Can AI assess suicide risk the way a clinician does — and show its evidence? Now in its 3rd edition, this challenge asks models not just to predict, but to justify.
The challenge A dual-objective task scored jointly. Subtask 1 — classify a Reddit user into one of four clinically grounded risk levels (indicator, ideation, behavior, attempt). Subtask 2 — return the supporting clinical evidence: psychological risk factors (e.g., access to means, prior self-harm, trauma) and protective factors (e.g., social support, coping, meaning of life), plus the verbatim post spans that signal risk.
The data Built on the Protective Factor-Aware (PFA) dataset — 237 Reddit users and 2,515 posts from r/SuicideWatch (2010–2022), expert-annotated with high agreement (Fleiss’ κ = 0.84 for risk, 0.79 for factors) and released in a privacy-protected format.
How you win Macro-F1 on each subtask, combined as S = 0.6·S1 + 0.4·S2 on a hidden test set with a live public leaderboard. Source code and a qualifying report are required for finalist eligibility.
Track record Grew from 21 teams (2024) to 36 teams (2025), with finalists invited to present in Macau.
Organizers Prof. Qing Li’s team at The Hong Kong Polytechnic University, with City University of Hong Kong.
Solar filaments trigger the storms that knock out power grids, GPS, and satellites — yet no operational system has tracked them since around 2016. Help reopen the window.
The challenge Produce pixel-precise segmentations of solar filaments in full-disk H-Alpha images — capturing fine structures (barbs), separating faint material from ground-based noise, and detecting each filament as one coherent object. Any method is welcome, from classical image processing to deep neural networks.
The data MAGFiLO — the largest gold-standard filament dataset ever released (Nature Scientific Data, 2024): 10,244 manually annotated filaments across 1,593 GONG observations, with polygon masks, spines, bounding boxes, and chirality labels. Built over 1.5 years by ~40 annotators across three institutions.
How you win IoU, precision, recall, AP@IoU, hit/miss rate, and the organizers’ Multi-scale IoU (MIoU) for fine structures. Hosted on Kaggle with custom-metric scoring; source code required.
Platform & prizes Kaggle. Cash prizes and/or IEEE Big Data 2026 registration for top teams, with invited talks at the proposed SABiD workshop. NSF-supported.
Organizers Azim Ahmadzadeh (Univ. of Missouri–St. Louis, lead), Dustin Kempton (Georgia State), Qin Li (NJIT), and Alexei Pevtsov (NSF National Solar Observatory).
FinReason Cup invites teams to develop and evaluate financial AI systems across three complementary tasks: verifiable financial reasoning, market-neutral hedging, and targeted audit verification. The competition evaluates both the outputs of these systems and the evidence needed to check their results.
Competition website: the-finai.github.io/IEEE-bigdata-cup
Competition tasks
- Task 1 Verifiable Financial Chain Reasoning: Solve multi-step financial problems and submit typed final answers together with values for the published intermediate reasoning slots. The scorer reports Final answer and Reasoning steps scores separately. Public training data, local development data, unlabeled leaderboard-development questions, and test questions are available through the Task 1 hub.
- Task 2 Market Neutral Hedging: Select an asset pair and manage a zero-net-dollar position over time using point-in-time prices, news, and corporate filings. Evaluation measures include cumulative return, Sharpe ratio, and maximum drawdown. Additional live-trading rounds run on 19–23 October, with the final action due on 23 October at 15:25 New York time. Each action must meet its round deadline.
- Task 3 Financial Audit Verification: Check targeted numeric facts in SEC EDGAR XBRL filing materials by comparing reported values with values implied by calculation relationships. This task concerns numeric-fact verification, not a full financial-statement audit. Practice, development, and Test submission services are available.
How to participate
Competition files use organizer-hosted, task-specific submission platforms linked from the competition website. Task 1 is open for direct uploads. It requires no pre-registration, letter of intent, organizer approval, access code, or participant account.
For Task 1, development submissions receive scores and a receipt, and each team’s best eligible result appears on the public leaderboard. Test submissions receive an acceptance receipt only; test scores and rankings remain hidden until the final results release. Follow the published task guide for required files, fields, validation, and submission limits.
