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Data Science UA Conference 2018 -
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  • Data Science UA Conference 2018

    4th International
    Conference in Kyiv
    KYIV
    MARCH 17
    Attendees
    350+
    Speakers
    20+
    Flows
    3
    Hours of networking
    10

    What is Data Science UA 2018?

    Data Science UA Conference brings together leaders in machine learning,
    analytics, BI, data science, AI for a day-long exploration of how data trans-
    forms the World today and what is going to be tomorrow.

    Data Science UA 2018 is the 4th international
    conference in Kyiv,Ukraine.

    Technical flow

    New methods and technologies of data science, machine learning and artificial intelligence.

    Business flow

    Real cases of use and implementation of technology to improve business efficiency.

    Workshops flow

    Ability to acquire practical skills in using a modern data analysis tool.

    Panel discussion

    Experts will consider data science as a business booster.

    Book now


    Hosts

    klepa
    flag_ua

    Jane Klepa

    Executive director,
    1991 Open Data Incubator

    Chernega
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    Daniel Chernega

    Comandante in
    “Che – Guerrilla Marketing”

    Speakers

    ×

    David Braun

    CEO & co-founder TemplateMonster

    Theme: Big data and adaptive marketing. Friendship or Love?

    About speaker:
    David is a well-known public activist and serial IT entrepreneur. In May 2002 together with his
    mates, he co-founded the TemplateMonster company.
    Working as CEO for TemplateMonster for 13 years he has launched numerous IT projects. Since 2002 he has invested in multiple projects, such as MotoCMS, Photodoto, Site2you, Designfloat.
    “When I quit my job in the advertising agency and turned to Internet business I became a partner in InverseLogic, a custom design studio. We worked hard to make it a successful company, but what made me feel really bad was that we had to turn down loads of potential customers who were seeking something cheaper than a custom design. I wasn’t able to do anything about it until I saw how our designer worked with design templates and within a minute or so the idea of TemplateMonster was born.”

    ×

    Borys Pratsiuk

    Head of R&D, Ciklum

    Theme: Data Science education for Managers

    About speaker:
    Borys graduated with honors from the Chair of Physical and Biomedical Electronics of the KPI in 2007 on the specialty “Physical and Biomedical Electronics”, and in 2012 he defended his dissertation at the Faculty of Electronics in the KPI.
    Boris Pracyuk works in the Ciklum R&D department. And also has his own startup – Fino (financial assistant).
    Boris was a speaker at the third Data Science UA Conference on the business flow and took part in the panel discussion.
    Briefly about the report
    1. We will talk about how a manager can become more familiar with Machine Learning. What is your first step in education chain?
    2. What should you know at the pre-sale stage to speak with your client in the same language? Technical and nontechnical Client.
    3. How to educate your junior part of the team to create a nice balance for project development.

    ×

    Borys Pratsiuk

    Head of R&D, Ciklum

    Theme: Data Science education for Managers

    About speaker:
    Borys graduated with honors from the Chair of Physical and Biomedical Electronics of the KPI in 2007 on the specialty “Physical and Biomedical Electronics”, and in 2012 he defended his dissertation at the Faculty of Electronics in the KPI.
    Boris Pracyuk works in the Ciklum R&D department. And also has his own startup – Fino (financial assistant).
    Boris was a speaker at the third Data Science UA Conference on the business flow and took part in the panel discussion.

    ×

    Oleg Boguslavskyi

    General Manager, Ring Ukraine

    Theme: Correct problem statement in ML tasks as a key to success

    About speaker:
    Graduated from Faculty of Applied Mathematics and Faculty of Management (Second Higher Education) at NTUU “KPI” in 2003-2004. Worked in embedded software development for SoC and MPSoC for more than 16 years for the companies Motorola/Freescale, Mindspeed, AMD, LGE, NVidia, Renesas. Holds leading management positions for 12+ years. Has broad experience in running remote teams in USA, China, France, Russia, etc. Currently runs the organization of ±150 highly-qualified specialists. Has 9 scientific publications and technical expertise in:
    -VoIP;
    -Video codecs and real-time video streaming
    -GPU compute (including neural networks optimization for embedded GPU)
    -Building fault-tolerant real-time systems
    -Augmented navigation in Automotive industry.
    Briefly about the report
    We’ll talk about correct task formulation while solving the problem by applying ML methods, and how it can affect results quality. We’ll look into examples to understand how data physical properties consideration and correct KPIs selection can solve certain engineering problems.

