Centre for Networked Intelligence

Dear All, Networks Seminar, supported by the Centre for Networked Intelligence, is a technical discussion forum in topics including but not limited to computer networks, machine learning, signal processing, and information theory. The seminar series has a webpage hosted at https://cni.iisc.ac.in/seminars/. You are invited to the following seminar held as part of this series. Title: Future of Mobile: Extreme Connectivity, AI-Native, and Beyond Communications Speaker: Dr. Harish Viswanathan, Head of Radio Systems Research Lab, Nokia Bell Labs Time: 12:00 PM - 1:00 PM (IST) Date: 14 November 2025 Venue: Golden Jubilee Hall and Online on Zoom Zoom link: https://us06web.zoom.us/j/83388976389?pwd=XcpO3GhLxsR14a7SVbPx33HQQa1jbt.1 Zoom Meeting ID: 833 8897 6389, Pass Code: NSSIISc YouTube Livestream: https://www.youtube.com/watch?v=tzFs5RkC_Lo Webpage Link: https://cni.iisc.ac.in/seminars/2025-11-14/ Abstract: With the start of standardization of 6G being imminent, the shape that future mobile networks will take is becoming increasingly clearer. We begin with an overview of the current industry trends and future human needs that are driving the contours of the next generation of mobile networks. We will then describe the technologies that will be at the foundation of the three main themes, namely extreme connectivity, AI-native, and integrated sensing and communications that are likely to characterize future mobile networks. Extreme connectivity in new spectrum bands will rely on advances in massive MIMO enabling its evolution to extreme MIMO. Pervasive use of AI techniques across the layers of the protocol stack with a solid management framework will pave the way for AI-native networks. Integrating sensing into communications coupled with an API exposure framework will offer new opportunities for monetization. We will discuss the various opportunities and challenges across these novel technologies. Bio: Dr. Harish Viswanathan is Head of Radio Systems Research Lab at Nokia Bell Labs. He received the B. Tech. degree from the Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai, India and the M.S. and Ph.D. degrees from the School of Electrical Engineering, Cornell University, Ithaca, NY. Since joining Bell Labs in October 1997, he has worked extensively on wireless research ranging from physical layer to network architecture and protocols including multiple antenna technology for cellular wireless networks, multi-hop relays, network optimization, network architecture, and IoT communications. He has published extensively with over 150 publications. From 2007 to 2015, Harish was in the Corp CTO organization, where as a CTO Partner he advised the Corporate CTO on Technology Strategy through in-depth analysis of emerging technology and market needs. He is a Fellow of the IEEE and a Bell Labs Fellow. He received the 2023 Edison Patent Award from the R&D Council of New Jersey. More details on: https://www.nokia.com/people/harish-viswanathan/ ALL ARE WELCOME. Thank you, CNI Seminar Series Organizing Committee.

Dear All, Networks Seminar, supported by the Centre for Networked Intelligence, is a technical discussion forum in topics including but not limited to computer networks, machine learning, signal processing, and information theory. The seminar series has a webpage hosted at https://cni.iisc.ac.in/seminars/. You are invited to the following seminar held as part of this series. Title: CONGO: Compressive Online Gradient Optimization Speaker: Prof. Srinivas Shakkottai, Professor, Texas A&M University Time: 5:30 PM - 6:30 PM (IST) Date: 18 November 2025 Venue: Online on Zoom Zoom link: https://us06web.zoom.us/j/83388976389?pwd=XcpO3GhLxsR14a7SVbPx33HQQa1jbt.1 Zoom Meeting ID: 833 8897 6389, Pass Code: NSSIISc YouTube Livestream: https://www.youtube.com/watch?v=wLs8yG8PNhg Webpage Link: https://cni.iisc.ac.in/seminars/2025-11-18/ Abstract: We address the challenge of zeroth-order online convex optimization where the objective function’s gradient exhibits sparsity, meaning that only a small number of dimensions possess nonzero gradients. Our goal is to leverage this sparsity to obtain useful estimates of the objective function’s gradient even when only a limited number of function samples are available. Our motivation stems from the optimization of large-scale queueing networks that process time-sensitive jobs. In such systems, a job may pass through many queues in sequence before producing an output, and the service time at each queue depends on the resources allocated to it. Since resources are costly, the end-to-end latency for jobs must be balanced against the total resource cost. Although the number of queues can be large, the latency function typically reacts to resource changes in only a few of them, rendering the gradient sparse. We tackle this problem by introducing the Compressive Online Gradient Optimization (CONGO) framework, which enables compressive sensing methods—previously applied to stochastic optimization—to achieve regret bounds with optimal dependence on the time horizon, without the full problem dimension appearing in the bound. For specific algorithms, we reduce the number of samples required per gradient estimate to scale with the gradient’s sparsity rather than its full dimensionality. Numerical simulations and real-world microservices benchmarks demonstrate CONGO’s superiority over gradient descent approaches that do not account for sparsity. Joint work with Jeremy Carleton, Prathik Vijaykumar, Divyanshu Saxena, Dheeraj Narasimha, and Aditya Akella. Bio: Srinivas Shakkottai received his Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana–Champaign in 2007, followed by a postdoctoral position in Management Science and Engineering at Stanford University. He joined Texas A&M University in 2008, where he is currently a Professor in the Department of Electrical and Computer Engineering and holds a courtesy appointment in the Department of Computer Science and Engineering. His research interests include wireless networks, reinforcement learning, caching and content distribution, multi-agent learning and game theory, networked markets, and data analytics. He co-directs the Learning and Emerging Network Systems (LENS) Laboratory and the RELLIS Spectrum Innovation Laboratory (RSIL) at Texas A&M University. He has served as an Associate Editor for the IEEE/ACM Transactions on Networking and the IEEE Transactions on Wireless Communications. Dr. Shakkottai is the recipient of the Defense Threat Reduction Agency (DTRA) Young Investigator Award and the National Science Foundation (NSF) CAREER Award, as well as research awards from Cisco and Google. His papers have received honors at venues such as ACM MobiHoc, ACM eEnergy, and the International Conference on Learning Representations (ICLR). At Texas A&M, he has received several distinctions, including the Outstanding Professor Award, the Select Young Faculty Fellowship, the Engineering Genesis Award (twice), and the Dean of Engineering Excellence Award. More details on: https://cesg.tamu.edu/faculty/srinivas-shakkottai/ ALL ARE WELCOME. Thank you, CNI Seminar Series Organizing Committee.