IT Weekend Kharkiv: AI & ML

2019 Oct 26
Blagovishenska 1, Fabrika.space, Kharkiv
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IT Weekend Kharkiv:

Artificial Intelligence & Machine Learning

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Program

Program

Stream 1

Stream 2

Stream 1

Stream 2

09:00 10:00

Registration and Coffee Break

10:00 10:10

Official Conference Opening

COgnitive Analysis of CHange – COACH

George Stark

Distinguished Engineer at IBM

George Stark is a Distinguished Engineer with the IBM corporation who specializes in data science applied to IT operations.  George has received 13 patents and published more than 40 articles in the areas of software reliability, service management, and data center operations.  He recently led the creation of the Data Science Certification offered by The Open Group . more

TBD

Anton Vaisburd

Anton is a data science manager with over 7 years industry experience, strong analytical skills and broad range of computer expertise with a background in financial and risks domains. He has an extensive knowledge in hands-on application of machine learning techniques, such as logistic regression models, neural networks, decision trees and boosting algorithms application, signal processing for a number of projects, including application and behavioral scorecard building and marketing personalization, predictive maintenance and price/demand prediction. more

MLOPS: From research to production in days not months

Starting from a small AI/ML experiment or a proof of concept, all the way down to the production-grade system, a machine learning solution lifecycle and infrastructure cover much broader space than just an ML model code. It often consists of multiple stages and many different building blocks, frameworks and modules. Productionizing ML training and serving workflows brings up new technological and operational challenges, such model deployment, management, monitoring, optimization and integrations. These challenges cannot be addressed by a team of data scientists by themselves and requires strong collaboration and cooperation with different business stakeholders, software engineering and DevOps teams. In this session, Iurii will share design recommendations and CI/CD best practices in building large-scale AI and Machine Learning systems using open source and cloud-native technologies to address nowadays business and technical challenges and bridge the gap between data, science, IT, business stakeholders and end-users.

Starting from a small AI/ML experiment or a proof of concept, all the way down to the production-grade system, a machine learning solution lifecycle and infrastructure cover much broader space than just an ML model code. It often consists of multiple stages and many different building blocks, frameworks and modules. Productionizing ML training and serving workflows brings up new technological and operational challenges, such model deployment, management, monitoring, optimization and integrations. These challenges cannot be addressed by a team of data scientists by themselves and requires strong collaboration and cooperation with different business stakeholders, software engineering and DevOps teams. In this session, Iurii will share design recommendations and CI/CD best practices in building large-scale AI and Machine Learning systems using open source and cloud-native technologies to address nowadays business and technical challenges and bridge the gap between data, science, IT, business stakeholders and end-users.

Iurii Milovanov

Data Science Practice Leader, SoftServe

Iurii Milovanov is a Director of AI & Data Science at SoftServe with more than 10 years of experience in building enterprise-level AI, big data and advanced analytics solutions. Iurii is a computer science expert with strong emphasis on cutting-edge technologies. His research interests include various aspects of modern, progressive IT, and state-of-the-art artificial intelligence, such as distributed and parallel computing, large-scale machine learning, natural language processing, computer vision, and speech recognition. Iurii is actively contributing to various research and scientific communities, including his participation in the KarooGP project, a genetic programming suite used at LIGO Lab for detecting gravitational-waves; SIMOC, an interactive model of a scalable, human community located on a remote planet; and DRLearner project, the first open source implementation of Google’s Deep Reinforcement Learning (DQN) algorithm for playing ATARI games. more

Cloud Capacity Planning models

Capacity demand forecast in Cloud industry is highly important right now as the Cloud technologies and businesses are constantly growing. Oracle provides variety of cloud services such IaaS, PaaS, SaaS, DaaS that require different approaches for Capacity Planning and Management. During the presentation, we’ll talk about forecast models, which describe the demand for crucial resource types and combines unsupervised clustering technique, piecewise linear modelling and ensembles of models deployed for PaaS, SaaS. Designed forecast methods consider the specificity of usage behavior change for different services and critical types of resources.

