Prof. Dr. Debarghya Ghoshdastidar

Department

Informatics

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)

Ghoshdastidar D, von Luxburg U: „Practical methods for graph two-sample testing“. Advances in Neural Information Processing Systems (Neurips). 2018; 31: 3019-3028.

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Haghiri S, Ghoshdastidar D, von Luxburg U: „Comparison based nearest neighbor search“. Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS). 2017; 54: 851-859.

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Ghoshdastidar D, Dukkipati A: „Consistency of spectral hypergraph partitioning under planted partition model". The Annals of Statistics. 2017; 45(1): 289-315.

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Ghoshdastidar D, Dukkipati A: „Spectral clustering using multilinear SVD: Analysis, approximations and algorithms“. In: AAAI Conference on Artificial Intelligence (AAAI). 2015.

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Ghoshdastidar D, Dukkipati A: „Consistency of spectral partitioning of uniform hypergraphs under planted partition model“. In: Advances in Neural Information Processing Systems (NIPS). 2014: 397-405.

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