DATA SCIENCE UA CONFERENCE -

DATA SCIENCE UA

CONFERENCE

KYIV
MARCH 16
2019
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  • THE KNOWLEDGE IS EVERYTHING

    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.
    On March 16 we will be gathering for the 6th time in Kyiv.

    400
    Participants
    18
    Speakers
    3
    Tracks
    10
    Hours of networking

    Stages


    Tech

    Business

    Workshops

    Panel discussion

    Speakers

    Professor Marcel Worring

    Director of the Informatics Institute and Full Professor Amsterdam Business School,University of Amsterdam

    (Hyper) graph convolutional networks for analyzing social multimedia data.

    Oles Petriv

    CTO, NeoCortext

    Face transfer on video using conditional GANs

    Nazar Shmatko

    VP of engineering, NeoCortex

    Face transfer on video using conditional GANs

    Elizaveta Lebedeva

    Data Scientist, Taxify

    Experiments in Data Science: beyond AB-tests and p-values

    Eugene Potemskyi

    COO, DataTrading — AI for smart trading decisions

    Taming Neural Networks or In Search of the Soul of Artificial Intelligence

    Anton Vokrug

    CEO, DataTrading — AI for smart trading decisions

    Taming Neural Networks or In Search of the Soul of Artificial Intelligence

    Ruslan Gutnikov

    Executive Director of OMD MD Intelligence

    Brand Metrics®: How Data Science helps boost the ROI of the bank's advertising campaign

    Yana Fareniuk

    Head of Data Science Analytics, OMD MD Intelligence

    Brand Metrics®: How Data Science helps boost the ROI of the bank's advertising campaign

    Tetiana Hladkykh

    Research Data Scientist, PhD in Technical Science, SoftServe

    Predictive Analytics in Human Resources

    Dmytro Zikrach

    Data Scientist, PhD in Mathematics, SoftServe

    Predictive Analytics in Human Resources

    Ksenia Demskaya

    Research Engineer at Ciklum

    Adversarial attacks on deep neural networks

    Professor Marcel Worring

    Director of the Informatics Institute and Full Professor Amsterdam Business School,University of Amsterdam

    (Hyper) graph convolutional networks for analyzing social multimedia data.

    Oles Petriv

    CTO, NeoCortext

    Face transfer on video using conditional GANs

    Nazar Shmatko

    VP of engineering, NeoCortex

    Face transfer on video using conditional GANs

    Elizaveta Lebedeva

    Data Scientist, Taxify

    Experiments in Data Science: beyond AB-tests and p-values

    Eugene Potemskyi

    COO, DataTrading — AI for smart trading decisions

    Taming Neural Networks or In Search of the Soul of Artificial Intelligence

    Anton Vokrug

    CEO, DataTrading — AI for smart trading decisions

    Taming Neural Networks or In Search of the Soul of Artificial Intelligence

    Руслан Гутніков

    Executive Director of OMD MD Intelligence

    Info coming soon

    Яна Фаренюк

    Head of Data Science Analytics, OMD MD Intelligence

    Info coming soon

    Tetiana Hladkykh

    Research Data Scientist, PhD in Technical Science, SoftServe

    Predictive Analytics in Human Resources

    Dmytro Zikrach

    Data Scientist, PhD in Mathematics, SoftServe

    Predictive Analytics in Human Resources

    Ksenia Demskaya

    Research Engineer at Ciklum

    Adversarial attacks on deep neural networks

    PANEL DISCUSSION

    Oleg Boguslavskyi

    General Manager, Ring Ukraine

    Panel Discussion

    Dimitri Podoliev

    Founder, iHUB

    Panel Discussion

    Hosts

    Daniel Che

    Comandante

    Che – Guerrilla Marketing

    Jane klepa

    Executive director

    1991 Open Data Incubator

    Tickets

    Buy 5 tickets — Get 10% off
    Buy 10 tickets — Get 15% off
    25% discount for students
    1450
    January 15 — January 31
    2150
    February 1 — February 28
    2750
    March 1 — March 15
    3000
    March 16

    Become one of our Partners

    The Data Science UA Conference attracts both, the best and brightest attendees, and many of the top companies using data science.

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    VENUE

    Oasis concert hall Kyiv

    Kyiv, Lipkovskogo str, 1A
    Ultramarine, 3rd floor

    Location

    Oasis concert hall Kyiv

    Kyiv, Lipkovskogo str, 1A
    Ultramarine, 3rd floor


    Contact information

    +38 099 055 23 92
    info@data-science.com.ua
    #datascienceua

    ×

    Professor Marcel Worring

    Director of the Informatics Institute and Full Professor Amsterdam Business School,University of Amsterdam

    Technical track Language: English

    (Hyper) graph convolutional networks for analyzing social multimedia data.