Capacity demand forecast in Cloud industry is highly important right now as the Cloud technologies and businesses are constantly growing. Oracle provides variety of cloud services such IaaS, PaaS, SaaS, DaaS that require different approaches for Capacity Planning and Management. During the presentation, we’ll talk about forecast models, which describe the demand for crucial resource types and combines unsupervised clustering technique, piecewise linear modelling and ensembles of models deployed for PaaS, SaaS. Designed forecast methods consider the specificity of usage behavior change for different services and critical types of resources.

Anna Chystiakova

Principal Data Scientist, Oracle, USA

12 years in Information technology, 8 years in analytics and Data Science. I’m analyst of computer systems by education, engineer by heart and data scientist by profession. Have been building data models and analytical solutions for Oracle for 4 years right now. more

TBD

Mykola Maksymenko

R&D Director at SoftServe

Mykola Maksymenko drives technological development in applied science, human-computing interactions, and sensing. Mykola holds a Ph.D. in Theoretical Condensed Matter Physics, with over ten years of industry experience, previously working at the Max Planck Institute for the Physics of Complex Systems and the Weizmann Institute of Science.   more

Unsupervised Real-Time Stream-Based Novelty Detection Technique

Anna Vergeles

Researcher, Lead of DataOps team at Oracle

Adores Lewis Carroll: “…it takes all the running you can do, to keep in the same place. If you want to get somewhere else, you must run at least twice as fast as that!”(c) more

Topological data analysis for discovering patterns in multidimensional data

Yan Rybalko

Data/Research Analyst, Intego Group

I am a PhD student in the mathematical department of B. Verkin Institute for Low Temperature Physics and Engineering, my speciality is mathematical physics. Currently I have two articles in the peer-reviewed abroad journals (Journal of Mathematical Physics, Opuscula Mathematica). Since 2018 I have been working as a data research analyst in the data analysis team at Intego group LLC. At work I primarily deal with the analysis of the high dimensional data sets by using machine learning algorithms, statistical methods, cutting edge topological approaches, such as mapper and persistent homology. Additionally, I am interested in the analysis of networks, particularly, in the community search on graphs. more

How DataScience helps to see more in your data and fight with Fraud!

I'll tell you how to build the Datasines team of your dreams and build a revolutionary product together. I will show you some interesting AI projects and tell you what to consider when doing a research project. And show you a couple of insights from our daily work in Fraud detection.

I'll tell you how to build the Datasines team of your dreams and build a revolutionary product together. I will show you some interesting AI projects and tell you what to consider when doing a research project. And show you a couple of insights from our daily work in Fraud detection.

Borys Pratsiuk

Chief Technology Officer at Scalarr

Borys has 5 years of experience in embedded development and 6 years in Android. From 2007 to 2012 worked at KPI University as a Professor assistant. In 2012 he became a Ph.D. in solid-state electronics. In 2013 Borys launched Android Dev Club and has grown it up to 300 members. From 2015 – 2019 lead R&D department at Ciklum and work with IoT, BigData, VR, Blockchain and Machine Learning projects. 2019 join Scalarr to do Big Data, DataScience and fight with Fraud! more

Symbolic transformation in Artificial Intelligence and their application in geometric control theory

Zakovorotnyi Olexandr

Doctor of Technical Science, professor of Computers and Programming Department, Academic Secretary of NTU “KhPI”

Direction of scientific researches: development of fundamental theory of optimal control and artificial neural networks Scientific Areas: control automation, geometric control theory, artificial neural networks, fuzzy logic, simulation more

Mezentsev Nikolay

PhD, professor of Computers and Programming Department

Scientific Areas: optimal control of complex technical objects, computer networks, artificial intelligence, mathematical simulation more

Formalization of Diagnostic and Treatment activity in Healthcare Decision Support Systems

The Formalized stages diagnostic-medical process at development computer decision support system in medicine. Transition from the traditional space of marks to the medical action space is offered to minimize the risk. The use of hierarchical clustering with the criterion of minimum aggregate relations (the search for the minimum cut) in the medical action space for the synthesis of the decision tree provides minimum risk of decision-making in integrated assessment of diagnostic and medical action.

