Research

My research interests include Graph Representation Learning and Geometric Deep Learning. More specifically, I am interested in leveraging ideas from differential geometry and/or physics to design better representation learning algorithms for graphs.

Papers

• Spectral Pruning Against Over-Squashing and Over-Smoothing.
  Adarsh Jamadandi, Celia Rubio-Madrigal, Rebekka Burkholz. Pre-print, 2024.

• Graph of Thrones: Adversarial Perturbations dismantle Aristocracy in Graphs.
  Adarsh Jamadandi, Uma Mudenagudi. AAAI Student Poster, 2021. Extended Version in Differential Geometry meets Deep Learning - DiffGeo4DL (NeurIPS), 2020.

• Probabilistic Word Embeddings in Kinematic Space.
  Adarsh Jamadandi, Rishabh Tigadoli, Ramesh Tabib, Uma Mudenagudi. International Conference on Pattern Recognition (ICPR), 2020.

• Exemplar-based Underwater Image Enhancement Augmented by Wavelet Corrected Transforms.
  Adarsh Jamadandi, Uma Mudenagudi. Computer Vision and Pattern Recognition (CVPR Workshop, Oral), 2019.

• Learning Hierarchical Representations in Kinematic Space.
  Adarsh Jamadandi, Uma Mudenagudi. Graph Representation Learning Workshop, Neural Information Processing Systems (NeurIPS), 2019.

• PredGAN: a deep multi-scale video prediction framework for detecting anomalies in videos.
  Adarsh Jamadandi, Sunidhi Kotturshettar, Uma Mudenagudi. 11th Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP ‘18), 2018.