The European Laboratory for Learning and Intelligent Systems offers an interdisciplinary PhD program. The ELLIS PhD program is a key element of the ELLIS initiative and its goal is to foster and educate the best talent in machine learning related research areas by pairing outstanding students with leading academic and industrial researchers in Europe. The program supports excellent PhDs across Europe by giving them access to leading research through boot camps, summer schools and workshops of the ELLIS programs. Every PhD student is supervised by one ELLIS fellow/scholar and one ELLIS member from a different country and conducts a 1 year exchange at the other location.
This year Philipp Hennig is among the "Top 40 under 40" in the category Society and Science.
For the eleventh time the magazine Capital has chosen the "Young Elite - the Top 40 under 40". According to Capital, these are the most important talents that shape our country.
Project Title PANAMA
Max Planck Research Group Leader Philipp Hennig will use the five-year funding with a total of 1.45 million Euros for his “Probabilistic Automated Numerical Analysis in Machine learning and Artificial intelligence (PANAMA)” project.
An upcoming workshop in June 2017 will explore applications of probabilistic numerics.
A recent meeting at the Leibniz Centre for Computer Science highlights the ongoing significance of analytic nonparametric models for machine learning.
Max Planck Society funds focussed research program on uncertainty in computation
Our research group will be funded as an independent entity within the Max Planck Institute for Intelligent Systems from December 2016. An official set-up phase starts in September 2016. This also brings an end to our beloved status as an Emmy Noether group.
PhD student will present her work on optimization for deep learning
Maren Mahsereci's paper on probabilistic line searches for stochastic optimization has been selected for a full oral presentation at the flagship conference of machine learning.
PhD student will present his work on probabilistic solvers for differential equations
Michael Schober' paper on probabilistic solvers for ordinary differential equations has been selected for a full oral presentation at the flagship conference of machine learning.