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Catered Lunch and Poster Viewing (in Workshop Room)
Gustavo Stolovitzky · Prabhu Pradhan · Pablo Duboue · Zhiwen Tang · Aleksei Natekin · Elizabeth Bondi-Kelly · Xavier Bouthillier · Stephanie Milani · Heimo Müller · Andreas T. Holzinger · Stefan Harrer · Ben Day · Andrey Ustyuzhanin · William Guss · Mahtab Mirmomeni

Fri Dec 13 12:15 PM -- 02:00 PM (PST) @
Event URL: https://sites.google.com/a/chalearn.org/workshop/ciml2019/accepted »

Accepted Posters

Kandinsky Patterns: An open toolbox for creating explainable machine learning challenges Heimo Muller · Andreas Holzinger

MOCA: An Unsupervised Algorithm for Optimal Aggregation of Challenge Submissions Robert Vogel · Mehmet Eren Ahsen · Gustavo A. Stolovitzky

FDL: Mission Support Challenge Luís F. Simões · Ben Day · Vinutha M. Shreenath · Callum Wilson

From data challenges to collaborative gig science. Coopetitive research process and platform Andrey Ustyuzhanin · Mikhail Belous · Leyla Khatbullina · Giles Strong

Smart(er) Machine Learning for Practitioners Prabhu Pradhan

Improving Reproducibility of Benchmarks Xavier Bouthillier

Guaranteeing Reproducibility in Deep Learning Competitions Brandon Houghton

Organizing crowd-sourced AI challenges in enterprise environments: opportunities and challenges Mahtab Mirmomeni · Isabell Kiral · Subhrajit Roy · Todd Mummert · Alan Braz · Jason Tsay · Jianbin Tang · Umar Asif · Thomas Schaffter · Eren Mehmet · Bruno De Assis Marques · Stefan Maetschke · Rania Khalaf · Michal Rosen-Zvi · John Cohn · Gustavo Stolovitzky · Stefan Harrer

WikiCities: a Feature Engineering Educational Resource Pablo Duboue

Reinforcement Learning Meets Information Seeking: Dynamic Search Challenge Zhiwen Tang · Grace Hui Yang

AI Journey 2019: School Tests Solving Competition Alexey Natekin · Peter Romov · Valentin Malykh

A BIRDSAI View for Conservation Elizabeth Bondi · Milind Tambe · Raghav Jain · Palash Aggrawal · Saket Anand · Robert Hannaford · Ashish Kapoor · Jim Piavis · Shital Shah · Lucas Joppa · Bistra Dilkina

Author Information

Gustavo Stolovitzky (IBM Research)
Prabhu Pradhan (FGIET (AKTU) | IIT Genoa | IISc Bangalore)

[Prabhu](https://prabhupradhan.github.io) is a Research Assistant at MPI-IS Tübingen, working on Robustness and Confounding in Machine Learning.

Pablo Duboue (Textualization Software Ltd.)

Pablo Duboue has a PhD in Computer Science from Columbia University and was part of the IBM Watson team that beat the Jeopardy! Champions in 2011. He splits his time between teaching Machine Learning, doing open research, contributing to free software projects, and consulting for start-ups. He has taught in three different countries and done joint research with more than 50 co-authors. Recent career highlights include a best paper award in the Canadian AI conference industrial track and consulting for a start-up acquired by Intel Corp.

Zhiwen Tang (Georgetown University)
Aleksei Natekin (OpenDataScience.ru)
Elizabeth Bondi-Kelly (Harvard University)
Xavier Bouthillier (Université de Montréal)
Stephanie Milani (Carnegie Mellon University)
Heimo Müller (Medical University Graz)
Andreas T. Holzinger (Medical University Graz and Technical University Graz)

Andreas Holzinger promotes a synergistic approach to Human-Centred AI (HCAI) and has pioneered in interactive machine learning (iML) with the human-in-the-loop. He promotes an integrated machine learning approach with the goal to augment human intelligence with artificial intelligence to help to solve problems in health informatics. He works towards explainable AI and Causability, ultimately fostering ethical responsible machine learning, trust and acceptance for AI.

Stefan Harrer (IBM Research)
Ben Day (University of Cambridge)
Andrey Ustyuzhanin (NRU HSE, YSDA)
William Guss (Carnegie Mellon University)
Mahtab Mirmomeni (IBM Research)

Mahtab is a computer scientist and machine learning researcher in IBM Research Australia where she works on detecting epileptic seizures from time series, multi-modal data. She is also the lead solutions architect for IBM's crowdsourced AI platform for enterprises.

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