CNI CONNECT

Monthly Newsletter from CNI

Issue - May 2026

Upcoming Summer School

With the seminar series taking a short break over the summer, we look ahead to the 6th CNI Summer School 2026, with registrations now open.

This year we will focus on model approximation techniques in Partially Observable Markov Decision Processes (POMDPs), covering core ideas in stochastic systems and optimization, along with their connections to reinforcement learning. The summer school will be led by Prof. Aditya Mahajan (McGill University).

Interested in participating? Explore the programme details and submit your application through the registration form.

From the Workshop: UK–India Conversations on Future Networks

Last month, CNI organised the UK–India Workshop on Future Communications and Networking, bringing together researchers and practitioners from academia and industry for a full day of technical exchange and collaboration.

The workshop featured 17 speakers across academia and industry, with discussions spanning wireless systems, networking technologies, satellite communications, and future paradigms beyond 5G. Talks by Prof. Steve Hailes and Prof. Gerard Parr set the tone, addressing robustness in wireless sensing systems and the security of critical submarine communication infrastructure, highlighting both emerging vulnerabilities and design challenges in real-world deployments.

These themes were complemented by a broader set of contributions spanning next-generation wireless systems and network design. Prof. Ayalvadi Ganesh talked about information dissemination over random graphs, while Dr. Dilip Krishnaswamy discussed distributed space-based networking architectures. Prof. Neelesh Mehta presented advances in RIS-enabled communication systems, bridging theoretical design with deployment challenges.

Further, Prof. Anurag Kumar and Anindya Saha focused on efficient resource allocation and energy-aware network design, while Sheela Prabhakar shared insights from practical deployments of private 5G networks. Prof. Huzur Saran and Prof. David Koilpillai outlined broader system-level visions for 6G, including AI-native networks and India’s roadmap for future communication technologies.

The workshop also featured contributions on optical communication scaling by Prof. Deepa Venkitesh, data-driven clustering methods by Prof. Srikrishna Bhashyam, UAV communication systems by Dr. Sreenath Ramnath, and low-latency coding and transmission strategies by Srivathsa Acharya, Prof. Radhakrishna Ganti. Prof. A. Chockalingam concluded the workshop with perspectives on waveform design for joint communication and sensing.

The event saw strong participation, with over 180 attendees joining across in-person and online formats, reflecting wide interest across academia and industry.

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.

Highlights from Recent Talks

Reliable Inference at Scale Using Graph Structure

Mansi Sood (MIT) explored how graph structure can be leveraged to make large-scale inference both reliable and efficient. She discussed approaches for learning from distributed data and reducing the computational cost of parameter estimation, highlighting how structural insights can guide the design of scalable algorithms.

Waveform Design in Modern Radar Systems

Prof. M. R. Bhavani Shankar (SnT, University of Luxembourg) discussed how waveform design influences the capabilities of modern radar systems. He highlighted how advances in bandwidth, architectures, and algorithms are enabling robust and efficient sensing, while outlining key trade-offs and opportunities across emerging applications.

The Friendship Paradox for Social Networks

Prof. Frank den Hollander (Leiden University) unpacked the “friendship paradox”, the idea that, on average, your friends tend to have more friends than you do. Using graph-based models, he showed why this occurs and how it can be understood and quantified in large, random networks.

Who is Important in a Network? Bringing Order to Centrality Measures

Prof. Remco van der Hofstad (Eindhoven University of Technology) looked at how we define “importance” in networks. He discussed widely used centrality measures like PageRank and introduced ways to compare them, offering a clearer view of how different metrics capture influence in large, complex networks.

A Sanov-type Theorem for Marked Sparse Random Graphs and its Applications

Prof. Sarath Yasodharan (IIT Bombay) discussed how rare events can be understood in sparse random graph models. He showed how probabilities of such events can be characterized in a tractable way, and illustrated how these ideas apply to analysing stochastic networks under unusual or extreme conditions.

How Do Models for Disease Spread Help Us Make Better Policies?

Prof. Gautam Menon (Ashoka University) spoke about how disease spread models can inform real-world policy decisions. Drawing on work with BharatSim, he showed how large-scale simulations help answer questions on interventions, school reopening, and outbreak control, while also reflecting on what makes such models useful in practice.

Consensus in the Weighted Voter Model with Noise-free and Noisy Observations

Prof. Ayalvadi Ganesh (University of Bristol) discussed how groups reach consensus in decision-making settings. Using the weighted voter model, he showed how agreement emerges in networks, and examined how noise or errors in individual observations can influence collective outcomes.