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July 14, 2026

CNI Connect - July 2026 Edition

CNI Connect - July 2026 Edition

CNI Connect - July 2026 Edition
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CNI CONNECT

Monthly Newsletter from CNI

Issue - July 2026

Upcoming Events


This month CNI looks forward to SPARC workshop on Distributed Learning and Optimization and the 6th CNI Summer School 2026.

SPARC Workshop on Distributed Learning and Optimization

The Centre for Networked Intelligence (CNI) is organizing a SPARC Workshop, a one-day workshop to be held on 13 July, featuring technical talks by researchers from IITs, IISc, Rutgers University, and Google DeepMind, focusing on recent advances in distributed learning, optimization, and related areas.

The workshop offers an opportunity to engage with experts, exchange ideas, and connect with the broader research community. Further details are available on the CNI workshop webpage.

6th CNI Summer School 2026

CNI is looking forward to welcoming participants to the 6th CNI Summer School 2026, beginning on 20 July. Joining us this year is Prof. Aditya Mahajan (McGill University) who will teach the five-day course on model approximation techniques in Markov Decision Processes (MDPs) and Partially Observable Markov Decision Processes (POMDPs). The programme offers a structured introduction to decision-making under uncertainty, stochastic optimization, and their connections to reinforcement learning. Learn more on the CNI summer school webpage.

What’s cooking at CNI?


Online Self-Study Course Content

CNI's self-study course on Probability and Random Processes is now available for self-paced learning. Equivalent to a graduate-level semester course on foundations of probability, the 28 modules feature detailed lecture notes paired with video lectures. These videos are produced using AI that converts LaTeX lecture notes to video lectures. The content was extracted, rendered as Beamer slides, and a text explanation was generated, which was fed to text-to-speech models for voice-over creation. Finally, the slides and voice were woven together to form the final videos. All these processes involved AI assistance under the oversight of a subject expert. To preserve the real experience of the course in the video lectures, the voice-over is generated based on a cloned voice model of the instructor’s actual voice. We welcome your feedback to help us refine this technique and introduce new features!


Research Spotlight

This month’s spotlight is on a paper co-authored by CNI faculty member T V Prabhakar, accepted at 2026 IEEE Vehicular Networking Conference (VNC).

To Charge or Not to Charge: Autonomous Battery Digital Twin for Electric Vehicles

Lakshmi Poola, Kaushik Raghunandan Hasabnis, and Prabhakar Venkata T V

Running out of battery before reaching the charging station remains a major concern for electric vehicle users. This work presents a system that estimates the battery’s remaining charge in real time and uses this information to recommend whether to charge or continue driving, ensuring that the vehicle can safely reach its destination. This increases user confidence in electric vehicle adoption.

Highlights from Recent Talks

Memory Efficient Information Retrieval and AI Inference

Prof. Narasimha Reddy discussed the growing memory demands of information retrieval systems and large language models. He presented practical approaches to make AI inference more efficient by combining system-level optimizations with algorithmic techniques, demonstrating how thoughtful co-design can reduce both memory usage and computational costs without compromising performance.

Structured Reinforcement Learning in NextG Cellular Networks

Prof. Srinivas Shakkottai spoke about bringing reinforcement learning into real-time control of next-generation cellular networks. By combining AI with structured decision-making, he demonstrated how wireless networks can respond more quickly to changing demands while improving performance for applications such as media streaming and other latency-sensitive services.

Planning Decisions Under Uncertainty: A Data-driven Approach

Prof. Natarajan Gautam discussed how better decisions can be made when key information is uncertain or incomplete. Through examples ranging from bike-sharing and restaurant bookings to air traffic management, he showed how data-driven methods can improve planning by going beyond conventional estimation and optimization approaches.

Phase Transition in the 2-Choices Opinion Dynamics under Stochastic Node Failures

Prof. Arpan Mukhopadhyay talked about how groups reach consensus when some participants make random or unreliable decisions. His talk showed that collective decision-making remains remarkably robust up to a critical level of failures, offering new insights into the resilience of distributed systems and networked algorithms.


Join the CNI Team!



As our research expands, so does our team.

CNI is inviting applications for the following openings in the Distributed Systems Lab:

  1. Research Associate: For experienced professionals and fresh graduates planning to get into systems research.

  2. Research Intern: For students who are interested in experimental research in systems/networks while pursuing their studies.


More details at the CNI opportunities page.

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