Interest in Gaussian Processes, Bayesian Inference, Online Learning and Julia programming.
Our paper “Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models” got accepted at the AISTATS 2020 conference, check it out! Here is a short explaining Twitter thread.
Our paper “Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation” got accepted for the UAI conference 2019, see you in Tel Aviv
Recently gave a short talk at Berlin Julia Meetup (join!), here are the slides and the notebook is here
Our paper “Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation” got accepted at the AAAI conference 2019!
AISTATS 20’ “Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models”, T. Galy-Fajou, F. Wenzel, M. Opper
UAI 19’ “Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation”, T. Galy-Fajou, F. Wenzel, C. Donner, M. Kloft, M. Opper
AAAI 19’ “Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation”, F. Wenzel, T. Galy-Fajou, C. Donner, M. Kloft, M. Opper
ECML 17’ “Bayesian Nonlinear Support Vector Machines for Big Data”, F. Wenzel, T. Galy-Fajou, M. Deutsch, M. Kloft
Current : PhD in Machine Learning under the supervision of Professor Manfred Opper.
2016-2017 : Research intern @ Humboldt University of Berlin
2015-2016 : C++ Software developper in MRI development @ Siemens Healthcare in Lausanne
2012-2015 : Teaching assistant for Particle Physics, General Physics @ University of Edinburgh & EPFL
2015 : Internship in Particle Physics @ Lancaster University
You can find a more complete CV here