WORLD'S BEST TEAMS
IDAO 2019 is over, we will keep you updated on IDAO 2020!
WHAT IS IDAO?
Higher School of Economics and Yandex are proud to announce an olympiad created by and for data analysts.
Bringing the world of Data Competitions to the grand stage for people in all walks of life, be they PhD holders, company teams, students or new data scientists, the event is open to all teams and individuals alike.
The event aims to bridge the gap between the all-increasing complexity of Machine Learning models and performance bottlenecks of the industry. The participants will strive not only to maximize the quality of their predictions, but also to devise resource-efficient algorithms.
This will be a team machine learning competition, divided into two stages. The first stage will be online, open to all participants. The second stage will be the offline on-site finals, in which the top 30 performing teams from the online round will compete at the Yandex office in Moscow.
In 2018 the Online Round gathered 1500 participants from all over the world. It was conducted on January 15–December 11, 2018. 100 best participants took part in the On-Site Final that was held in Moscow on April 2–3.
STAGE 1. ONLINE
There will be two separate tracks during the online stage. From the machine learning perspective, the tracks will be similar, yet the restrictions put on the solutions are different for each track.
The first track will be a traditional data science competition. Having a labeled training data set, participants will be 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, your task will be 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. You will need to upload the end-to-end code for your solution: both learning and inference. The evaluation server will run training and testing for your model and report the result. Both learning and evaluation must fit into time and memory constraints. If you like the most efficient solutions, this is the right track for you.
We hope that the two tracks will make 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 encourage 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."
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 will be 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 go to the final.
Please note, that only submissions to the private tasks will be considered.
Thus, in order to qualify for the final a team may choose one of the two strategies:
- to obtain the highest score in the second track where the code is needed, or
- to obtain the highest score in the first track.
Each of 30 teams, which are selected as finalists, will receive a letter describing further steps.
First of all, we will 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 will check that your solution contains no cheating, and your team does not attempt to unfairly pass the rules.
The finalists table 2019 will be published in February only after the jury’s decision.
The second, onsite tour will be held in Moscow in April 2019 at the central headquarters of Yandex. Over the 36 hours of competition, participants will try 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 are also planned.
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
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.
|Team Name||Team Captain -Name||Team Captain - University or Company||Member I - Name||Member I - University or Company||Member II - Name||Member II - University or Company|
|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" will participate in the final hors concours.
Winners of the first stage will be 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.
Registration for IDAO 2019 is closed now.
The final count is 1287 teams from 78 countries
Chairman of the Judiciary Commission,
Research Professor in HSE,
Head of the Deep Learning
and Bayesian Methods Centre
Data Analyst in Yandex, highest
overall rank in Kaggle is 5th
Head of AI at Yandex.Zen
Deputy Head of the Big Data
and Information Retrieval School
PhD student in Skoltech University,
Research Scientist at Bayesian Methods Centre
Head of Methods for Big Data
Analysis Lab at HSE
Data Analyst at Yandex.Taxi
Researcher at the Laboratori Nazionali di Frascati of INFN
Team leader of Frascati LHCb group and Deputy Operation Coordinator of the experiment
Researcher at the Laboratori Nazionali di Frascati of INFN
Member of the LHCb experiment at CERN
"Data Science is becoming one of the most important domains of human knowledge. Our civilization is at the point when we are able collect and store large amounts of data but still do not fully realize what we can do with them. There is a clear need in professionals in data science and machine learning and this need will only grow in next years. Such olympiads are excellent ways to encourage more gifted young people to become data scientists. The more data scientists we will have, the faster we will move towards AI and the creation of new post-industrial future for humanity."
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.
Leader of the Banking Industry
Sberbank today is the circulatory system of the Russian economy, accounting for one third of its banking system. The Bank provides employment and a source of income for every 150th Russian family.
Sberbank has 28,7% share of the aggregate Russian banking sector assets (as of January 1st, 2016).
The bank is the biggest holder of retail deposits and provider of loans in Russia. It holds around 46% of the country’s retail deposits, and provides 38,7% of consumer loans and 32,2% of corporate loans.
Sberbank today is 12 territorial banks and over 16 thousand branches throughout the country in all 83 constituent entities of the Russian Federation located across 11 time zones.
Sberbank has over 100 million clients in Russia, which makes more than half of entire population, and around 11 million clients abroad.
LHCb is an experiment set up to explore what happened after the Big Bang that allowed matter to survive and build the Universe we inhabit today.
Fourteen billion years ago, the Universe began with a bang. Crammed within an infinitely small space, energy coalesced to form equal quantities of matter and antimatter. But as the Universe cooled and expanded, its composition changed. Just one second after the Big Bang, antimatter had all but disappeared, leaving matter to form everything that we see around us — from the stars and galaxies, to the Earth and all life that it supports.
Located in a vast underground cavern, 100 metres beneath the French countryside, LHCb is one of four large experiments based at the CERN laboratory near Geneva, Switzerland.
From Brits to Brazilians, Americans and Poles, the LHCb is a truly international collaboration. About 1250 scientists representing 79 different universities and laboratories from 18 countries are involved in the project, with support from about 400 technicians and engineers (September 2018).
BECOME A PARTNER
So why should you support this event? As a partner of IDAO, your organization will have an opportunity to raise awareness about your brand in Data Science community around the world.
Through such event, you will have a chance to contribute to development of Data Science and your positions as a potential employee in the sphere. With more than 2,500 participants, the Olympiad allows you to have access to potential employees, select best participants and maximize connection with them.
There are many ways to support the Olympiad, we would be most delighted to see you as a partner. If you have any questions on potential partnership, please contact Ms. Irina Plisetskaya, IDAO Partnership Coordinator, email@example.com.
SUPPORT THE EVENT
Anyone can contribute to the development of IDAO. We would be truly grateful for your support, because with your participation we can do more! If you want to support the Olympiad through a contribution, please click "contribute" button below.
BECOME A VOLUNTEER
To become a volunteer at IDAO, please contact firstname.lastname@example.org .
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