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Department of Mathematics
Faculty of Science, Mahidol University

272 Rama VI Rd. Rajchathevi
Bangkok 10400, Thailand
Tel. +66 0-2201-5344
Fax. +66 0-2201-5343

Empirical Inference Department
Max Planck Institute for Intelligent Systems

Spemannstrasse 38, 72076 Tübingen, Germany
Tel. +49 (0)7071 601 554
Fax. +49 (0)7071 601 552


Hi there. My name is Krikamol Muandet (ไกรกมล หมื่นเดช). I am currently a lecturer at the Department of Mathematics, Faculty of Science, Mahidol University. I am also an affiliated research scientist with the Empirical Inference Department at Max Planck Institute for Intelligent Systems, Tübingen, Germany. My research interest lies in the area of machine learning, its theory, and applications. I am particularly interested in, for example, statistical learning theory, kernel methods, Bayesian nonparametric, large-scale learning, and causal inference (see a full list of my publications for details). When I am not doing research, I enjoy reading books and watching movies as well as doing outdoor sports like swimming, bouldering, climbing, and snowboarding (if the weather permits).

Previously, I was a PhD student at the Empirical Inference Department, Max Planck Institute for Intelligent Systems, Tübingen, Germany where I have worked primarily with Prof. Bernhard Schölkopf. I obtained the doctoral degree (summa cum laude) from the University of Tübingen. I previously obtained a master's degree with distinction in machine learning from University College London (UCL), United Kingdom. At UCL, I worked primarily with Prof. Yee Whye Teh. (M.Sc. thesis advisor) at the Gatsby Computational Neuroscience Unit and Prof. John Shawe-Taylor (M.Sc. Tutor) at the Center for Computational Statistics and Machine Learning. During my PhD, I was a visiting scholar at the Institute of Statistical Mathematics, Japan; Center for Cosmology and Particle Physics, New York University; Palomar Observatory in San Diego; American Museum of Natural History, and Institut für Stochastik und Anwendungen, University of Stuttgart, among others.

In 2011, it was a great honour for me to co-organize a Festschrift symposium together with my PhD advisor, Prof. Bernhard Schölkopf, and Yevgeny Seldin to honor Prof. Vladimir Vapnik, on the occasion of his 75th birthday. I also helped organize the 29th Neural Information Processing Systems (NIPS 2016), which took place in Barcelona, Spain (together with Ulrike von Luxburg, Isabelle Guyon, Behzad Tabibian, Rohit Babbar, and several other people). In December 2016, I was also invited to participate in the Dagstuhl Seminar in New Directions for Learning with Kernels and Gaussian Processes. At the seminar, we discussed various prospects of kernel methods in machine learning.

I always seek new collaboration. If you are interested in working with me, I will be glad to hear from you.


Kernel Mean Embedding of Distributions: A Review and Beyond
K. Muandet, K. Fukumizu, B. Sriperumbudur and B. Schölkopf
Foundations and Trends in Machine Learning: Vol. 10: No. 1-2, pp 1-141.

ISBN: 978-1-68083-288-4
Published: 28 Jun 2017

Full Publication



I feel really fortunate to have worked with these wonderful people.

  • Bernhard Schölkopf (MPI-IS, Germany)
  • Kenji Fukumizu (ISM, Japan)
  • Bharath Sriperumbudur (PennState, USA)
  • Yee Whye Teh (Oxford, UK)
  • Kun Zhang (CMU, USA)
  • David Balduzzi (DeepMind, UK)
  • Arthur Gretton (Gatsby Unit, UCL, UK)
  • Francesco Dinuzzo (Amazon)
  • Ilya Tolstikhin (MPI-IS, Germany)
  • David Lopez-Paz (Facebook AI Research)
  • Zhikun Wang (Google)
  • Stefan Harmeling (Dusseldorf)
  • Jonas Peters (U of Copenhagen, Denmark)
  • Sanparith Marukatat (NECTEC, Thailand)
  • Cholwich Nattee (SIIT, Thailand)
  • David Hogg (NYU, USA)
  • Rob Fergus (Facebook and NYU, USA)
  • To be updated soon!