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Time for Skoltech Machine Learning Summer School

The six Phd students that are chosen to participate in the Machine Learning Summer School at Skoltech, are now in Moscow and have started their course. They have exciting days ahead - during two weeks they will be a part of a high profiled education together with prominent artificial intelligence researchers from all over the world.
On place at Skoltech in Moscow: Carl-Johan Hoel, Oskar Allerbo, Emilio Jorge, Constantin Cronrath, Gang Li, Julius Pettersson

The Machine Learning Summer school (MLLS) takes the form of an intensive 2 week experience with a full immersion into Skoltech's innovative and internationally research-oriented community. By this, the participants will be able to get deeper into their ML-knowledge. The students will meet and make deep relations with international students, academics and professionals within their own area.

That the course is popular and highly ranked is no doubt, approx 700 students were applying to the around 100 seats on the course. Read more about The Machine Learning Summerschool 

Behind the participation is AI Innovation of Sweden in collaboration with CHAIR -  Chalmers AI Research Centre, that handle the scholaship and the arrangement. 

“The summer school is an excellent opportunity for our PhD students to learn the latest progress in research and innovation. The curious Swedish PhDs will increase their views and learn a lot when the meet and work with about 100 peers” tells Professor Mats Hanson, Senior Advisor at AI Innovation of Sweden.

The Phd´s that are selected from the open Swedish MLLSs scholarship call are: 

  • Constantin Cronrath, with the poster: Safe Reinforcement Learning for Adaptive Control of Industrial Systems.
  • Oskar Allerbo, with the poster: PrAda-net - Adaptive Lasso for Interpretable Neural Networks
  • Carl-Johan Hoel, with the poster: Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving.
  • Emilio Jorge, with the poster: Spectral Analysis of Kernel and Neural Embeddings: Optimization and Generalization.
  • Gang Li, with the poster: Machine learning applied to predicting microorganism growth temperatures and enzyme catalytic optima.
  • Julius Pettersson, with the poster: Human Intention Prediction using Virtual Reality and Eye-tracking - in Collaborative Robot Environments.