and spectral estimation.