IDAO History

IDAO has been held since 2018. This page tells a story of the olympiad.

IDAO 2019: How it was?

IDAO 2019: How it was?

The final count is 1287 teams from 78 countries


Stage 1. On-line competition

There were two separate tracks during the online stage. From the machine learning perspective, the tracks were similar, yet the restrictions put on the solutions are different for each track.

The first track was a traditional data science competition. Having a labeled training data set, participants were asked to make a prediction for the test data and submit their predictions to the leaderboard. In this track, participants can produce arbitrarily complex models. If you like to use 4-level stacking or deep neural networks, this is the right track for you – you will only need to submit test predictions. However, those who qualify for the finals will be asked to submit the full code of the solution for validation by the judges.

In real world problems, efficiency is as important as quality. Complex and resource-intensive solutions will not fit the strict time and space restrictions often imposed by an application. That is why in the second competition track, the task was to solve the same problem as was in track one, but with tight restrictions on the time and on the memory used during both learning and inference. The participants had to upload the end-to-end code for your solution: both learning and inference. The evaluation server ran training and testing for the model and report the result. Both learning and evaluation must fit into time and memory constraints.

We hope that the two tracks made the olympiad fascinating for both machine learning competition experts and competitive programming masters, Kaggle winners and ACM champions, as well as everyone eager to solve real world problems with Data. Moreover, we encouraged people with different backgrounds, ML and ACM, to team up and push Data Analysis to new frontiers.

The muon research group of the LHCb experiment (LHCb Muon Group) provided the task for participants of the online qualifying round. Nikita Kazeev, co-author of the task, about the task:

“The task we gave the participants, muon identification, is important for the LHCb experiment. Majority of the physics research done at LHCb uses the output of this algorithm. I am looking forward to data science practitioners trying a hand on the problem. At the LHCb collaboration, we hope that the ideas and techniques they develop will ultimately bring us a step closer to understanding the big mysteries of the Universe. The task is also tricky from the machine learning point of view, for it contains features of variable length and negative example weights.”

Nikita Kazeev
PhD student at HSE and the University of Rome, researcher at the Laboratory of Methods for Big Data Analysis

Stage 2. Finals

The following two-step procedure has been used to select finalists.
Firstly, 15 teams with the highest score in the second track go to final (no matter what is their score in the first track).
Secondly, we consider all remaining teams and select 15 teams with the highest score in the first track (no matter what is their score in the second track).
These teams also went to the final.

Only submissions to the private tasks were considered.

Thus, in order to qualify for the final a team could choose one of the two strategies:

  1. to obtain the highest score in the second track where the code is needed, or
  2. to obtain the highest score in the first track.

Each of 30 teams, which were selected as finalists, received a letter describing further steps.
First of all, we ask the source code of your solution (for both tracks) which will be reviewed and validated. The solution must reproduce your submission exactly. Our jury members check that your solution contains no cheating, and your team does not attempt to unfairly pass the rules.
The finalists table 2019 was published in February after the jury’s decision.

The second, onsite stage was held in Moscow in April 2019 at the central headquarters of Yandex. Over the 36 hours of competition, participants tried not only to get up to speed on the model, but to create a full-fledged prototype that will be tested both in terms of accuracy and performance.

As part of the onsite round of the olympiad, speeches and workshops by international experts in machine learning and data analysis were also held.


To take part in the Olympiad, each team participant must register. Each team consists of 1-3 members.

The Olympiad is held in two rounds: online qualification round hosted on the Yandex.Contest Platform, and the on-site finals, held in Moscow. The solution of the task of the online round must be submitted by the team to the contest system no later than 23.59 Moscow time on February 11, 2019.

Based on the results of the online round, a table with points scored by teams will be published on the IDAO site by February 18, 2019, highlighting the list of finalists.

Each team can submit only one solution.

Only participants who have reached the age of 18 before the start of the on-site finals can participate.

At the finals, participants will need to use their own computer. Use of any legal software is allowed.

Three prizes will be awarded in the final round: one for the winning team, and two runners up.

Employees of Yandex and members of the LHCb collaboration can only participate hors concours, since Yandex and LHCb provide tasks for IDAO 2019.

Winners of the first stage (finalists) were invited to Moscow to take part in the on-site competition.

All participants have the chance to showcase their skills to the data science community on an international scale – the results will be internships, networking with some of the most passionate and like-minded individuals, and job opportunities. Winning also will be a serious advantage for students applying to the master’s degree programs at the HSE Faculty of Computer Science.

For winners, valuable prizes will be awarded. All members of the winning team will receive laptops as prizes. The winners will be determined by the leaderboard ranking based on private test set.

