IDAO has been held since 2018. This page tells a story of the olympiad.
2021 First Round
This year the online task was coming again from Physics. The task was given by the Laboratory of Methods for Big Data Analysis (LAMBDA, HSE University) together with CYGNO Collaboration. We would like to extend our general thanks to the CYGNO Collaboration and in particular to André Cortez, Flaminia Di Giambattista, Giulia D'Imperio, and Fabrizio Petrucci who helped in preparing the challenge samples.
In the final round, participants had 36 hours to solve a task from Otkritie Bank: once a month the bank selects its most loyal customers and generates personalised consumer loan offers for them. Call centre managers then phone the clients with the offers. The bank profits if the loan is taken out. The bank spends different amounts of resources on communicating with different clients. The task for the IDAO participants was to create a list of clients, the interaction with whom would bring the maximum profit. Special thanks to Alexander Guschin, Senior Data Scientist at Mechanica AI, for his help in preparing the final task.
The winners of IDAO 2021 are:
🥇random team: Ilya Kornakov, Kirill Borozdin
🥈Mylene Farmer: Vasiliy Rubtsov, Anvar Kurmukov
🥉Shizika: Dmitry Simakov, Nikita Churkin
Task creator, Senior business leader, Next Best Action team,
Customer Value Management and loyalty tribe,
Task creator, Data Scientist, Next Best Action team,
Customer Value Management and loyalty tribe,
2020 First Round
This year the online task is coming from astronomy. It is focused on building a model that would predict the position of space objects using simulation data. The task was given by Russian Astronomical Science Center (ASC) and adopted for the Olympiad by the Laboratory of Methods for Big Data Analysis (LAMBDA, HSE University).Predicting the position of satellites is one of the most important tasks in astronomy. For example, information on the exact position of satellites in orbit is necessary to avoid extremely dangerous satellite collisions. Each collision leads not only to satellites destruction, but also results in thousands of space debris pieces. For instance, Iridium-Coscos collision in 2009 increased number of space debris by approximately 13%. Further collisions may result in Kessler syndrome and the inaccessibility of outer space. Also, a more accurate prediction of satellite position will help calculate more eﬃcient maneuvers to save propellant and extend satellite life in orbit.
Task creator, Research Assistant at the Laboratory of Methods for Big Data Analysis (LAMBDA), HSE University
2020 First Stage results
|Place||Track 1 Best Teams||Track 1 Score||Country|
|2||Mrs. MIPT||96,57||France/ Switzerland|
|3||Veni Vedi Vici||96,06||Russia/ Belarus|
|5||Extra Mile Stat||95,89||Peru|
|7-8||Baby Data O Plomo||95,26||France|
|10||Gradient Ascent||94,82||Russia/ USA|
|11||The Land of Crimson Clouds||94,46||Russia|
|14||CHAD DATA SCIENTISTS||93,72||Russia|
|Place||Track 2 Best Teams||Track 2 Score||Country|
|1||Data O Plomo||96,61||France|
|2||New Era los Guys||96,26||Russia|
|13||Hotteam v final||93,95||Russia|
|15-16||Good Luck||93,93||Russia/ Kazakhstan|
The winners of IDAO 2020 are:
🥇First place: Vrn (Ivan Bragin, Igor Kleynikov)
🥈Second place: Mylene Farmer (Ilya Ivanitskiy, Vasiliy Rubtsov, Anvar Kurmukov)
🥉Third place: random team (Ilya Kornakov, Kirill Borozdin)
The task for the finals was provided by the platinum partners QIWI. QIWI is a leading provider of next generation payment and financial services in Russia and the CIS. It has an integrated proprietary network that enables payment services across online, mobile and physical channels. It has deployed over 21.8 million virtual wallets, over 136,000 kiosks and terminals, and enabled merchants and customers to accept and transfer over RUB 116 billion cash and electronic payments monthly connecting over 44 million consumers using its network at least once a month. QIWI’s consumers can use cash, stored value and other electronic payment methods in order to pay for goods and services or transfer money across virtual or physical environments interchangeably.
Final Stage results
|3||random team||90.98||Russia/ Switzerland|
|5||Hotteam v final||90.95||Russia|
|6||CHAD DATA SCIENTISTS||90.82||Russia|
|9||Baby Data O Plomo||90.75||France|
|10-11||Veni Vedi Vici||90.69||Belarus/ Russian|
|13||The Land of Crimson Clouds||90.47||Russia|
|16||Gradient Ascent||90.38||Russian/ USA|
|20||New Era los Guys||89.94||Russia|
|23||Extra Mile Stat||89.74||Peru|
|26||Baby Data O Plomo||61.18||USA|
|29-31||Mrs. MIPT||55.95||France/ Switzerland|
2019 First Round
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.”