    Prof. dr. Marcel Worring is a full professor in Data Science for Business Analytics at the Amsterdam Business School of the University of Amsterdam (UvA). He is the director of the Informatics Institute of the UvA where he is an associate professor and does most of his research. He was an associate director of Amsterdam Data Science a network based organization with companies and researchers to promote data science in Amsterdam. He is also one of the co-founders of the recently launched Innovation Center for Artificial Intelligence in which companies / government organizations and knowledge institutes work together on a common research agenda in labs of at least five sponsored PhD students.

    We will give an overview of different methods of face transfer and our own system based on conditional generative adversarial networks. Also, we will talk about the problems we met with temporal video frame consistency and the ways to solve it.

    Oles Petriv

    CTO, NeoCortext

    Technical track Language: ukrainian

    Face transfer on video using conditional GANs

    Oles has been involved in active research and development of computer vision and natural language processing systems for the last 6 years. He is the author of Machine Learning course on Prometheus and Deep Learning course in ARVI Lab. Oles has a strong background in video processing using deep learning methods for object and action detection, image and video captioning for Hollywood movie studios

    We will give an overview of different methods of face transfer and our own system based on conditional generative adversarial networks. Also, we will talk about the problems we met with temporal video frame consistency and the ways to solve it.

    Nazar Shmatko

    VP of engineering, NeoCortex

    Technical track Language: ukrainian

    Face transfer on video using conditional GANs

    Nazar, as a specialist in machine learning, has experience in computer vision, natural language processing, and healthcare. He is currently actively working with generative models for the project to transfer facial rice.

    We will give an overview of different methods of face transfer and our own system based on conditional generative adversarial networks. Also, we will talk about the problems we met with temporal video frame consistency and the ways to solve it.

    Elizaveta Lebedeva

    Data Scientist, Taxify

    Technical track Language: russian

    Experiments in Data Science: beyond AB-tests and p-values

    Elizaveta Lebedeva works as Data Scientist at Taxify. Her main focus is supporting lifecycle marketing campaigns, ensuring the company growth and delivering the best experience for riders and drivers. Being passionate about math and having degrees in finance and economics, she transitioned to Data Science from Business Analytics, marking her path with numerous math competitions and hackathons.

    Experiments are a key part in applied data science. Running A/B tests (either for models or for product features) and evaluating results with t-tests is common practice. But there are much more methods to perform experiments. This presentation will cover causal inference, multi-armed bandit (MAB)-based continuous experiments, as well as frequent pitfalls in A/B/N tests and guidelines on how to avoid them. In addition, I talk about different types of metrics used in experiments and how to improve test evaluation with outlier detection, variance reduction, and pre-experiment bias.

    Anton Vokrug

    CEO, DataTrading — AI for smart trading decisions

    Business track Language: Russian

    Taming Neural Networks or In Search of the Soul of Artificial Intelligence

    He have over 10 years of experience in IT entrepreneurship. Successful sale of several businesses. Marketing and Sales in IT. Blockchain and ICO support.

    Real experience in developing commercial products based on artificial intelligence algorithms. Expectations and reality. Curiosities, fears, and joys of the achieved results.

    Eugene Potemskyi

    COO, DataTrading — AI for smart trading decisions

    Business track Language: Russian

    Taming Neural Networks or In Search of the Soul of Artificial Intelligence

    Eugene is a COO at DataTrading (which has offices in China and the United States), which deals with the prediction of financial markets for artificial intelligence. Eugene constantly speaks at international conferences devoted to blockchain and artificial intelligence. Basic skills and competencies: investment analysis (more than 10 years), development and management of systems for working with large data (more than 4 years).

    Real experience in developing commercial products based on artificial intelligence algorithms. Expectations and reality. Curiosities, fears, and joys of the achieved results.

    Ruslan Gutnikov

    Executive Director of OMD MD Intelligence

    Business track Language: Russian

    Brand Metrics®: How Data Science helps boost the ROI of the bank's advertising campaign

    Ruslan is an ambassador of introduction of data-based marketing solutions. He has 8 years of experience in the country's largest advertising holdings, which resulted in expertise in the field of media and communication planning, a deep understanding of market opportunities and business needs. Today, he is responsible for the development of the OMD MD Intelligence analytical unit. The main focus of the team is the introduction of data science technologies to optimize marketing investments, provide integrated marketing analytics, strategic customer support.

    Bank calls to the Call Center, online application forms and visits to the branch offices themselves are the main channels for the sale of consumer products. The main purpose of any advertising campaign is to attract attention to the bank's offers and increase the number of customer queries. Thanks to our Brand Metrics® approach, we can clearly identify and distinguish the impact factors on the feedback of potential customers, determine which communication channels to use to increase inflow of queries. In the speech, we will talk about one component of this project - a case for forecasting and managing calls in the Call Center. We will talk about how to increase the effectiveness of advertising campaign by 8% in the first 2 months. We will go through the key stages of implementing our approach, directly in the process of machine learning and dwell on the important technical aspects of the implementation of forecasting models.