The Formalized stages diagnostic-medical process at development computer decision support system in medicine. Transition from the traditional space of marks to the medical action space is offered to minimize the risk. The use of hierarchical clustering with the criterion of minimum aggregate relations (the search for the minimum cut) in the medical action space for the synthesis of the decision tree provides minimum risk of decision-making in integrated assessment of diagnostic and medical action.

Povorozniuk Anatoliy

Doctor of Technical Science, professor of Computers and Programming Department

Scientific Areas: methods and algorithms of the experimental medical data analysis, medical signals and images processing, synthesis of decision rules based on structural models, design of healthcare decision support systems control automation, computer architecture, design of specialized computer systems more

Computer vision for biometrics: from humans to animals

Biometrics has the capability to identify or verify an individual correctly by using a wide range of physiological characteristics possessed by the user. Today, it’s impossible to imagine cutting-edge biometrics-based identification systems without computer vision involvement. In his talk, Pavlo will overview both most popular biometric traits that can be used for recognition with computer vision (face, fingerprint, iris, vascular pattern, gait, etc.) and unusual ones like ear, tongue, nose and cover related algorithms used for the recognition. He will also describe two real examples of the application of computer vision methods for biometric identification: a custom solution for human vein recognition with state-of-the art accuracy based on both image processing and computer vision and the identification pipeline developed for a crocodile skin recognition.

Biometrics has the capability to identify or verify an individual correctly by using a wide range of physiological characteristics possessed by the user. Today, it’s impossible to imagine cutting-edge biometrics-based identification systems without computer vision involvement. In his talk, Pavlo will overview both most popular biometric traits that can be used for recognition with computer vision (face, fingerprint, iris, vascular pattern, gait, etc.) and unusual ones like ear, tongue, nose and cover related algorithms used for the recognition. He will also describe two real examples of the application of computer vision methods for biometric identification: a custom solution for human vein recognition with state-of-the art accuracy based on both image processing and computer vision and the identification pipeline developed for a crocodile skin recognition.

Pavlo Vyplavin

CTO, It-Jim

more

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Prices

Prices

799
till 31.08
Early Birds
899
01.09-20.10
Regular
999
21.10
Hot Birds
Speakers

Speakers

CTO, It-Jim

Pavlo Vyplavin

CTO, It-Jim

Pavlo Vyplavin

Chief Technology Officer at Scalarr

Borys Pratsiuk

Chief Technology Officer at Scalarr

Borys Pratsiuk

Borys has 5 years of experience in embedded development and 6 years in Android. From 2007 to 2012 worked at KPI University as a Professor assistant. In 2012 he became a Ph.D. in solid-state electronics. In 2013 Borys launched Android Dev Club and has grown it up to 300 members. From 2015 – 2019 lead R&D department at Ciklum and work with IoT, BigData, VR, Blockchain and Machine Learning projects. 2019 join Scalarr to do Big Data, DataScience and fight with Fraud!

Distinguished Engineer at IBM

George Stark

Distinguished Engineer at IBM

George Stark

George Stark is a Distinguished Engineer with the IBM corporation who specializes in data science applied to IT operations.  George has received 13 patents and published more than 40 articles in the areas of software reliability, service management, and data center operations.  He recently led the creation of the Data Science Certification offered by The Open Group .

Principal Data Scientist, Oracle, USA

Anna Chystiakova

Principal Data Scientist, Oracle, USA

Anna Chystiakova

12 years in Information technology, 8 years in analytics and Data Science. I’m analyst of computer systems by education, engineer by heart and data scientist by profession. Have been building data models and analytical solutions for Oracle for 4 years right now.