PhD student at HSE and the University of Rome, researcher at the Laboratory of Methods for Big Data Analysis
The task for 2019 Finals was provided by Yandex.Taxi. Emil Kayumov, task creator: 'This is one of our real tasks — to be able to predict the waiting time for the next order for a taxi driver at the airport, so that drivers can better understand how long they have to wait for a client. The specificity is that we place orders in the order of taxi drivers' arrival at the airport, because, unlike the city, passengers have only one point where they can call a taxi. Drivers know how many cars are in front of them, but it is more convenient to know how much time to wait for their order. In some cases, you can be closer to the terminal or take a break and have a cup of coffee, and sometimes wait for an order for so long that it is better to return to the city center. In general, IDAO participants solved this problem approximately the same way we did, but you could see some good ideas that we hadn't thought of. For example, someone in his decision took into account the influence of different days of the week and holidays on the number of drivers and passengers. This is not exactly a business task, it will not help the company to make more money, but it will make life more comfortable for drivers.'
|Team||Captain||First Member||Second Member|
|AR_U_KIDDIN_MI||Eugene Bobrov||Moscow State University||Vladimir Bugaevskii||Moscow State University||Denis Bibik||Moscow State University|
|BarelyBears||Hiroshi Yoshihara||The University of Tokyo||Kosaku Ono||The University of Tokyo||Naoki Maeda||The University of Tokyo|
|Columbarium||Konstantin Frolov||SKB Kontur||Grigoriy Pogorelov||MTS||Nikolay Prokoptsev||Tinkoff Bank|
|DataBroom||Pawan Kumar Singh||Myntra Design Pvt. Ltd.||Shruti Singh|
|DataScienceBois||Egor Kravchenko||Lomonosov Moscow State University||Vladislav Trifonov||Lomonosov Moscow State University||Artyom Mironov||Lomonosov Moscow State University|
|Eureka||Sandeep Singh Adhikari||Myntra||Yadunath Gupta||Myntra||Nilpa Jha||Myntra|
|FeelsBadMan||Iskander Safiulin||OKKO||Ksenia Balabaeva||ITMO||Dmitry Ivanov||Higher School of Economics|
|Gradient Boosting||Pavel Shevchuk||NRU HSE (applied mathematics)||Mikhail Diskin||NRU HSE||Dmitriy Nikulin||Samsung AI Center Moscow|
|HAL 9000 followers||Aleksandr Belov||National Research University of Electronic Technology, Applied mathematics||Andrey Gorodetsky||Bauman Moscow State Technical||Maxim Tsygankov||Bauman Moscow State Technical University|
|HardNet||Yaroslav Murzaev||MIPT||Andrey Kachetov||MIPT||Viktor Nochevkin||MIPT|
|holistic agency||Maxim Shaposhnikov||Ural Federal University||Elena Arslanova||Ural Federal University||Denis Razbitsky||Ural Federal University|
|Hunky-dory||Ihar Shulhan||Innopolis University||Almira Murtazina||Innopolis University||Ruslan Mustafin||Machine learning engineer|
|ifelse||Samir Mammadov||E-gov Development Center||Asgar Mammadli||E-gov Development Center||Umid Suleymanov||E-gov Development Center|
|ImprovY||Stanislav Sopov||SEMrush||Mikhail Alekseev||Okko||Rinat Shakbasarov||GrowFood|
|itchy mcfly||Petr Kuderov||N/A||Alex Maslov|
|John Keats||Kirill Trofimov||self-employed||Sabina Abdullaeva|
|kek ||Ranis Nigmatullin||yandex|
|Magic CIty||Sergei Arefev||Saint Petersburg State University||Artem Plotkin||Saint Petersburg State University||Roman Pyankov||Saint Petersburg State University|
|Mylen Farmer||Ilya Ivanitskiy||Avito|
|Polis||Yuriy Gavrilin||Innopolis University||Vladislav Kurenkov||Innopolis University||Andrey Kulagin||Innopolis University|
|shadd||Daniil Barysevich||BSUIR, Computer Science||Dzmitry Vabishchewich||BSUIR||Aliaksei Barysevich||BSUIR, Computer Science|
|Singularis Lab||Aleksei Alekseev||Singularis Lab||Oleg Shapovalov||Singularis Lab||Andrey Pedchenko||Mello|
|TEAM X||Andrey Kutsenko||Moscow State University||Nazar Beknazarov||Higher School of Economics||Sergey Kolomiyets||Tyumen State University|
|Team_Name||Daniil Cherniavskii||MIPT||Alexandr Valukov||MIPT|
|trtr||Denis Litvinov||Sberbank||Aleksey Buzovkin||Michail Voronov|
|Umka||Dmitrii Fedotov||PJSC Norilsk Nickel|
|Unnamed:0||Arthur Bogdanov||Innopolis University||Gcinizwe Dlamini||Innopolis University||Rufina Galieva||Innopolis University|
|wearenotgonnapasstothefinalanyways||Toghrul Rahimli||ADA University||Jalal Rasulzade||ADA University||Orkhan Bayramli||ADA University|
|Zvezdochka*||Ernest Glukhov||Innopolis University||Daria Zapekina||Innopolis University||Vyacheslav Karpov||Innopolis University|
 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|
|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|
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’.
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).