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    Alex Nesterenko

    CEO & Founder, ARTKB

    Theme: How to build your product: Hardware mass production stage

    About speaker:
    Founder of ARTKB – full-cycle hardware product development company. Offices are located in Kyiv, Ukraine and Shenzhen, China. At the moment, ARTKB is the only company of this kind in Ukraine.
    Its projects went far beyond the scope of the country. Its team has become experts in hardware product development and mass production launch and it freely shares this experience with customers.
    – From 2014 till now, co-founder of Carrot – first Ukrainian acceleration program for startups, that
    are linked with the creation and launch of any physical objects.
    – From 2016 – till now, co-Founder of Hushme – the World’s first voice mask for smartphones.
    Briefly about the report
    How to develop and launch hardware products into mass production in China: what should not be underestimated, what should be taken into account, possible difficulties.” Everyone knows that technical task and a set of technical documentation is required to launch the production. Some can do this having only a drawing on a napkin and his own charisma and some can’t manage this even having a complete documentation. The distinguishing feature of Chinese manufacturers – is that they`ll always say “OK” Whether it`s good or not, are there a pitfalls, we`ll discuss within our meet up.

    ×

    Vladymyr Khyliuk

    CTO at ARTKB – new product development

    Theme: How to build your product: Hardware mass production stage

    About speaker:
    – From 2011 – till now, managing partner at ARTKB – hardware product development company. Offices are located in Kyiv, Ukraine and Shenzhen, China.
    Main Competencies:
    project management
    production preparation
    – From 2014 – till now, co-Founder of Carrot – first Ukrainian acceleration program for startups,
    that are linked with the creation and launch of any physical objects.
    – From 2016 – till now, co-Founder of Hushme – the World’s first voice mask for smartphones.
    Briefly about the report
    How to develop and launch hardware products into mass production in China: what should not be underestimated, what should be taken into account, possible difficulties.” Everyone knows that technical task and a set of technical documentation is required to launch the production. Some can do this having only a drawing on a napkin and his own charisma and some can’t manage this even having a complete documentation. The distinguishing feature of Chinese manufacturers – is that they`ll always say “OK” Whether it`s good or not, are there a pitfalls, we`ll discuss within our meet up.

    ×

    Sergey Nikolenko

    Chief Research Officer, Neuromation

    Theme: What Do AlphaGo and AlphaZero Do, Exactly? Deep Reinforcement Learning

    About speaker:
    Research Fellow at the Russian Academy of Sciences, Chief Research Officer at Neuromation. Research engineer in the field of machine learning (deep neural learning, Bayesian methods, NLP, etc.), algorithms analysis (network algorithms, competitive analysis), bioinformatics, author of more than 120 scientific papers, several books, popular authoring courses.
    Briefly about the report
    “On March 9-15, 2016, the AlphaGo program, created by Google DeepMind based on the methods of deep reinforcement learning, won Li Sedol, a GO 9 professional and one of the best human players, with a score of 4-1, and most recently AlphaZero has learned to play GO, chess and shogi are better than the former, without using any outside information at all, just the rules of the game. In the report, we will try to answer the following questions:
    – why it is so important and difficult, after all, it would seem, DeepBlue beat Kasparov ten years
    ago?
    – What is deep reinforcement learning, how does it work?
    – What are the main ideas of AlphaGo actually, what is the breakthrough?
    – why these toys? why can AlphaGo ideas be used in particular and deep reinforcement learning in general?

