SCPP Workshop

Stochastic Processes
on Networks

Exploring modern mathematical paradigms of diffusion, random walks, percolation, and dynamic processes running across physical and computational networks.

Date

August 2 - 3, 2026

Distinguished Lecturers

A. Ganesh & V. Shneer

Coding Challenge

SPoN Vibe (Teams of 2)

Interaction tip: Click the background to inject random walkers & explore dynamic diffusion!

Understanding SPoN 2026

Networks constitute the backbone of modern structural representations, ranging from neural systems and social platforms to power grids and epidemic pathways. The SPoN workshop addresses how system dynamics behave over these topologies.

Day 1 Focus: Pedagogy & Foundational Lectures covering limits of random walks and network queueing theories.

Day 2 Focus: Research talks showcasing leading global findings on percolation and structural network evolution.

Coordinated Vibe: Hands-on team coding event where participants implement networks with rapid code development loops.

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Distinguished Lecturers

Get to know the leading academics delivering deep-dive lectures on Day 1.

AG

Ayalvadi Ganesh

University of Bristol

Ayalvadi Ganesh is a premier researcher operating at the boundaries of queuing theory, stochastic networks, and epidemical processes on graphs. His work details how localized rules dictate macroscopic phase transitions in dynamic networks.

Queuing Networks Epidemics on Graphs Stochastic Approximation
Topic: Rumour spreading on networks
VS

Vsevolod (Seva) Shneer

Heriot-Watt University

Seva Shneer is well known for his substantial contributions in applied probability, queueing networks, and stochastic models for wireless communication networks. He covers spatial models where stochastic flow interaction dictates operational throughput.

Applied Probability Spatial Queueing Random Graphs
Title: First passage percolation

Workshop Schedule

Both days of foundational series lectures & research presentations at a glance.

Day 1 · Sunday, Aug 2, 2026

09:30 - 10:20 Lecture 1: Ayalvadi Ganesh
10:20 - 10:50 SPoN Vibe Coding Registration
10:50 - 11:15 Coffee Break
11:15 - 12:05 Lecture 2: Ayalvadi Ganesh
12:10 - 13:00 Lecture 1: Seva Shneer
13:00 - 14:00 Lunch Break
14:00 - 14:50 Lecture 3: Ayalvadi Ganesh
14:50 - 15:40 Lecture 2: Seva Shneer
15:40 - 16:05 High Tea
16:05 - 16:55 Lecture 3: Seva Shneer
17:00 - 18:30 SPoN Vibe Coding Competition + Demos

Day 2 · Monday, Aug 3, 2026

09:30 - 10:15 Talk 1
10:15 - 11:00 Talk 2
11:00-11:30 Coffee Break & Networking
11:30-12:15 Talk 3
12:15 - 13:00 Short / Lightning talks by students
13:00 - 14:00 Lunch Break
14:00 - 14:40 Talk 4
14:40 - 15:20 Talk 5
15:20 - 15:45 High Tea
15:45 - 16:25 Talk 6
16:25 - 17:05 Talk 7
17:05 - 17:30 Vote of thanks and prize distribution
Teams of 2 Challenge

SPoN Vibe Coding Competition

Test your speed, algorithmic knowledge, and intuitive reasoning while "vibe coding" stochastic processes on networks in a competitive marathon. Bring your teammate and craft unique simulations!

Team Composition

Exactly 2 members per team. Share planning, execution, and present together on Day 2.

The Prompt

Build a stochastic simulation/visualizer or make a video representing a stochastic process on a network. More details during the workshop.

Exciting Prizes

Prizes worth up to INR 10000 to be won.

Workshop FAQs

Essential information for attendees, students, and participants.

Who can attend SPoN 2026?

The lectures are tailored for graduate students, researchers, and advanced undergraduates interested in probability theory, operations research, computer science, and network physics.

Do I need to sign up for Vibe Coding separately?

Yes. The registration will be conducted during the workshop. You need to registed for the workshop in order to participate in the SPoN vibe coding competition. Workshop registration is free for IITB students and faculty and other invited individuals.

What background is required for lectures?

Basic probability (Markov chains, continuous-time processes) and rudimentary graph theory will help you extract the maximum learning value from the sessions.