B. V. Rao is an adjunct professor in Chennai Mathematical Institute. He worked as a professor, then as a distinguished scientist, and then as an NHBM visiting professor at ISI Kolkata, between the period 1974 and 2009. He received his bachelor's degree in math and physics from the Andhra university, Waltair, 1963. He received his master's degree in statistics in Osmania University, Hyderabad, in 1965, and his Ph.D. degree from ISI Kolkata in 1970. His research interests include Set theory, Analysis, and Probability.

Upamanyu Madhow is Professor of Electrical and Computer Engineering at the University of California, Santa Barbara. His research interests broadly span communications, signal processing and networking, with current emphasis on millimeter wave communication, and on distributed and bio-inspired approaches to networking, inference and learning. He received his bachelor's degree in electrical engineering from the Indian Institute of Technology, Kanpur, in 1985, and his Ph. D. degree in electrical engineering from the University of Illinois, Urbana-Champaign in 1990.

Erdal Arikan is a Professor in the department of Electical-Electronics Engineering at the Bilkent University, Ankara, Turkey. He received the B.S. degree from the California Institute of Technology, Pasadena, in 1981 and the S.M. and Ph.D. degrees from the Massachusetts Institute of Technology, Cambridge, in 1982 and 1985, respectively, all in electrical engineering. He is working at the Bilkent University since 1987. His research interests include Coding theory and Information theory.

Random quantities that `arise in practice' are not `violently random'; they remain pretty close to their mean value with overwhelming probability. Quantitative versions of this statement are `concentration inequalities'. Such inequalities can be used --- to great advantage --- in understanding, analysing and controlling these quantities. These inequalities have applications in diverse areas: graph theory, computer science, statistics, biology, spin glasses and so on. Starting at basic level, this course is aimed at understanding some of these techniques, results and their use.

mmWave represents the next frontier in wireless communication, providing "effectively unlimited" spectrum for short to medium range networks, due to the huge amounts of available spectrum and the aggressive spatial reuse enabled by highly directive links. Similarly, short-range mmWave radar is a key enabler for sensing applications such as gesture recognition and vehicular situational awareness. mmWave systems differ fundamentally from existing wireless systems because of the order of magnitude smaller carrier wavelength, and the order of magnitude higher available bandwidth. In this mini-course, we discuss some of these key differences and their design consequences, ranging from hardware/signal processing co-design to network protocols.

Polar coding is a method that can achieve the Shannon limits in a wide range of source and channel coding scenarios. The goal of this mini-course is to give an in depth coverage of the fundamentals of polar coding, as well as discussing a range of selected theoretical and practical topics. In particular, a comparison of polar coding with other codes for various application scenarios will be presented.

1. Overview and Tchebycheff
2. Cramer, Chernoff, Hoeffding-Azuma
3. Cramer, Chernoff, Hoeffding-Azuma continued
4. Effron-Stein and McDiarmid (bounded differences)
5. Stein and Chatterjee
6. Entropy, log-Sobolev, and Herbst
7. Entropy, log-Sobolev, and Herbst continued
8. Talagrand

1. Overview: mmWave characteristics, concept systems, technical challenges
2. The mm wave channel: diversity, multiplexing, blockage
3. Steering large arrays: compressive and scan-based approaches
4. Fundamentals of compressive estimation: theory and algorithms
5. Networking with highly directional links
6. Networking (contd.)
7. Signal Processing at high bandwidths
8. Short-range mmWave radar

1. Overview of polarization and polar coding
2. Encoding, decoding and code construction
3. Polarization details: Martingale analysis, rate of polarization
4. Density evolution and polar code construction
5. Polar codes in practice: performance, complexity of implementation, comparisons with other codes
6. Generalizations: Non-binary codes, non-uniform inputs, larger kernels, universal constructions
7. Polar coding in multi-user settings: multiple-access, degraded broadcast, Slepian-Wolf, Wyner-Ziv, Gelfand-Pinsker, etc.
8. Open problems, discussion, and topics for future research