    ×

    Illarion Khlestov

    Research Engineer, Ring Ukraine

    WorkShop: Deploy Machine Learning systems at the production level

    About speaker:
    Currently I’m working in Ring Ukraine and engaged in computer vision. Prior to that, I worked in XOResearch, was engaged in issues related to health care.
    Briefly about the workshop:
    In this lecture I will describe useful tips&tricks and important milestones while preparing and deploying your models to the production. We will discuss various frameworks, optimization methods and testing/monitoring systems from the ML point of view.

    Duration: 2 hours.
    You need a laptop to participate.

    ×

    Oleksii Vynogradov

    Founder and CEO, HeartIn

    About speaker:
    From January 2009 – until now Founder HeartIn, Inc.
    From November 2011 – until now Founder CFC.IO
    From 1999 – until now Founder ixc.ua
    HeartIn, Inc., was founded as a California corporation by Alex Vinogradov of Sunnyvale, CA and Kiev, Ukraine in response to health problems he saw in the country of his birth. A driven, goal-oriented and experienced professional investor with exquisite vision, HeartIn’s team thrives and works as one under his skillful leadership.

    ×

    Ievgen Belobrov

    Senior CRM Consultant at SMART business

    WorkShop: “How to develop full-featured chat-bot within Microsoft Bot Framework, platform LUIS and Azure Services on your own?”.

    About speaker:
    More than 6 years of experience working with CRM systems: configuration and development, participation in the implementation of projects at all stages (pre-sales demonstrations, building of the system architecture, project support).
    More than 7 years of experience in training on CRM.
    More than 4 years of working with MS SQL Server Reporting.
    More than 2.5 years of working with web technologies (JavaScript
    Briefly about the workshop:
    1. The theoretical part: cases, tasks that are solved by chatbots
    2. Frameworks and services to create a chatbot.
    3. A practical example of creating and training a chatbot.
    The time for the workshop is 2 hours
    Number of participants: 20-30 people

    ×

    Vasyl Palchykov

    Research Consultant, SoftServe
    Researcher, Institute for Condensed Matter Physics, NAS of Ukraine

    Theme: Predicting the Unpredictable. Agent-based Modeling

    About speaker:
    Vasyl Palchykov is a research consultant at SoftServe in the areas of Data Science and Agent-based Modeling (ABM) and a researcher at the Institute for Condensed Matter Physics, NAS of Ukraine. Currently he is working on applying ABM to investigate and improve blockchain-based business solutions. In the past he performed research on the edge of Physics, Computer and Social Sciences at Aalto University (Finland) and Leiden University (the Netherlands) and has a number of publication in international scientific journals. His research has been highlighted by such media as The Economist, BBC News, LA Times and Scientific American.
    Briefly about the report:
    Imagine that a company would like to implement a new idea. This idea is unique and you have no clue whether it will work or not. Or imagine a government that would like to change the rules and is concerned whether such change will not destroy the economy of the country. Such scenarios are very difficult to predict due to the lack of historical records with given settings and possible emergent phenomena. In this this talk we’ll introduce Agent-based Modeling (ABM): a methodology that allows us to make such predictions. Using real examples we’ll explore successful applications of ABM, describe its building blocks and pay attention to the potential risks.

    ×

    Jane Klepa

    Executive director,
    1991 Open Data Incubator

    About Jane:
    Executive Director at 1991 Open Data Incubator, Co-founder & CMO at SPREAD.
    Startups mentor and adviser. Teenagers mentor at ukrainian business schools (Computer academy “Step” and “Creators”). Actively involved in the tech ecosystem development in Kyiv and Ukraine.
    Expert of a corporate social responsibility. Her first exposure to Ukraine’s IT world was when she worked as a PR-manager at tech events. Then moved to work as an organizer of largest tech events (Ukraine, Russia, Poland).

    ×

    Daniel Chernega

    Comandante in
    “Che – Guerrilla Marketing”

    About Daniel:
    Founder of “CHE-guerrilla marketing”.
    A passionate, futuristic, technocratic dreamer, and evangelist of change.
    “I believe that data science will change the perception of how we live and conceive. Humanity is on the verge of colossal changes, which is why the best we can do is to be surrounded by people who are just dreaming that same way. Those who dream not only to observe changes but also to be their main driving force.
    So, see at Data Science UA Conference 2018.