Task 1 participant hub | Task 2 live-trading hub and submission link | Task 3 participant hub
Task 2 live-trading actions must meet the published per-round deadlines on 19–23 October; the final action is due on 23 October at 15:25 New York time. Task 3 practice submissions are not ranked; development submissions receive scores and rankings, and Test submissions receive an acceptance receipt and determine the final result. Follow the task hubs for submission instructions.
Organizer team
The organizer team is led by The Fin AI, with contributors affiliated with MBZUAI, McGill University, Stevens Institute of Technology, Yale University, and the University of Manchester. These affiliations do not imply institutional sponsorship.
Important dates
All dates are in 2026. Competition submissions and Working Notes have separate deadlines. The conference deadline of 20 November for final materials and organizer overviews remains unchanged.
AoE means Anywhere on Earth (UTC−12). The 25 October system/Test submission deadline does not extend Task 2 live-trading action deadlines: each action must meet its round deadline, with the final action due on 23 October at 15:25 New York time. The 25 November winner announcement is separate from the 27 October verified final results.
Full schedule and submission links on the FinReason Cup website
| Date |
Milestone |
| 25 October, 23:59 AoE | System/Test submissions due; Task 2 actions must meet earlier per-round deadlines |
| 27 October | Verified final results |
| 2 November, 23:59 AoE | Working Notes papers due through CyberChair SC03 |
| 9 November | Review feedback and acceptance decisions |
| 16 November, 23:59 AoE | Participant camera-ready papers due |
| 20 November | Organizer overview paper due to the conference |
| 25 November | Winning teams announced |
| 14–17 December | IEEE Big Data 2026 in Phoenix, Arizona, USA |
Working Notes and publication
Teams seeking final ranking and awards must submit Working Notes describing their system through the FinReason Cup SC03 track in CyberChair. Papers may contain up to 10 pages total, including references, and must use the IEEE two-column conference format.
Submit Working Notes through CyberChair SC03
The organizers review the papers and decide acceptance. Selected accepted papers may be included in the conference proceedings, subject to conference publication, camera-ready, registration, and presentation requirements. Submission, ranking, or a certificate does not guarantee paper acceptance or publication. If CyberChair displays a different date, follow the organizer schedule above.
Certificates
FinReason Cup does not offer cash prizes. A team receives a participation certificate when it submits both at least one valid final solution under the applicable task rules and deadlines (25 October, 23:59 AoE for Tasks 1 and 3; the published live-trading deadlines for Task 2), and Working Notes through SC03 by 2 November, 23:59 AoE. Winning teams receive winner certificates. Registration support is not confirmed, and registration funding is not promised.
Contact
zhuohan.xie@mbzuai.ac.ae | Terms of Participation
For submission questions, include your team name and task number.
Roadside-LiDAR perception is bottlenecked by the cost of labeled data. Can synthetic LiDAR be made realistic enough to train detectors that work on the real thing?
The challenge Train a 3D object detector on synthetic LiDAR only — no real labels — then run it on held-out real frames. Generate your synthetic data any way you like: physics simulation (CARLA), diffusion / flow models, neural radiance fields, or hybrids. Submit 50 synthetic frames, your detections on 50 real frames, and a signed honor declaration.
Two parallel tracks
- LUMPI (Hannover, Germany): multi-perspective roadside LiDAR, 8 classes including vulnerable road users.
- V2X-Real (Los Angeles, USA): infrastructure-centric V2X LiDAR, 3 classes, very different sensor geometry. Winning both proves cross-distribution robustness.
How you win A combined score of 0.6·detection + 0.4·realism — detection via KITTI 3D mAP, realism via Chamfer Distance, MMD, Earth Mover’s Distance, and Fréchet Point-cloud Distance. The exact scorer ships, so you can reproduce server scores locally.
Platform & prizes Codabench (already provisioned). A USD 2,000 pool, with per-track awards of $500 / $300 / $200; top-3 teams release code and present a challenge report.
Head start A reference synthesis pipeline (UrbanTwin), ready-made synthetic datasets (Harvard Dataverse), and the open-source LiGuard toolkit are all provided.
