PhD Student

Fabio Ferreira

Postal address
Institut für Informatik
Albert-Ludwigs-Universität Freiburg
Sekretariat Hutter/Maschinelles Lernen
Georges-Köhler-Allee 074
79110 Freiburg, Germany
Fax
+49 761 203-74217
Office
Building 74, Room 00-013
TwitterGoogleScholarGitHub

About

In 2020 I joined the Machine Learning Lab as a PhD student under the supervision of Frank Hutter. Before that I joined the Stanford AI Lab for my master’s thesis to work in Jeannette Bohg’s IPRL group on robotic manipulation. As a student research assistant, I also worked at Tamim Asfour’s H2T Lab with focus on robot perception. I hold a M. Sc. from KIT in CS/Machine Learning and am alumnus of the German National Academic Scholarship Foundation.

Research Interests

I am generally interested in learning to learn and meta-learning (learning algorithms). My current research focuses on generating learning environments (MDPs, image datasets, …) to devise performance surrogate models for efficient knowledge transfer.

Getting in touch with me for a student collaboration

If you are a very good student and interested in working with me on the above mentioned research interests, please feel free to reach out via e-mail. In order to assess a good fit, please answer the questions listed on this webpage. Also, if you have any nice material such as code, figures, paper summaries, work results or a paper in which you had major contributions in please attach them to your e-mail (if it’s unclear from the context please denote what your exact contributions were in this/these material/s). If you have already concrete ideas about what you would like to do in your project, please do not hesitate to communicate them as well.

Publications

2021

Ferreira, Fabio; Nierhoff, Thomas; Hutter, Frank

Learning Synthetic Environments for Reinforcement Learning with Evolution Strategies Journal Article

In: 2021.

2020

Rajan, Raghu; Diaz, Jessica Lizeth Borja; Guttikonda, Suresh; Ferreira, Fabio; Biedenkapp, André; Hutter, Frank

MDP Playground: Controlling Dimensions of Hardness in Reinforcement Learning Inproceedings

In: 2020.

Liu, Zhengying; Pavao, Adrien; Xu, Zhen; Escalera, Sergio; Ferreira, Fabio; Guyon, Isabelle; Hong, Sirui; Hutter, Frank; Ji, Rongrong; Junior, Julio C S Jacques; Li, Ge; Lindauer, Marius; Luo, Zhipeng; Madadi, Meysam; Nierhoff, Thomas; Niu, Kangning; Pan, Chunguang; Stoll, Danny; Treguer, Sebastien; Wang, Jin; Wang, Peng; Wu, Chenglin; Xiong, Youcheng; Zela, Arber; Zhang, Yang

Winning Solutions and Post-Challenge Analyses of the ChaLearn AutoDL Challenge 2019 Journal Article

In: IEEE Transactions on Pattern Analysis and Machine Intelligence, 43 (9), pp. 3108-3125, 2020.