Abdalaziz Rashid Al-Maeeni
Task creator, Research Assistant at the Laboratory of Methods
for Big Data Analysis (LAMBDA), HSE University
Giulia D'Imperio
Task creator, Researcher at INFN Sezione di Roma
2021 Finals
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
Roman Monakhov
Task creator, Senior business leader, Next Best Action team,
Customer Value Management and loyalty tribe,
OTKRITIE Bank
Chady Tofaily
Task creator, Data Scientist, Next Best Action team,
Customer Value Management and loyalty tribe,
OTKRITIE Bank
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.
Leonid Gremyachikh
Task creator, Research Assistant at the Laboratory of Methods for Big Data Analysis (LAMBDA), HSE University
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.”
Nikita Kazeev
PhD student at HSE and the University of Rome, researcher at the Laboratory of Methods for Big Data Analysis
Competition
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).