1989-1993: PhD, Radboud University Nijmegen。 1993-1994: Postdoc, Beckman Institute, University of Illinois, Champaign-Urbana, USA。 1994-2004: Senior researcher, SNN and director of spin-off company SMART Research BV。 2004-2008: Assistant/associate Professor Radboud University Nijmegen, Computer Science。 2007- : Principal Investigator at the Institute for Computing and Information Sciences Affiliated Principal Investigator at the Donders Centre for Neuroscience。 2008- : Full Professor Radboud University Nijmegen, Computer Science。 2009-2014: Director of the Institute for Computing and Information Sciences。 My research is on artificial intelligence, in particular (Bayesian) machine learning. In a nutshell, we use probability calculus and statistics to design and understand "intelligent" systems that can learn from data. See my publications for recent and older results. In 2006, I received a prestigious Vici grant and in 2014 a TOP grant, both from NWO. Besides that, I am involved in several projects that concern applications in, among others, neuroimaging and bioinformatics. My research group is part of the section Intelligent Systems of iCIS, the best computer science institute within the Netherlands (according to this official report). I teach several courses in computer science and artificial intelligence. People from our group put together several suggestions for Master and Bachelor thesis projects that students can do under our guidance. Aankomende studenten: kijk op onze website om te zien waarom je in Nijmegen informatica of kunstmatige intelligentie moet komen studeren. Since 2003, I am Editor-in-Chief of the scientific journal Neurocomputing. We publish papers on topics ranging from computational neuroscience to machine learning. Neurocomputing's 2013 impact factor is 2.005. I am a principal investigator (PI) at and research director of iCIS and an affiliated PI at the Donders Centre for Neuroscience. If you really have to, you can check out my (short, not really up-to-date) cv. On October 9, 2009, I held my inaugural speech. Research interests： - Machine learning (approximate inference, multi-task learning, causal discovery dynamic Bayesian networks, preference learning, ...); - Applications to, among others, neuroimaging (brain-computer interfaces, fMRI analysis, ...) and bioinformatics (PPI networks, GWAS, ...)。 Articles; Max Hinne, Matthias Ekman, Ronald Janssen, Tom Heskes, and Marcel van Gerven. Probabilistic clustering of the human connectome identifies communities and hubs. PLOS ONE, 10(1):e0117179, 2015. [bib] Sanne Schoenmakers, Umut Gl, Marcel van Gerven, and Tom Heskes. Gaussian mixture models and semantic gating improve reconstructions from human brain activity. Frontiers in Computational Neuroscience, 8(173), 2015. [bib] Elena Sokolova, Martine Hoogman, Perry Groot, Tom Claassen, Alejandro Arias Vasquez, Jan Buitelaar, Barbara Franke, and Tom Heskes. Causal discovery in an adult ADHD data set suggests indirect link between DAT1 genetic variants and striatal brain activation during reward processing. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 9999B:1-8, 2015. [bib] Christiaan de Leeuw, Joris Mooij, Tom Heskes, and Danielle Posthuma. MAGMA: Generalized Gene-Set Analysis of GWAS Data. PLOS Computational Biology, 11(4):e1004219, 2015. [bib] Syed Saiden Abbas, Tjeerd Dijkstra, and Tom Heskes. A comparative study of cell classifiers for image-based high-throughput screening. BMC Bioinformatics, 15:342, 2014. [bib] Jesse Alama, Tom Heskes, Daniel K黨lwein, Evgeni Tsivtsivadze, and Josef Urban. Premise selection for mathematics by corpus analysis and kernel methods. Journal of Automated Reasoning, 52:191-213, 2014. [bib] Adriana Birlutiu, Florence d'Alch-Buc, and Tom Heskes. A Bayesian framework for combining protein and network topology information for predicting protein-protein interactions. IEEE/ACM Transactions on Computational Biology and Bioinformatics (accepted), 2014. [bib] Tom Heskes, Rob Eisinga, and Rainer Breitling. A fast algorithm for determining bounds and accurate approximate p-values of the rank product statistic for replicate experiments. BMC Bioinformatics, 15:367, 2014. [bib] Max Hinne, Luca Ambrogioni, Ronald Janssen, Tom Heskes, and Marcel van Gerven. Structurally-informed Bayesian functional connectivity analysis. NeuroImage, 86:294-305, 2014. [bib] Max Hinne, Alex Lenkoski, Tom Heskes, and Marcel van Gerven. Efficient sampling of Gaussian graphical models using conditional Bayes factors. Stat, 3(1):326-336, 2014. [bib] Ronald Janssen, Max Hinne, Tom Heskes, and Marcel van Gerven. Quantifying uncertainty in brain network measures using Bayesian connectomics. Frontiers in Computational Neuroscience, 8(126), 2014. [bib] Frank Koopmans, Niels Cornelisse, Tom Heskes, and Tjeerd Dijkstra. An empirical Bayesian random censoring threshold model improves detection of differentially abundant proteins. Journal of Proteome Research, 13:3871-3880, 2014. [bib] Syed Saiden Abbas, Tjeerd Dijkstra, and Tom Heskes. A direct comparison of visual discrimination of shape and size on a large range of aspect ratios. Vision Research, 91:84-92, 2013. [bib] Syed Saiden Abbas, Tom Heskes, Onno Zoeter, and Tjeerd Dijkstra. A Bayesian psychophysical model for angular variables. Journal of Mathematical Psychology, 57:134-139, 2013. [bib]