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Professor Dino Sejdinovic

Associate Professor of Statistics

Fellow at Mansfield College

OxCSML group

+44 (0)1865 285386 (Direct)

Biographical Sketch

Dino Sejdinovic joined the Department in October 2014 as a Departmental Lecturer and became an Associate Professor in January 2016. He previously held postdoctoral positions at the Gatsby Computational Neuroscience Unit, University College London and at the Department of Mathematics, University of Bristol. He receivedPhD in Electrical and Electronic Engineering from the Univeristy of Bristol (2009) and a Diplom in Mathematics and Theoretical Computer Science from the University of Sarajevo (2006). He is a Turing Fellow of the Alan Turing Institute.

Research Interests

  • Statistical machine learning
  • Reproducing kernel Hilbert spaces
  • Nonparametric hypothesis testing
  • Measures of association and multivariate interaction
  • Tradeoffs between computational and statistical efficiency
  • Coding and information theory

Selected Publications

  • H. C. L. Law, C. Yau, and D. Sejdinovic, Testing and Learning on Distributions with Symmetric Noise Invariance, in Advances in Neural Information Processing Systems (NIPS), vol. 30, 2017.
  • S. Flaxman, Y. W. Teh, and D. Sejdinovic, Poisson Intensity Estimation with Reproducing Kernels, in International Conference on Artificial Intelligence and Statistics (AISTATS), 2017, PMLR 54:270–279.
  • Q. Zhang, S. Filippi, A. Gretton, and D. Sejdinovic, Large-Scale Kernel Methods for Independence Testing, Statistics and Computing, 2017.
  • J. Mitrovic, D. Sejdinovic, and Y. W. Teh, DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression, in International Conference on Machine Learning (ICML), 2016, PMLR 48:1482–1491.
  • H. Strathmann, D. Sejdinovic, S. Livingstone, Z. Szabo, and A. Gretton, Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families, in Advances in Neural Information Processing Systems (NIPS), vol. 28, 2015, 955–963.
  • D. Sejdinovic, B. Sriperumbudur, A. Gretton, and K. Fukumizu, Equivalence of distance-based and RKHS-based statistics in hypothesis testing, Annals of Statistics, vol. 41, no. 5, 2263–2291, Oct. 2013.