Krikamol Muandet
Curriculum vitae

Research statement


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


My name is Krikamol Muandet (ไกรกมล หมื่นเดช). I am a lecturer at the Department of Mathematics, Faculty of Science, Mahidol University. I am also a research scientist affiliated with the Empirical Inference Department at Max Planck Institute for Intelligent Systems, Tübingen, Germany. My research interest lies in the area of machine learning and its applications. Topics of interest includes, for example, kernel methods, Bayesian nonparametric, large-scale learning, and causal inference. When I am not doing research, I like reading books and watching movies as well as doing outdoor sports like swimming, bouldering, climbing, and snowboarding.

Previously, I was a PhD student at the Empirical Inference Department at Max Planck Institute for Intelligent Systems, Tübingen, Germany where I have worked primarily with Prof. Bernhard Schölkopf. 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 Dr. 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, to honor Prof. Vladimir Vapnik, on the occasion of his 75th birthday.


  • I am attending Dagstuhl seminar on "New Directions for Learning with Kernels and Gaussian Processes" during Nov 27 - Dec 2, 2016.
  • A new paper entitled "Kernel Mean Embedding of Distributions : A Review and Beyonds" is available on ArXiv.
  • I am serving as one of the program managers for NIPS2016. See the summary of the review process.
  • I am visiting the Department of Statistics, University of Oxford from Nov 29-Dec 6, 2015.
  • I passed a Ph.D. oral examination on September 30, 2015 with summa cum laude.
  • Our paper entitled Kernel Mean Shrinkage Estimators has been accepted to Journal of Machine Learning Research. See ArXiv version here.
  • I will co-teach a practical on kernel methods at the Machine Learning Summer School (MLSS2015).