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UseData Conf

UseData 2019 logo

Sep 16, 2019

Moscow, Infospace

A conference for machine learning and data analysis professionalsThis is the conference for those who apply data analysis and machine learning in their everyday work. There often is a chasm between the algorithm from a textbook and its application on real-world data. We want those able to cross that chasm to meet and share their knowledge on our conference.

Conference features

  • 300+ Application, API and
    fullstack developers, CTO’s
    and CEO’s

  • Best networking

  • 20+ speakers -
    leading industry experts


Conference will be held in the center of Moscow in Event-hall "Infospace".
It’s 2 kilometers away from the Kremlin and 2,2 kilometers from Gorky Central Park.

For speakers

We do not accept sales pitch style presentations. We are looking for passionate people who understand developers and whose speeches are energetic and based on the experience. The selection of talks is multi-layered and complex with the sole purpose of selecting the best.

  • Possible topics:

    • machine learning and data analysis in sales and marketing;
    • computer vision;
    • natural language processing;
    • ML-powered decision making;
    • machine learning contests;
    • testing, monitoring, result interpretation and data preparation.

    If your topic doesn't match, submit it anyway. We'll see what we can do.

  • Speaker package includes:

    • Accommodation during the conference at the venue hotel
    • Economy travel expense coverage
    • Full visa support
    • Speaker dinner
    • Tour of Moscow (family and friends are welcome)
    • Transfer from the airport and back
    • Support from our team at all stages of your participation – from talk acceptance until you get home after the event


Are you ready to apply?

All applications must be submitted before July 31Those approved by the Program Committee will be announced on August 16


Our talks

  • Guido Montufar

    Guido Montufar University of California; Max Planck Institute

    Wasserstein Regularization for Generative and Discriminative Learning

  • Ilya Sergeev

    Ilya Sergeev Avito

    How to find and cover the licence plate on a car photo and how to protect your content from copying using adversarial attack

  • Saloni Garg

    Saloni Garg

    Machine Learning Based Autonomous Car Driving Algorithms

  • Vladimir Bougay

    Vladimir Bougay Knoema

    Implementation of fast and efficient search using clickstream, transformers and approximate nearest neighbour search

  • Ed Tyantov

    Ed Tyantov Group

    Project Management 2.0: AI Transformation

  • Alexandra Lomakina

    Alexandra Lomakina Joom

    The efficiency of TV commercials: evaluation and optimization

  • Alexander Alekseytsev

    Alexander Alekseytsev OZON.RU

    ML-powered sales prediction at OZON.RU e-store. Optimizing prices using demand prediction models

  • Grigory Sapunov

    Grigory Sapunov Intento

    Modern Neural Net Architectures / Year 2019 version

  • Irina Pchelintseva

    Irina Pchelintseva Yandex

    The good and the bad in recommendation systems metrics

  • Alexander Patrushev

    Alexander Patrushev AWS

    AWS DeepRacer: studying difficult stuff while playing

  • Andrey Ivanov

    Andrey Ivanov Tinkoff

    Development and deployment of intelligent agents

  • Ivan Isaev

    Ivan Isaev Altarix

    Drilling incidents prediction

  • Artem Seleznev

    Artem Seleznev MegaFon

    Adding data control to ML pipeline

  • Alexander Smorkalov

    Alexander Smorkalov Xperience.AI

    512 Kb of RAM will be enough for everybody

  • Georgiy Shushuev

    Alexander Smorkalov CFT

    Incorrect names detection

  • Egor Bulychev

    Egor Bulychev source{d}

    Machine learning for code

  • Darima Mylzenova

    Darima Mylzenova Gazprom Neft

    Neural networks helping to understand what happens underground and to find oil

  • Vladislav Blinov

    Vladislav Blinov Tinkoff

    Can we teach a machine the sense of humor?

Program committee

Program committee consists of representatives of large companies, frameworks and communities from Russian Internet and around the world.

  • Roman Poborchiy

    Specialist in metrics assessing the performance of algorithms and AB-testing systems.

    Roman PoborchiyIndependent expert

  • Nikita Povarov

    Specialist in data analysis, statistics and machine learning. In the past, he was engaged in user behavior metrics and the system of online experiments in Yandex. Now he is working on the creation and implementation of machine learning models in JetBrains products. In addition, leads a course on ML in Computer Science Center.

    Nikita PovarovJetBrains

  • Petr Ermakov

    Senior Data Scientist in LAMODA. He loves word processing and engineering in machine learning. Helps do PyData Moscow Meetup and DataFest.

    Petr ErmakovLamoda

  • Ivan Yamshchikov

    Received a PhD in Applied Mathematics from Brandenburg University of Technology. He worked as an analyst at Yandex for six years. Researcher at the Max Planck Institute in Leipzig, evangelist of ABBYY and co-founder of the project Creaited Labs.

    Ivan YamshchikovABBYY / Max Planck Institute

Code of Conduct

We adhere to the standard rules outlined in the Code of Conduct.

Our conference is dedicated to providing a harassment-free conference experience for everyone, regardless of gender, gender identity and expression, age, sexual orientation, disability, physical appearance, body size, race, ethnicity, religion (or lack thereof), or technology choices. We do not tolerate harassment of conference participants in any form. Sexual language and imagery is not appropriate for any conference venue, including talks, workshops, parties, Twitter and other online media. Conference participants violating these rules may be sanctioned or expelled from the conference without a refund at the discretion of the conference organizers.


If you have any questions, suggestions or comments, please do not hesitate to contact head of the program committee Roman Poborchy