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About CodaLab

CodaLab is an open-source platform that provides an ecosystem for conducting computational research in a more efficient, reproducible, and collaborative manner. There are two aspects of CodaLab: worksheets and competitions.

Worksheets allow you to capture complex research pipelines in a reproducible way and create "executable papers". Use any data format or programming language — great for the power user! Codalab worksheets has a public instance hosted by Stanford University.

Competitions bring together the entire community to tackle the most challenging data and computational problems today. You can win prizes and also create your own competition. Codalab competitions has a public instance hosted by Université Paris-Saclay.

The CodaLab Team

Percy Liang is an assistant professor of Computer Science at Stanford University. His primary research areas are machine learning and natural language processing. He leads the development of CodaLab in close collaboration with Microsoft Research and the rest of the community.

Isabelle Guyon is full professor at UPSud University Paris-Saclay and president of ChaLearn a non-profit organization dedicated to running machine learning competitions. Her research interested include automatic machine learning, transfer learning, and causal discovery. Isabelle served as an advisor in the development of the CodaLab competition platform and pioneered the implementation of several challenges on Codalab.

Evelyne Viegas is a Director at Microsoft Research responsible for the outreach artificial intelligence program. She leads the CodaLab project working in collaboration with Isabelle Guyon, Percy Liang and the machine learning and artificial intelligence communities.

Sergio Escalera

Sergio Escalera is adjunct professor at Universitat Oberta de Catalunya, Aalborg University, and Dalhousie University and a member of the Visual and Computational Learning consolidated research group of Catalonia and a member of the Computer Vision Center at UAB. He is series editor of The Springer Series on Challenges in Machine Learning. He is Editor-in-Chief of American Journal of Intelligent Systems and editorial board member of more than 5 international journals. He is vice-president of ChaLearn Challenges in Machine Learning, leading ChaLearn Looking at People events.

Xavier Baro

Xavier Baró Solé is a professor in the Computer Science Department at the Universitat Autònoma de Barcelona (UAB), a Teacher Assistant at the Universitat de Barcelona (UB), and an Associate Professor at the Faculty of Computer Science, Multimedia and Telecommunication in the Universitat Oberta de Catalunya (UOC). His research interests are related to machine learning, evolutionary computation, and statistical pattern recognition.


CodaLab has received important contributions from many people, and we would like to thank their efforts in making CodaLab what it is today:

Benjamin Aaron Bearce, Pujun Bhatnagar, Feng Bin, Justin Carden, Richard Caruana, Francis Cleary, Laurent Darré, Sergio Escalera, Xiawei Guo, Jennifer He, Ivan Judson, Arslan Kabeer, James Keith, Lori Ada Kilty, Shaunak Kishore, Stephen Koo, Anne-Catherine Letournel, Zhengying Liu, Zhenwu Liu, Adrien Pavao, Pragnya Maduskar, Simon Mercer, Arthur Pesah, Christophe Poulain, Lukasz Romaszko, Loïc Sarrazin, Laurent Senta, Xavier Baro Sole, Lisheng Sun, Tyler Thomas, Dinh Tuan Tran, Sebastien Treguer, Bailey Trefts, Nic Threfts, Wei-Wei Tu, Cedric Vachaudez, Paul Viola, Erick Watson, Zhen Xu, Tony Yang, Flavio Zhingri, Michael Zyskowski.