Finalists 2019

TeamCaptainFirst Member Second Member
AR_U_KIDDIN_MIEugene BobrovMoscow State UniversityVladimir BugaevskiiMoscow State UniversityDenis BibikMoscow State University
BarelyBearsHiroshi YoshiharaThe University of TokyoKosaku OnoThe University of TokyoNaoki MaedaThe University of Tokyo
ColumbariumKonstantin FrolovSKB KonturGrigoriy PogorelovMTSNikolay ProkoptsevTinkoff Bank
DataBroomPawan Kumar SinghMyntra Design Pvt. Ltd.Shruti Singh
DataScienceBoisEgor KravchenkoLomonosov Moscow State UniversityVladislav TrifonovLomonosov Moscow State UniversityArtyom MironovLomonosov Moscow State University
EurekaSandeep Singh AdhikariMyntraYadunath GuptaMyntraNilpa JhaMyntra
FeelsBadManIskander SafiulinOKKOKsenia BalabaevaITMODmitry IvanovHigher School of Economics
Gradient BoostingPavel ShevchukNRU HSE (applied mathematics)Mikhail DiskinNRU HSEDmitriy NikulinSamsung AI Center Moscow
HAL 9000 followersAleksandr BelovNational Research University of Electronic Technology, Applied mathematicsAndrey GorodetskyBauman Moscow State TechnicalMaxim TsygankovBauman Moscow State Technical University
HardNetYaroslav MurzaevMIPTAndrey KachetovMIPTViktor NochevkinMIPT
holistic agencyMaxim ShaposhnikovUral Federal UniversityElena ArslanovaUral Federal UniversityDenis RazbitskyUral Federal University
Hunky-doryIhar ShulhanInnopolis UniversityAlmira MurtazinaInnopolis UniversityRuslan MustafinMachine learning engineer
ifelseSamir MammadovE-gov Development CenterAsgar MammadliE-gov Development CenterUmid SuleymanovE-gov Development Center
ImprovYStanislav SopovSEMrushMikhail AlekseevOkkoRinat ShakbasarovGrowFood
InspirationAlexander KolomoetsUAC
itchy mcflyPetr KuderovN/AAlex Maslov
John KeatsKirill Trofimovself-employedSabina Abdullaeva
kek [1]Ranis Nigmatullinyandex
LivingtonIvan GlebovMIPT
Magic CItySergei ArefevSaint Petersburg State UniversityArtem PlotkinSaint Petersburg State UniversityRoman PyankovSaint Petersburg State University
Mylen FarmerIlya IvanitskiyAvito
PolisYuriy GavrilinInnopolis UniversityVladislav KurenkovInnopolis UniversityAndrey KulaginInnopolis University
shaddDaniil BarysevichBSUIR, Computer ScienceDzmitry VabishchewichBSUIRAliaksei BarysevichBSUIR, Computer Science
Singularis LabAleksei AlekseevSingularis LabOleg ShapovalovSingularis LabAndrey PedchenkoMello
TEAM XAndrey KutsenkoMoscow State UniversityNazar BeknazarovHigher School of EconomicsSergey KolomiyetsTyumen State University
Team_NameDaniil CherniavskiiMIPTAlexandr ValukovMIPT
trtrDenis LitvinovSberbankAleksey BuzovkinMichail Voronov
UmkaDmitrii FedotovPJSC Norilsk Nickel
Unnamed:0Arthur BogdanovInnopolis UniversityGcinizwe DlaminiInnopolis UniversityRufina GalievaInnopolis University
wearenotgonnapasstothefinalanywaysToghrul RahimliADA UniversityJalal RasulzadeADA UniversityOrkhan BayramliADA University
Zvezdochka*Ernest GlukhovInnopolis UniversityDaria ZapekinaInnopolis UniversityVyacheslav KarpovInnopolis University
[1] The team “kek” participated in the final hors concours.