    ×

    Viktor Sdobnikov

    Head of Strategic RnD, Apostera GmbH

    Theme: Fusion of visual recognition results and sensors data using Unscented Kalman Filtering

    About speaker:
    On current position Viktor is responsible for Apostera Strategic RnD Roadmap and Strategy development and implementation, which includes building Advanced Driver-Assistance Systems prototypes, involving state of the art computer vision and patterns recognition techniques. Has over ten years of experience in commercial and research projects in the field of computer vision and patterns recognition, as well as experience in technical project management at various stages and at different scales.
    Active co-organizer of the new master’s specialization “Mathematical Methods of Patterns Recognition and Computer Vision” and assistant of practical classes in the course of Prof. Dr. Mikhail Schlesinger “Statistical and structural methods of image recognition”, Institute of Physics and Technology, KPI. VP at Hackathon Expert Group. Co-initiator of UAIQ.ORG project, a platform for raising funds for participation in international olympiads, holding local olympiads and other systematic non-profit educational initiatives in mathematics, physics, and computer science in Ukraine among high school and university students.
    Briefly about the report:
    Fusion of visual recognition results and sensors data using Unscented Kalman Filtering Agenda:
    Data fusion: motivation and problem statement – 5m
    Kalman filtering for linear and non-linear systems – 10m
    Example of UKF for combining data from sensors and visual recognition results in vehicle – 15-20m
    UKF implementation examples in CV and ML – 5m
    Q&A – 5-10m

    ×

    Oleksii Potapenko

    Lead Data Scientist, OSA Hybrid PLatform

    Theme: Latent representations and variational autoencoders

    About speaker:
    Oleksii works as a leading researcher at ECR OSA Hybrid Platform, which creates solutions for product retail based on machine learning methods. In particular, in the process of work it encounters a wide range of tasks of machine learning: classification, clustering, regression, reduction in the dimensionality of data. Before that he worked in the financial sector on the task of forecasting the proceeds from portfolios with overdue consumer loans. Has more than five years of experience in data research.
    Briefly about the report:
    Discriminatory models of machine learning, models that are able to allocate any properties of objects, are now widely used, but there is also a more interesting class of models: generative. These models allow the creation of objects with given properties: for example, to write chemical formulas for cancer-free drugs previously unknown to mankind or to draw advertising banners that will be called by a particular audience.
    Among generative neural networks, two classes are most popular: VAEs and GANs. During the presentation, we will consider variation encoders, while maintaining the balance between the availability of presentation and mathematical rigor. The introduction of the Bayesian approach to the report may also be useful for self-study of such topics as deep reinforcement learning.

    ×

    Nikolay Lysenko

    Data Scientist, Yandex Data Factory
    Data Scientist, OSA Hybrid PLatform

    Theme: Latent representations and variational autoencoders

    About speaker:
    Nikolay is engaged in predictive analytics and modeling in Yandex Data Factory.
    Prior to that, he worked in the ECR OSA Hybrid Platform on projects related to forecasting, character building and classifications.
    He is interested in neural network technologies and in solving problems requiring a non-trivial approach.
    Engaged in computer analysis of data for more than three years.
    Briefly about the report:
    Discriminatory models of machine learning, models that are able to allocate any properties of objects, are now widely used, but there is also a more interesting class of models: generative. These models allow the creation of objects with given properties: for example, to write chemical formulas for cancer-free drugs previously unknown to mankind or to draw advertising banners that will be called by a particular audience.
    Among generative neural networks, two classes are most popular: VAEs and GANs. During the presentation, we will consider variation encoders, while maintaining the balance between the availability of presentation and mathematical rigor. The introduction of the Bayesian approach to the report may also be useful for self-study of such topics as deep reinforcement learning.