Organizers Muhammad Shahbaz and Shaurya Agarwal, Urbanity Lab, University of Central Florida.
Emulate four decades of the planet’s carbon cycle — the first open competition for global, multi-decadal land-ecosystem forecasting.
The challenge Given initial ecosystem states and long-term environmental forcings, forecast seven annual carbon variables — vegetation height, aboveground biomass, soil carbon, leaf area index, and gross / net primary productivity and heterotrophic respiration — over multi-decadal horizons. Build fast ML emulators of a physics-based ecosystem model.
The data CarbonGlobe (NeurIPS 2025) — 40 years of global data at 0.5° resolution, 136 input variables, and calibrated Ecosystem Demography (ED) model outputs across 54,152 land sites and 15 forest-age conditions: 812,280 forecasting sequences. ED underpins NASA’s Carbon Monitoring System and the Global Carbon Budget.
How you win RMSE and MAE plus problem-driven metrics — cumulative error and year-to-year delta error — on a hidden test set of future ecosystem states. Source code required for reproducibility.
Platform Kaggle, with baselines and starter notebooks on GitHub.
Organizers Yiqun Xie, Zhihao Wang, Lei Ma, and George Hurtt (University of Maryland), Xiaowei Jia (Rutgers), and Yanhua Li (Worcester Polytechnic Institute).
06 OpenCity and TrafficFlowBench
Two separately managed transportation tracks, with distinct tasks, submission platforms, organizing teams, and awards. Results will be presented at IEEE Big Data 2026 in Phoenix, December 14–17, 2026.
Track 1 OpenCity Challenge
The challenge: Build a reproducible city transportation model connecting the network, origin–destination demand, route flows, and link performance. Teams document their data sources and modeling assumptions and provide evidence that their outputs are internally consistent and supported by observations.
Data and evaluation: The OpenCity specification defines the required city-model files, supporting evidence, validation checks, and scoring rules. See the track page for the current data-access instructions, submission requirements, deadlines, and awards.
Platform: Codabench — OpenCity competition page
Organizers and sponsor: Shenzhen Urban Transport Planning Center (SUTPC). Xiaochun Zhang leads the track, with Yong Zhou coordinating operations. Evaluation committee chair: Prof. Pan Shang, Beijing Jiaotong University (BJTU) — shangpan@bjtu.edu.cn.
Planned prize pool: $3,500, subject to confirmation by the Shenzhen/SUTPC organizing team. The award breakdown will be published on the OpenCity competition page.
Contact: Yong Zhou — zhouyong@sutpc.com.
Track 2 TrafficFlowBench
The challenge: Four connected tasks on a shared freeway network:
- Traffic-state reconstruction — recover missing speed and flow measurements.
- Queue prediction — forecast queued links over the next 30 minutes.
- Physics consistency — check the fundamental diagram and vehicle conservation.
- OD and path-flow estimation — recover demand from link counts.
The data: Synthetic freeway records at five-minute resolution across ten directional corridors, generated by a traffic-flow model calibrated on real detector data. The release includes network topology and path-incidence information; it does not redistribute the underlying real detector records.
How you win: Submissions are evaluated across the four tasks using the scoring rules published on the Kaggle competition page. Final rankings and awards are subject to the published eligibility and reproducibility requirements. See Kaggle for the current metrics and scoring details.
Platform: Kaggle — Competition, data, rules, and submissions
Code and baselines: github.com/jacky850/trafficflowbench-public
Sponsor and prizes: INRIX. One total prize pool of $3,500: Gold $1,500; Silver $1,000; Bronze $500; K–12 Student Award $500 (for K–12 students, including high school students).
Final submission: November 6, 2026. Follow the Kaggle page for the exact cutoff and current submission rules.
Organizers: ASU team, with the IEEE ITS Society Technical Committee on Travel Information and Traffic Management and RERITE. Co-chaired by Xuesong (Simon) Zhou (Arizona State University), Cathy Wu (Massachusetts Institute of Technology), Ruolin Li (University of Southern California), and Yudai Honma (The University of Tokyo). Technical contact: Jinxi Wu, Arizona State University — jinxiwu@asu.edu.