1st place – Mylen Farmer
Ilya Ivanitskiy – Higher School of Economics/Avito
2nd place – Zvezdochka*
Ernest Glukhov – Innopolis University,
Daria Zapekina – Innopolis University,
Vyacheslav Karpov – Innopolis University
3rd place – TEAM X
Andrey Kutsenko – Moscow State University,
Nazar Beknazarov – Higher School of Economics,
Sergey Kolomiyets – Tyumen State University
IDAO 2019 Finals Score in Yandex.Contest Ranks for Tasks Place by Rank
Team Name taskA taskB taskC Total Score Place by Score rankA rankB rankC Total Rank
Mylen Farmer 75.21 63.46 62.3 200.97 1 3.0 2.0 1.0 6.0 1
Zvezdochka* 75.38 62.91 61.8 200.09 2 2.0 6.0 5.0 13.0 2
TEAM X 74.91 62.55 62.09 199.55 4 5.0 8.0 2.0 15.0 3
shadd 74.66 62.85 62.06 199.57 3 9.0 7.0 3.0 19.0 4-5
BarelyBears 74.89 62.95 61.54 199.38 6 6.0 5.0 8.0 19.0 4-5
Eureka 73.93 63.45 62.04 199.42 5 15.0 3.0 4.0 22.0 6
Magic CIty 75.59 62.23 61.53 199.35 7 1.0 13.0 9.0 23.0 7
AR_U_KIDDIN_MI 73.96 63.27 61.77 199.0 8 14.0 4.0 6.5 24.5 8
Unnamed:0 73.8 63.65 61.46 198.91 9 18.0 1.0 11.0 30.0 9
FeelsBadMan 74.05 62.16 61.5 197.71 11 13.0 14.0 10.0 37.0 10
Team_Name 74.35 62.08 61.41 197.84 10 11.0 15.0 12.0 38.0 11
kek (hors concours) 74.1 62.39 61.04 197.53 12 12.0 10.0 16.0 38.0 12
HAL 9000 followers 74.7 62.32 60.26 197.28 13 7.0 11.5 27.0 45.5 13
Umka 73.83 61.93 61.28 197.04 15 17.0 16.0 13.0 46.0 14
itchy mcfly 71.95 61.88 61.77 195.6 20 23.0 18.0 6.5 47.5 15
ifelse 72.15 62.5 60.91 195.56 21 22.0 9.0 17.0 48.0 16
Gradient Boosting 74.98 61.6 60.55 197.13 14 4.0 25.0 20.0 49.0 17
Columbarium 74.64 61.87 60.52 197.03 16 10.0 19.0 22.5 51.5 18
Hunky-dory 70.76 62.32 61.16 194.24 24 25.0 11.5 15.0 51.5 19
Polis 74.67 61.78 60.45 196.9 17 8.0 20.0 25.0 53.0 20
Inspiration 73.87 61.89 60.52 196.28 18 16.0 17.0 22.5 55.5 21
Singularis Lab 72.24 61.73 61.24 195.21 23 21.0 22.5 14.0 57.5 22
ImprovY 73.54 61.63 60.52 195.69 19 19.0 24.0 22.5 65.5 23
wearenotgonnapass… 73.4 61.4 60.59 195.39 22 20.0 27.0 19.0 66.0 24
HardNet 68.31 61.75 60.85 190.91 27 27.0 21.0 18.0 66.0 25
holistic agency 71.13 61.57 60.52 193.22 25 24.0 26.0 22.5 72.5 26
trtr 68.98 61.73 60.35 191.06 26 26.0 22.5 26.0 74.5 27


The on-site finals, in which the top 30 performing teams from the online round will compete, has been held in Moscow, Yandex office.


  • Dmitry VetrovChairman of the Judiciary Commission, Research Professor in HSE, Head of the Deep Learning and Bayesian Methods Centre
  • Alexander GuschinJudge, Data Analyst at Yandex, highest overall rank in Kaggle is 5th
  • Emil KayumovJudge, Data Analyst at Yandex.Taxi
  • Matteo PalutanJudge, Researcher at the Laboratori Nazionali di Frascati of INFN, Member of the LHCb experiment at CERN
  • Barbara SciasciaJudge, Researcher at the Laboratori Nazionali di Frascati of INFN, Team leader of Frascati LHCb group and Deputy Operation Coordinator of the experiment
  • Evgeny SokolovJudge, Head of AI at Yandex.Zen, Deputy Head of the Big Data and Information Retrieval School
  • Dmitry UlyanovJudge, PhD student in Skoltech University, Research Scientist at Bayesian Methods Centre
  • Andrey UstyuzhaninJudge, Head of Methods for Big Data Analysis Lab at HSE
Dmitry Vetrov, Chairman of the judiciary commission, Research Professor in HSE, Head of the Deep Learning and Bayesian Methods Centre


Yandex is a technology company that builds intelligent products and services powered by machine learning. Our goal is to help consumers and businesses better navigate the online and offline world. Since 1997, we have delivered world-class, locally relevant search and information services. Additionally, we have developed market-leading on-demand transportation services, navigation products, and other mobile applications for millions of consumers across the globe. Yandex, which has 22 offices worldwide, has been listed on the NASDAQ since 2011.

About our education initiatives: Yandex is helping shape the future of education by enhancing the learning process with machine learning technologies and teaching the next generation of data scientists to thrive in a world driven by artificial intelligence. As the leading search provider in Russia and one of Europe’s largest internet companies, we have a responsibility to help educate future generations in data science, artificial intelligence and machine learning. We are proud to help provide the education that will make this goal a reality and help future generations prepare for the jobs of tomorrow through our math and coding competitions, learning platforms, school programs, online courses, and the Yandex School of Data Analysis.