    ×

    Ivan Fedorov

    New Business Director, Admixer

    Theme: Five biggest Data-Science challenges in the advertising industry

    About speaker:
    In the Internet-marketing since 2000, more than 10 years in development, he was involved in promotion and monetization of the large data projects. The last three years is engaged in development and launch of the new advertising products related to the behavioural technologies, e-commerce and mobile advertizing.
    Briefly about the report:
    Marketing industry, one of the first began to widely use data-based tools and build solutions based on them. Despite the widespread use of such tools, the main tasks have not yet been solved. What can AI do in advertising today and what else should Ad Tech companies do in the near future in conjunction with Data Specialist.

    ×

    Vitalii Bondarenko

    Enterprise Platform Architect, Big Data Architect, Eleks

    Theme: Building Data Science Platform as a main component of Enterprise Digital Transformation

    About speaker:
    I’ve been designing data-centric systems for last 20 years and have gained huge experience in developing and performance tuning of OLTP, DW and BI applications. Last 4 year I’m mostly focused on Data Analytics on Big Data Clusters and implementing different innovative approaches for Fast Data Processing. At the moment my responsibility is to design and develop Enterprise Data Platform.
    Briefly about the report:
    We will discuss how building Data Science platform can help in Enterprise Digital Transformation, will cover several Hybrid Cloud Architectures, review approaches and challenges of deploying trained models to production environments. Will focus on several common problems like Anomaly Detection and Classification using different approaches, such as CNN, K-means and others. I’ll show how to train models with getting data from different data sources: Kafka, Cassandra, OLAP Cubes, Elasticsearch, Data Warehouses, Hadoop and deploy trained models to Enterprise Infrastructure Components like ESB, Message Buses and ETL. The presentation has lots of demos and samples from real

    ×

    Sergey Borislavskiy

    Head of revenue management & big data, Vodafone Ukraine

    Theme: Great experiment with big data – Can you make a business in Big Data in Ukraine?

    About speaker:
    Info coming soon
    Briefly about the report:
    Info coming soon

    ×

    Sergey Borislavskiy

    Head of revenue management & big data, Vodafone Ukraine

    About speaker:
    Info coming soon

    ×

    Olexiy Oryeshko

    Staff Software Engineer,
    Google Search

    Theme: How we Learned to Stop Worrying and Love Machine Learning

    About speaker:
    Olexiy is developing an interactive platform for data science and machine learning. Olexiy also consults product teams across Google on the best practices of applied machine learning. Olexiy has used his experience to improve Play Store search, YouTube search, and YouTube recommendations systems.
    Previously, Olexiy was an SRE for storage systems in Dublin, Ireland. Prior to Google, he coordinated algorithm competitions for TopCoder, and was a SWE for Materialise. In 2004, Olexiy earned his MS degree in computer science from Kyiv University.
    Briefly about the report:
    How optimizing a metric is different from creating a lovable product? How a human can control and guide the machine? Why predictable behavior and understandable long-term effects are crucial for your product?
    We will try to answer those questions, and will talk about limitations of the “pure” machine learning approach. Aslo, we will see practical workflows which allow engineers to combine their intuition with the power of machine learning to build great products.


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    David Braun

    CEO & co-founder TemplateMonster

    Theme: Big data and adaptive marketing. Friendship or Love?


    flag_ua

    Olexiy Oryeshko

    Staff Software Engineer,
    Google Search

    Theme: How we Learned to Stop Worrying and Love Machine Learning


    flag_ua

    Borys Pratsiuk

    Head of R&D, Ciklum

    Theme: Data Science education for Managers


    flag_ua

    Oleg Boguslavskyi

    General Manager, Ring Ukraine

    Theme: Correct problem statement in ML tasks
    as a key to success


    flag_ua

    Sergey Borislavskiy

    Head of revenue management & big data, Vodafone Ukraine

    Theme: Great experiment with big data – Can you make a business in Big Data in Ukraine?


    flag_ua

    Alex Nesterenko

    CEO & Founder, ARTKB

    Theme: How to build your product: Hardware mass production stage


    flag_ru

    Sergey Nikolenko

    Chief Research Officer, Neuromation

    Theme: What Do AlphaGo and AlphaZero Do, Exactly? Deep Reinforcement Learning


    flag_ua

    Vasyl Palchykov

    Research Consultant, SoftServe
    Researcher, Institute for Condensed Matter Physics, NAS of Ukraine

