The First ESIT-D2I Competition
Channel Charting · Robust Learning · Unlearning Noisy Contributions
A student competition at the intersection of data science, machine learning, and information theory — held in conjunction with ESIT 2026, Nordfjordeid, Norway.
See the Final ResultsFour teams crossed the bridge. Here is the final leaderboard.
This year's challenge is inspired by the Viking legend of Bifröst — the shimmering bridge connecting Midgard to Asgard, where Valkyries ride across the sky to bring fallen heroes to Valhalla.
Back then, reaching Asgard required divine intervention, a winged horse, and — ideally — having died heroically in battle. Today's Nordic explorers, however, have 5G coverage reaching roughly 99.7% of households in Norway.
So we ask a simple question: If somewhere between Midgard and Asgard there were at least a stable cellular connection, and you were given noisy, partial, and imperfect measurements of the wireless channel — can you chart your way to Bifröst?
Welcome to the first edition of the European School of Information Theory Data-to-Information (D2I) Competition, organized by the IEEE Information Theory Society Student and Outreach Committee. Participants will work with real-world Channel State Information (CSI) measurements, designing methods that are robust to label noise, feature corruption, and faulty data contributions.
Throughout the competition, everything lived one click away: the Slack community for announcements and team-building, and the two Kaggle competitions hosting the data, baseline code, and live leaderboards. These platforms are now closed — they remain listed below as a record of the competition.
Phase 1 · In-Person
Phase 2 · Online
Final Session · Online
Open to all · Live online
Selected teams present their approaches and results on channel charting, robust learning, and unlearning noisy data contributions — working toward better localization quality from real-world CSI measurements. Everyone is welcome to attend.
Date
July 7, 2026
Time
16:00 – 17:00 CEST (GMT+2)
The live event is generously sponsored by ELLIS Society, Madrid Unit.
Registration is now closed. During the competition, participants registered through an online form, indicating whether they were joining as an individual or as part of a team, and whether they planned to attend ESIT in person.
Team formation was finalized on June 4, after the in-person ESIT session — giving participants an opportunity to meet and form teams on-site. Each team was required to include at least one member attending ESIT in person. Remote participants were assisted by the organizers in forming teams.
Registration closed
Final live event sponsored by ELLIS Society, Madrid Unit.
Participants will use a dataset collected in a realistic wireless environment (the DICHASUS dataset, Arena2036 research campus) where multiple remote antenna arrays capture Channel State Information (CSI) measurements. CSI describes how a wireless signal propagates through the environment — capturing reflections, scattering, and attenuation — and can be seen as a rich "fingerprint" of location.
The dataset is divided into two components:
Full dataset and tutorials: dichasus.inue.uni-stuttgart.de. All data, utilities, and the submission interface are hosted on Kaggle — see the Platforms section above for the Task 1 and Task 2 competition pages.
We gratefully acknowledge Prof. Stephan ten Brink, Florian Euchner, and Phillip Stephan for building and documenting this dataset and making it freely available to the research community.
Task 0 · Foundations (discussed at ESIT)
This preliminary task introduces the core elements of CSI-based localization and serves as a warm-up for the competition. Participants will explore basic localization (mapping CSI to positions via supervised learning), basic channel charting (low-dimensional representations preserving spatial structure), and trajectory-aware processing (temporal consistency across consecutive measurements).
Task 1 · Main Challenge
In realistic scenarios, both labels and measurements are imperfect. Participants design models that remain accurate and robust when data quality degrades. Two types of corruption are considered:
The goal is to balance accuracy and reliability, avoiding catastrophic localization errors while maintaining good average performance.
Hosted on Kaggle · ESIT-D2I Task 1 — closed
Task 2 · Advanced Challenge
A subset of users contributes corrupted trajectories due to faulty GPS or sensor drift. Participants will:
Hosted on Kaggle · ESIT-D2I Task 2 — closed
High localization accuracy is crucial, but equally critical is minimizing catastrophic failures. The combined loss function balances two metrics:
Average L2 distance between true and predicted positions. Reflects overall accuracy.
Smallest radius enclosing 90% of predicted locations around true positions. Measures reliability and consistency.
Performance is evaluated in terms of the lowest achievable combined loss. Submissions are made through Kaggle.
Organizing Committee
Technical Support & Competition Design