    Yana Fareniuk

    Head of Data Science Analytics, OMD MD Intelligence

    Business track Language: Russian

    Brand Metrics®: How Data Science helps boost the ROI of the bank's advertising campaign

    Yana is a leading specialist in the development and implementation of mathematical modeling projects and machine learning in the media field. Over the past 3 years, she has managed to implement more than 50 optimization projects for clients in the financial category, telecom, pharmaceutical industry, e-commerce, online and offline retail. Yana Fareniuk has a Master Degree specializing in "Economic Cybernetics", Kyiv Taras Shevchenko National University. Today, she is responsible for customer support through the Brand Metrics® approach, developed by OMD MD Intelligence, based on machine learning technology and aimed at optimizing customer marketing campaigns.

    Bank calls to the Call Center, online application forms and visits to the branch offices themselves are the main channels for the sale of consumer products. The main purpose of any advertising campaign is to attract attention to the bank's offers and increase the number of customer queries. Thanks to our Brand Metrics® approach, we can clearly identify and distinguish the impact factors on the feedback of potential customers, determine which communication channels to use to increase inflow of queries. In the speech, we will talk about one component of this project - a case for forecasting and managing calls in the Call Center. We will talk about how to increase the effectiveness of advertising campaign by 8% in the first 2 months. We will go through the key stages of implementing our approach, directly in the process of machine learning and dwell on the important technical aspects of the implementation of forecasting models.

    Tetiana Hladkykh

    Research Data Scientist, PhD in Technical Science, SoftServe

    Business track Language: Russian

    Predictive Analytics in Human Resources

    Tetiana is a Senior Data Scientist with strong computer science background. Her area of scientific interest includes Data mining, Artificial Intelligence, Mathematical Statistic, Computer Vision, Graph Theory, Game Theory and Computational mathematics. Tetiana has 18 years of academic experience and is an author of more than 35 scientific publications in fields of Electronic circuit simulation and Applied problems of Artificial Intelligence. Additionally, Tatiana was a research advisor in 26 scientific projects and publications in Computer Vision, Multi-criterion optimization and Economic planning and forecasting.

    Human Resource analytics is addressed to the analysis of any employees-related problems in any organization. The most significant problems facing by managers are connected with employee turnover. Turnover numbers were always important for any organization, especially in the case of their continued growth – the more the organization is growing the more these numbers become critical. We are presenting our solution in the context of HR analytics aimed to notify direct people managers about the risks of their employee leaving. Our data-driven solution with the state-of-art engine that uses machine learning approaches to help managers proactively manages unwanted dismissals.

    Dmytro Zikrach

    Data Scientist, PhD in Mathematics, SoftServe

    Business track Language: Russian

    Predictive Analytics in Human Resources

    Dmytro successfully combines knowledge in Mathematics and practical approaches in the Data Science area. He has two Master's degree (in Mathematics, and Finance), and he has the Ph.D. Dmytro has more than 50 scientific publications. His scientific interest lies in the area of Mathematical and Complex Analysis, Theory of Probabilities, Machine Learning and Predictive Analytics, Computer Vision and Time-series Analytics. Dmytro has great experience and research in Statistical Learning, Predictive Analytics, NLP, Time Series Analysis, Artificial Intelligence and Recommender Systems. Moreover, he often takes part in various Data Science community competitions, hackathons, and challenges.

    Human Resource analytics is addressed to the analysis of any employees related problems in any organization. The most significant problems facing by managers are connected with employee turnover. Turnover numbers were always important for any organization, especially in the case of their continued growth – the more the organization is growing the more these numbers become critical. We are presenting our solution in the context of HR analytics aimed to notify direct people managers about the risks of their employee leaving. Our data-driven solution with the state-of-art engine that uses machine learning approaches to help managers proactively manages unwanted dismissals.

    Ksenia Demskaya

    Research Engineer at Ciklum

    Technical track Language: ukrainian

    Adversarial attacks on deep neural networks

    Ksenia had graduated from Institute of Physics and Technology of the Igor Sikorsky KPI with a degree in Applied Physics. Currently she's working as a research engineer at Ciklum in the area of Deep Learning applied to computer vision tasks, such as image classification, object detection and segmentation. Her research interests cover the theoretical aspects of Deep Learning and problems of interpretability of convolutional neural networks.

    Recent advances in Deep Learning show the existence of well-designed input samples, called adversarial examples, that can fool a state-of-the-art network and cause it to make a mistake. We will discuss the following questions: 1) what are the adversarial examples 2) how to generate the adversarial attacks and to defend against them 3) what are the current challenges in this field of research 4) do we need to be aware of adversarial attacks

    Oleg Boguslavskyi

    General Manager, Ring Ukraine

    Language: Russian

    Panel Discussion

    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.

    Dimitri Podoliev

    Founder, iHUB

    Language: Russian

    Panel Discussion

    Не got an engineering degree from Cambridge University and MIT. Over the past decade, he founded one of the largest tech accelerators in Eastern Europe as well as was on the core team of an Agriculture Company which grew from zero to 2000+ employees with over $300mln in total invested capital. His specialties are hybrid mobile app development, deep neural networks, machine learning, smart contracts and blockchain-based tools and niche mobiles marketplaces.