Doctor of Technical Science, professor of Computers and Programming Department

Povorozniuk Anatoliy

Doctor of Technical Science, professor of Computers and Programming Department

Povorozniuk Anatoliy

Scientific Areas: methods and algorithms of the experimental medical data analysis, medical signals and images processing, synthesis of decision rules based on structural models, design of healthcare decision support systems control automation, computer architecture, design of specialized computer systems

PhD, professor of Computers and Programming Department

Mezentsev Nikolay

PhD, professor of Computers and Programming Department

Mezentsev Nikolay

Scientific Areas: optimal control of complex technical objects, computer networks, artificial intelligence, mathematical simulation

Doctor of Technical Science, professor of Computers and Programming Department, Academic Secretary of NTU “KhPI”

Zakovorotnyi Olexandr

Doctor of Technical Science, professor of Computers and Programming Department, Academic Secretary of NTU “KhPI”

Zakovorotnyi Olexandr

Direction of scientific researches: development of fundamental theory of optimal control and artificial neural networks Scientific Areas: control automation, geometric control theory, artificial neural networks, fuzzy logic, simulation

Anton Vaisburd

Anton Vaisburd

Anton is a data science manager with over 7 years industry experience, strong analytical skills and broad range of computer expertise with a background in financial and risks domains. He has an extensive knowledge in hands-on application of machine learning techniques, such as logistic regression models, neural networks, decision trees and boosting algorithms application, signal processing for a number of projects, including application and behavioral scorecard building and marketing personalization, predictive maintenance and price/demand prediction.

Researcher, Lead of DataOps team at Oracle

Anna Vergeles

Researcher, Lead of DataOps team at Oracle

Anna Vergeles

Adores Lewis Carroll: “…it takes all the running you can do, to keep in the same place. If you want to get somewhere else, you must run at least twice as fast as that!”(c)

Data/Research Analyst, Intego Group

Yan Rybalko

Data/Research Analyst, Intego Group

Yan Rybalko

I am a PhD student in the mathematical department of B. Verkin Institute for Low Temperature Physics and Engineering, my speciality is mathematical physics. Currently I have two articles in the peer-reviewed abroad journals (Journal of Mathematical Physics, Opuscula Mathematica). Since 2018 I have been working as a data research analyst in the data analysis team at Intego group LLC. At work I primarily deal with the analysis of the high dimensional data sets by using machine learning algorithms, statistical methods, cutting edge topological approaches, such as mapper and persistent homology. Additionally, I am interested in the analysis of networks, particularly, in the community search on graphs.

Data Science Practice Leader, SoftServe

Iurii Milovanov

Data Science Practice Leader, SoftServe

Iurii Milovanov

Iurii Milovanov is a Director of AI & Data Science at SoftServe with more than 10 years of experience in building enterprise-level AI, big data and advanced analytics solutions. Iurii is a computer science expert with strong emphasis on cutting-edge technologies. His research interests include various aspects of modern, progressive IT, and state-of-the-art artificial intelligence, such as distributed and parallel computing, large-scale machine learning, natural language processing, computer vision, and speech recognition. Iurii is actively contributing to various research and scientific communities, including his participation in the KarooGP project, a genetic programming suite used at LIGO Lab for detecting gravitational-waves; SIMOC, an interactive model of a scalable, human community located on a remote planet; and DRLearner project, the first open source implementation of Google’s Deep Reinforcement Learning (DQN) algorithm for playing ATARI games.

R&D Director at SoftServe

Mykola Maksymenko

R&D Director at SoftServe

Mykola Maksymenko

Mykola Maksymenko drives technological development in applied science, human-computing interactions, and sensing. Mykola holds a Ph.D. in Theoretical Condensed Matter Physics, with over ten years of industry experience, previously working at the Max Planck Institute for the Physics of Complex Systems and the Weizmann Institute of Science.  

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