IIT Kanpur Intensive Training School (ITS) on PYTHON












for Machine Learning, Neural Networks and Deep Learning










2^nd to 22^nd December 2023

Prof. Aditya K. Jagannatham

Prof. Aditya K. Jagannatham is a Professor in the Electrical Engineering department at IIT Kanpur, where he holds the Arun Kumar Chair Professorship. He is one of India’s best-known experts and trainers on 5G/ 6G technologies and AI/ML. He received his Bachelors degree from the Indian Institute of Technology, Bombay and M.S. and Ph.D. degrees from the University of California, San Diego, U.S.A. From April '07 to May '09 he was employed as a senior wireless systems engineer at Qualcomm, San Diego, California, where he was a part of the Qualcomm CDMA technologies (QCT) division. His research interests are in the area of next generation wireless networks, with a special emphasis on various 6G technologies such as Orthogonal Time Frequency Space (OTFS) modulation, Intelligent Reflective Surface (IRS), Terahertz (THz) and Visible Light Communication (VLC) and several 5G technologies such as massive MIMO, mmWave MIMO, FBMC, NOMA, Full Duplex and others.

His research articles have been published extensively in leading international IEEE journals and conferences. He has successfully conducted several Massive Open Online Courses (MOOCs) on various topics such as MIMO OFDM Wireless Systems, Probability and Random Processes, Signals and Systems, Principles of Communication Systems, Optimization, Machine Learning, which have been widely adopted and appreciated. A book authored by him titled Principles of Modern Wireless Communications Systems has been published by McGraw Hill Education and comprehensively covers several key aspects of modern wireless technologies. He has been recognized with several awards described below.

Suraj Srivastava

Prof. Suraj Srivastava received the M.Tech. degree in Electronics and Communication Engineering from Indian Institute of Technology Roorkee and Ph.D. degree in Electrical Engineering from Indian Institute of Technology Kanpur, India, in 2022. He was previously employed as a Staff-I systems design engineer with Broadcom Research India, and as a lead engineer with Samsung Research India. He also worked as a senior lead engineer in Qualcomm India Pvt. Ltd., Bangalore, from October 2022 to November 2023. His research interests include applications of Sparse Signal Processing in 5G Wireless Systems, mmWave and Tera-Hertz Communication, Optimization and Machine Learning. He was awarded Qualcomm Innovation Fellowships (QIF) in years 2018 and 2022. He is currently an Assistant Professor at the Department of Electrical Engineering, Indian Institute of Technology Jodhpur.

List salient publications of Suraj Srivastava

• Suraj Srivastava, R. K. Singh, A. K. Jagannatham and L. Hanzo "Bayesian Learning Aided Simultaneous Row and Group Sparse Channel Estimation in Orthogonal Time Frequency Space Modulated MIMO systems", Accepted in IEEE Transactions on Communications, 2022


• Suraj Srivastava, Prem Singh, Amrita Mishra, Aditya K. Jagannatham and Lajos Hanzo,"Sparse Bayesian Learning Aided Estimation of Doubly-Selective MIMO Channels for Filter Bank Multicarrier Systems", Accepted in IEEE Transactions on Communications, 2022


• Suraj Srivastava, C. S. K. Patro, A. K. Jagannatham and L. Hanzo, "Sparse, Group-Sparse and Online Bayesian Learning Aided Channel Estimation for Doubly-Selective mmWave Hybrid MIMO OFDM Systems", IEEE Transactions on Communications, vol. 69, pp. 5843-5858, Sept. 2021


• Suraj Srivastava, R. K. Singh, A. K. Jagannatham and L. Hanzo "Bayesian Learning Aided Sparse Channel Estimation for Orthogonal Time Frequency Space Modulated Systems", IEEE Transactions on Vehicular Technology, vol. 70, pp. 8343-8348, Aug. 2021


• Suraj Srivastava, J. Nath and A. K. Jagannatham, "Data Aided Doubly-Selective CSI Estimation using Affine-Precoded Superimposed Pilots in Millimeter Wave MIMO-OFDM Systems", IEEE Transactions on Vehicular Technology, vol. 70, pp. 6983-6998, July 2021


• M. Majumder, H. Saxena, Suraj Srivastava, and A. K. Jagannatham, "Optimal Bit Allocation-Based Hybrid Precoder-Combiner Design Techniques for mmWave MIMO-OFDM Systems", IEEE Access, vol. 9, pp. 54109-54125, April 2021
  
  
• Suraj Srivastava, P. Singh, A. K. Jagannatham, A. Karandikar and L. Hanzo, "Bayesian Learning-Based Doubly-Selective Sparse Channel Estimation for Millimeter Wave Hybrid MIMO-FBMC-OQAM", IEEE Transactions on Communications, vol. 69, pp. 529-543, Jan. 2021


• Suraj Srivastava, P. Sharma, S. Dwivedi, A. K. Jagannatham and L. Hanzo, "Fast Block LMS Based Estimation of Angularly Sparse Channels for Hybrid Wideband Millimeter Wave MIMO Systems", IEEE Transactions on Vehicular Technology, vol. 70, pp. 666-681, Jan. 2021


• K. P. Rajput, A. Kumar, Suraj Srivastava, A. K. Jagannatham and L. Hanzo, "Bayesian Learning-based Linear Decentralized Sparse Parameter Estimation in MIMO Wireless Sensor Networks with Perfect and Imperfect CSI", IEEE Transactions on Communications, vol. 69, pp. 6236-6250, Sept. 2021


• P. Singh, Suraj Srivastava, A. K. Jagannatham and L. Hanzo, "Second-Order Statistics-Based Semi-Blind Techniques for Channel Estimation in Millimeter-Wave MIMO Analog and Hybrid Beamforming", IEEE Transactions on Communications, vol. 68, pp. 6886-6901, Nov. 2020


• Suraj Srivastava, M. S. Kumar, A. Mishra, S. Chopra, A. K. Jagannatham and L. Hanzo, "Sparse Doubly-Selective Channel Estimation Techniques for OSTBC MIMO-OFDM Systems: A Hierarchical Bayesian Kalman Filter Based Approach", IEEE Transactions on Communications, vol. 68, pp. 4844-4858, Aug. 2020


• A. Chawla, P. S. Kumar, Suraj Srivastava and A. K. Jagannatham "Centralized and Distributed Millimeter Wave Massive MIMO based Data Fusion with Perfect and Bayesian Learning (BL)-based Imperfect CSI", Accepted in IEEE Transactions on Communications


• Suraj Srivastava, A. Mishra, A. Rajoriya, A. K. Jagannatham and G. Ascheid, "Quasi-Static and Time-Selective Channel Estimation for Block-Sparse Millimeter Wave Hybrid MIMO Systems: Sparse Bayesian Learning (SBL) Based Approaches", IEEE Transactions on Signal Processing, vol. 67, pp. 1251-1266, Mar. 2019