The Higher School of Economics (HSE) is the one of the most renowned Russian universities. The education is focused on economics and social sciences as well as high technologies and natural science. We stand on deep studying approach in fundamental disciplines combined with real experience at the biggest Russian companies to bring our graduates the perfect skills for their future carriers.

The HSE Faculty of Computer Science was created in March 2014 with the goal of becoming one of the world’s top 30 faculties in training developers and researchers in the field of big data storage and processing, system and software engineering and system programming. The Faculty is active in many research areas: machine learning, computer vision, theoretical computer science, algorithms for big data, optimisation, software engineering, and bioinformatics. We publish in leading computer science journals and present our results at major conferences.

Organizing Team

  • Tamara VoznesenskayaOrganizing Committee Chair, First Deputy Dean at the Faculty of Computer Science, HSE University
  • Irina PlisetskayaPartnership Coordinator, Deputy Dean for Development, Finance and Administration at the Faculty of Computer Science, HSE University
  • Sergey KarapetyanIDAO Coordinator, Manager at the Faculty of Computer Science, HSE University
  • Emil KayumovProblem Co-author, Data Analyst at Yandex.Taxi
  • Nikita KazeevProblem Co-author, PhD student at HSE and the University of Rome, researcher at the Laboratory of Methods for Big Data Analysis
  • Vladislav LipyaninWeb-Site Editor, Student at HSE University
  • Denis MashkovtsevSystem Administrator at HSE University
  • Alexey MitsyukTechnical Team Lead, Research Fellow at the Faculty of Computer Science, HSE University
  • Aleksey TolstikovYandex.Contest Expert, Yandex School for Data Analysis


Informational Partners

IDAO 2018: The first tournament

IDAO 2018: The first tournament

In 2018 the Online Round gathered 1500 participants from all over the world. It was conducted on January 15–February 11, 2018. 100 best participants took part in the On-Site Final that was held in Moscow on April 2–3.


Stage 1. On-line competition

The event was organized by the HSE Faculty of Computer Science, Yandex, and Harbour.Space University (Barcelona) with the support of Sberbank. The task for the Online Round was provided by Yandex.Market. Stanislav Fedotov, curator at the Yandex School of Data Analysis, and Associate Professor at the HSE Faculty of Computer Science, on the task for the Online Round: “At the online stage, the contestants solved a task for Yandex.Market. When a user enters this service with a specific purpose, the system chooses a set of options which match their query. For example, when someone looks for a kettle, Yandex.Market offers them a lot of options of kettles with various prices and options. But teaching the system to predict queries would be much more interesting, as this would mean that it would offer not what the individual is looking for at that particular moment, but something they would be likely to want in future. ‘The participants were given a search history of notional users, and they had to predict the categories of items these individuals hadn’t looked at over the last three weeks, but would be likely to search for in a week’s time. They had to choose five users, suggest five categories of goods for each user and ‘guess’ at least one of them

Stage 2. Final

The task for the Final was provided by Sberbank. According to Andrey Chertok, Managing Director for Research and Development at Sberbank, the participants had to solve a real problem on which the Sberbank team worked recently, and which is faced by all banks. The task is very applicable: it is about optimizing the cash supply for Sberbank ATMs, numbering tens of thousands across the country. The problem is that cash delivery isn’t always performed effectively, and as a result, cash lies useless in some ATMs, while others run out of cash too quickly. ‘The bank’s losses due to excessive money just ‘lying around’ in ATMs amounts to billions of roubles annually’, Andrey Chertok emphasized. ‘Our team uses data analysis more and more frequently to solve such problems. For example, the problem with cash delivery optimization and forecasting the amount of money to be cashed from a specific ATM was successfully solved with machine learning methods. We proposed a mini version of what we’ve done at Sberbank to the Olympiad participants.’ The finalists worked with real data of Sberbank ATMs’ locations and loading. During the process, the teams faced the same problems that are faced by bank data analysis teams in real life. This includes whether or not the data should be cleaned, and that the data sometimes has so-called ‘outliers’ which relate to more intensive cash delivery on days when salaries or pensions are paid. ‘In a short period of time, all the participants were quite successful in building usable models and got some hands-on experience in solving a real banking task’, said Andrey Chertok. ‘I believe, at this Olympiad, we managed to bring together competitive spirit and applicability’.

Winners 2018

In first place, and hailing from St. Petersburg, Magic City (Artem Plotkin, Roman Piankov and Sergey Arefev).

Coming in second place, all the way from Ukraine is team SantiagoSeaman (Alexander Makeev).

And finally in third place and making their way from Belarus, Apex (Evgeniy Demidovich, Sergei Petrov and Konstantin Mlynarchyk).


Informational and Tech Partners

Press about IDAO