My name is Adarsh Jamadandi. I have a Master’s degree in Computer Science from Saarland University. My research interests include Graph Representation Learning and Geometric Deep Learning.

I am currently working as a research assistant at the SprintML Lab, trying to understand if GNNs are also susceptible to memorization.

I finished my master’s thesis at the Relational Machine Learning Lab under the supervision of Dr. Rebekka Burkholz.

Previously, I completed my Bachelors in Electronics and Communication Engineering with Dr. Uma Mudenagudi as my advisor.

My CV can be found here.

Updates

January , 2025 Now accepted at ICLR 2025! GNNs Getting ComFy : Community and Feature Similarity Guided Rewiring . Why does graph rewiring via spectral gap maximization work? Should we always maximize the spectral gap? What dictates the success of these methods? Would additional information such as node features and latent community structure help better rewire the graph? These are some of the questions we investigate in this work.
September , 2024 Accepted at NeurIPS 2024! Spectral Graph Pruning Against Over-Squashing and Over-Smoothing. We introduce the Braess Paradox for the first time in context of GNNs. We propose a novel graph pruning strategy that can mitigate both over-squashing and over-smoothing and bonus also find graph lottery tickets!
August, 2024 My master's thesis is now online! On the Importance of Graph-Task Alignment for Graph Neural Networks.
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.
November, 2020 Probabilistic Word Embeddings in Kinematic Space, accepted at ICPR, 2020!