Adarsh Jamadandi

Hi 👋

I’m Adarsh Jamadandi, a CNRS doctoral researcher at IRISA, Rennes, working on diffusion models for graphs with Dr. Nicolas Keriven. I study the learning dynamics of discrete diffusion models for graph generation — when they generalize versus memorize — with the goal of building more efficient models that scale to large graphs.

Previously I was a research assistant at SprintML with Franziska Boenisch and Adam Dziedzic, where we built the first framework for studying memorization in GNNs.

I did my Master’s at Saarland University, with a thesis at the Relational ML Lab under Rebekka Burkholz on mitigating over-squashing and over-smoothing to improve GNN generalization.

I obtained my Bachelor’s in Electronics and Communication Engineering from India, with a thesis on video anomaly detection advised by Dr. Uma Mudenagudi.

My CV can be found here

Updates

Sept 2026 Now accepted at BeNTo Workshop, NeurIPS 2026!! Your Discrete Graph Diffusion Model is Secretly Memorizing

Sept 2026 Now accepted at NeurIPS 2026! When Edge Independence Fails: Joint Graph Diffusion with Latent Sociability Priors

Aug 2026 New pre-print alert! Your Discrete Graph Diffusion Model is Secretly Memorizing

May 2026 New pre-print alert! When Edge Independence Fails: Joint Graph Diffusion with Latent Sociability Priors

September 2025 Now accepted at NeurIPS 2025! Memorization in Graph Neural Networks

September 2025 Excited to start as a PhD student at IRISA, Rennes.

May 2025 New pre-print alert! Memorization in Graph Neural Networks.

March 2025 Started as a research assistant at SprintML Lab under the supervision of Franziska Boenisch and Adam Dziedzic. I will be working on understanding if GNNs also memorize.

January 2025 Now accepted at ICLR 2025! GNNs Getting ComFy: Community and Feature Similarity Guided Rewiring .

September 2024 Accepted at NeurIPS 2024! Spectral Graph Pruning Against Over-Squashing and Over-Smoothing.

August 2024 My master's thesis is now online! On the Importance of Graph-Task Alignment for Graph Neural Networks.

November 2022 Started as a research assistant at the Relational Machine Learning Group, under Dr. Rebekka Burkholz. I will be working on improving the generalizability of GNNs by tackling problems like over-squashing and over-smoothing.

December 2020 Graph of Thrones: Adversarial Perturbations dismantle Aristocracy in Graphs is accepted at AAAI'2021 Student Poster Program, and the extended version is accepted at DiffGeo4DL, NeurIPS 2020.