    Theme: Predicting the Unpredictable. Agent-based Modeling


    flag_ua

    Viktor Sdobnikov

    Head of Strategic RnD, Apostera GmbH

    Theme: Fusion of visual recognition results and sensors data using Unscented Kalman Filtering


    flag_ru

    Nikolay Lysenko

    Data Scientist, Yandex Data Factory
    Data Scientist, OSA Hybrid PLatform

    Theme: Latent representations and variational autoencoders


    flag_ua

    Ivan Fedorov

    New Business Director, Admixer

    Theme: Five biggest Data-Science challenges in the advertising industry


    flag_ua

    Vitalii Bondarenko

    Enterprise Platform Architect, Big Data Architect, Eleks

    Theme: Building Data Science Platform as a main component of Enterprise Digital Transformation

    Workshops


    flag_ua

    Illarion Khlestov

    Research Engineer, Ring Ukraine

    WorkShop: Deploy Machine Learning systems at the production level


    flag_ua

    Ievgen Belobrov

    Senior CRM Consultant at SMART business

    WorkShop: “How to develop full-featured chat-bot within Microsoft Bot Framework, platform LUIS and Azure Services on your own?”.

    Panel discussion

    Theme: “Data Science: the real business booster”.

    Braun
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    David Braun

    CEO & co-founder TemplateMonster

    Pratsiuk
    flag_ua

    Borys Pratsiuk

    Head of R&D, Ciklum

    vinogradov
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    Oleksii Vynogradov

    Founder and CEO, HeartIn


    flag_ua

    Sergey Borislavskiy

    Head of revenue management & big data, Vodafone Ukraine

    Program Conference

    Technical stream
    Business stream
    Workshops

    9:00 – 10:00
    Registration

    10:00 – 10:30
    Keynote speech

    10:40 – 11:25

    PDF

    Sergey Nikolenko

    Theme: What Do AlphaGo and AlphaZero Do, Exactly? Deep Reinforcement Learning

    PDF

    Ivan Fedorov

    Theme: Five biggest Data-Science challenges in the advertising industry

    11:25 – 11:50
    Coffee Break

    11:50 – 12:35

    PDF

    Vitalii Bondarenko

    Theme: Building Data Science Platform as a main component of Enterprise Digital Transformation

    PDF

    Oleg Boguslavskyi

    Theme: Correct problem statement in ML tasks as a key to success

    12:45 – 13:30

    PDF

    Nikolay Lysenko

    Theme: Latent representations and variational autoencoders

    PDF

    Alex Nesterenko

    Theme: How to build your product: Hardware mass production stage

    11:25 – 13:30

    Ievgen Belobrov

    Workshop: “How to develop full-featured chat-bot within Microsoft Bot Framework, platform LUIS and Azure Services on your own?”.

    13:30 – 14:30
    Lunch

    14:30 – 15:15

    PDF

    Vasyl Palchykov

    Theme: Predicting the Unpredictable. Agent-based Modeling

    PDF

    Sergey Borislavskiy

    Theme: Great experiment with big data – Can you make a business in Big Data in Ukraine?

    15:25 – 16:10

    PDF

    Viktor Sdobnikov

    Theme: Fusion of visual recognition results and sensors data using Unscented Kalman Filtering

    Borys Pratsiuk

    Theme: Data Science education for Managers

    16:10 – 16:35
    Coffee Break

    14:30 – 16:35

    Illarion Khlestov

    WorkShop: Deploy Machine Learning systems at the production level

    16:35 – 17:20

    PDF

    Olexiy Oryeshko

    Theme: How we Learned to Stop Worrying and Love Machine Learning

    PDF

    David Braun

    Theme: Big data and adaptive marketing. Friendship or Love?

    17:30 – 19:00

    Panel discussion

    Theme: Data Science: the real business booster
    David Braun
    Borys Pratsiuk
    Oleksii Vynogradov
    Sergey Borislavskiy

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