Prof. Dr. Debarghya Ghoshdastidar
Academic Career and Research Areas
Prof. Ghoshdastidar (born 1987) conducts research in the theory of machine learning, artificial intelligence and network science. The main focus of his research is on the statistical understanding and interpretability of methods used in machine learning. His works provide new insights and algorithms for decision problems, involving complex data such as networks and preference relations, that arise in various fields including neuroscience, crowdsourcing and computer vision.
Prof. Ghoshdastidar obtained a bachelor degree in electrical engineering in 2010 from Jadavpur University, India. He obtained a master degree in 2012 from the Indian Institute of Science, where he further completed his doctoral research in 2016. Subsequently, he joined the University of Tuebingen as a post-doctoral researcher and led a junior research group funded by the Baden-Wuerttemberg Foundation. In September 2019, he joined the Department of Informatics as a Tenure Track Professor for Theoretical Foundations of Artificial Intelligence.
Awards
- Member of the Baden-Wuerttemberg Eliteprogramm for Postdocs (2017)
- Google Ph.D. Fellowship (2013)
Key Publications
D. Ghoshdastidar, M. Gutzeit, A. Carpentier, U. Von Luxburg. Two-sample hypothesis testing for inhomogeneous random graphs. The Annals of Statistics, 48 (4): 2208-2229, 2020.
AbstractD. Ghoshdastidar, M. Perrot, U. Von Luxburg. Foundations of comparison-based hierarchical clustering. Advances in Neural Information Processing Systems (NeurIPS), 32, 2019
AbstractD. Ghoshdastidar, M. Gutzeit, A. Carpentier, U. von Luxburg. Two-sample tests for large random graphs using network statistics. Annual Conference on Learning Theory (COLT), PMLR 65: 954-977, 2017.
AbstractD. Ghoshdastidar, A. Dukkipati. Consistency of spectral hypergraph partitioning under planted partition model. The Annals of Statistics, 45 (1): 289-315, 2017.
AbstractD. Ghoshdastidar, A. Dukkipati. Consistency of spectral partitioning of uniform hypergraphs under planted partition model. Advances in Neural Information Processing Systems (NIPS), 27: 397-405, 2014.
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