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

grey hall

3rd floor

blue hall

2nd floor

Workshops

2nd floor, green hall

grey hall

blue hall

Workshops

09:00 10:00

Registration and Coffee Break

10:00 10:15

Official Conference Opening

10:15 11:15

COgnitive Analysis of CHange – COACH

COACH (COgnitive Analysis of Change) - it's an IBM Services platform for analyzing changes in IT operations that relies on the use of IBM Watson cognitive services to increase the relevance of predictions. The COACH intelligent core is based on the analysis and understanding of natural language texts and the use of complex machine learning models to calculate the risk associated with a request for a change in the IT field. Based on an analysis of historical incident data, COACH allows you to discover characteristic relationships, assess the similarities between new and historical change records, associate changes and incidents with configuration items, and assess the likelihood that a proposed change is a failure indicator. In his report, George will touch upon questions of predictive analytics regarding the risk of changes and talk about a new approach based on the analysis of historical information about server failures, incidents caused by changes, taking into account information received from experts.

COACH (COgnitive Analysis of Change) - it's an IBM Services platform for analyzing changes in IT operations that relies on the use of IBM Watson cognitive services to increase the relevance of predictions. The COACH intelligent core is based on the analysis and understanding of natural language texts and the use of complex machine learning models to calculate the risk associated with a request for a change in the IT field. Based on an analysis of historical incident data, COACH allows you to discover characteristic relationships, assess the similarities between new and historical change records, associate changes and incidents with configuration items, and assess the likelihood that a proposed change is a failure indicator. In his report, George will touch upon questions of predictive analytics regarding the risk of changes and talk about a new approach based on the analysis of historical information about server failures, incidents caused by changes, taking into account information received from experts.

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

11:20 12:10

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

Fraud types and their prevention in mobile ad market

I will talk about what types of fraud exist in the mobile advertising market. And also tell you how we identify fake traffic with the help of machine learning. I will show the real numbers of budget savings for our clients. And as a bonus, I will describe the complexity of everyday data science work.

I will talk about what types of fraud exist in the mobile advertising market. And also tell you how we identify fake traffic with the help of machine learning. I will show the real numbers of budget savings for our clients. And as a bonus, I will describe the complexity of everyday data science work.

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

12:10 12:30

Coffee Break

12:30 13:20

News Analytics Using Tweets Streams

Various processes in the society and business are mapped in social networks. In the speech, different approaches for intellectual analytics of tweets streams will be discussed. The speaker will speak about possible correlation between public opinion of twitter users and the decision-making in the society. Quantitative characteristics of frequent sets and association rules in the tweets related to different events will be considered. It will be discussed that the machine learning approach allows one to find complicated patterns in tweets, it makes it possible to detect fake and manipulative news. Case studies for tweets analytics will be considered.

Various processes in the society and business are mapped in social networks. In the speech, different approaches for intellectual analytics of tweets streams will be discussed. The speaker will speak about possible correlation between public opinion of twitter users and the decision-making in the society. Quantitative characteristics of frequent sets and association rules in the tweets related to different events will be considered. It will be discussed that the machine learning approach allows one to find complicated patterns in tweets, it makes it possible to detect fake and manipulative news. Case studies for tweets analytics will be considered.

Bohdan Pavlyshenko

Data Scientist (Ph.D.) at SoftServe

Bohdan combines academic theories and practical approaches in the data science area. His current scientific interest lies in the area of quantitative linguistics, machine learning, predictive analytics, computer vision, social network mining, business intelligence, time series analytics, numeric modeling, risk assessment, reliability theory, financial modeling. He has practical experience in retail and supply chain analytics, customer’s behavior analytics. In predictive analytics models, he combines machine learning and Bayesian inference that is an effective approach for forecasting and risk assessment in the business processes with non-Gaussian statistics. He works on the state-of-the-art predictive analytics solutions, taking part in Kaggle competitions where he has a Master degree and 3 gold medals for top positions in leaderboards. As a teammate, he won one Kaggle competition (“Grupo Bimbo Inventory Demand”) among nearly two thousand teams. more

Topological data analysis for discovering patterns in multidimensional data

Quite often, researching real data is challenging due to the complexity or inability to visualize it. Topological Data Analysis (TDA) is a powerful tool for dimensionality reduction, qualitative analysis and visualization of multidimensional data sets, which has been intensively developed since mid-2000. A distinctive feature of this approach is that it takes into account the geometry of the data to lower the dimension of their presentation. Given the internal geometry of the data sets, this methodology allows you to detect hidden patterns in the data without the need for an a priori hypothesis or the use of complex models. We represent TDA in combination with machine learning and statistical methods, which significantly increases its capabilities in the field of data analysis and visualization.

Quite often, researching real data is challenging due to the complexity or inability to visualize it. Topological Data Analysis (TDA) is a powerful tool for dimensionality reduction, qualitative analysis and visualization of multidimensional data sets, which has been intensively developed since mid-2000. A distinctive feature of this approach is that it takes into account the geometry of the data to lower the dimension of their presentation. Given the internal geometry of the data sets, this methodology allows you to detect hidden patterns in the data without the need for an a priori hypothesis or the use of complex models. We represent TDA in combination with machine learning and statistical methods, which significantly increases its capabilities in the field of data analysis and visualization.

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

13:25 14:15

Unsupervised Real-Time Stream-Based Novelty Detection Technique

A highly loaded cloud application environment requires high stability and uptime, and generates large streams of telemetry data. This updates the prerequisites for the development of a workload shift detector to prevent failures. The paper presents an approach to the detection of switching points based on the specific conditions of streaming data telemetry. Modeling the workload of the data center allows you to generate telemetry data for a specific workload, thus providing the opportunity to evaluate the performance of the detector in various conditions. The experiment showed the viability of the proposed approach, as well as directions for further study and improvement.

A highly loaded cloud application environment requires high stability and uptime, and generates large streams of telemetry data. This updates the prerequisites for the development of a workload shift detector to prevent failures. The paper presents an approach to the detection of switching points based on the specific conditions of streaming data telemetry. Modeling the workload of the data center allows you to generate telemetry data for a specific workload, thus providing the opportunity to evaluate the performance of the detector in various conditions. The experiment showed the viability of the proposed approach, as well as directions for further study and improvement.

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

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, NTU “KhPI”

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

14:20 15:20

Lunch

15:20 16:10

Searching for the best path in a Hyperparameter Space

Hyperparameter optimization remains core problem in training of deep architectures and any implementation of AutoML pipeline. Despite many recent advances most of the approaches intrinsically linked to the sampling of hyperparameter space or greedy search. We show that at a negligible additional computational cost, results can be improved by sampling nonlocal paths instead of points in hyperparameter space. To this end, we interpret hyperparameters as controlling the level of correlated noise in training, which can be mapped to an effective temperature. We then perform training in the joint hyperparameter/model-parameter space with an optimal training protocol corresponding to the path in this space. We observe faster training and improved resistance to overfitting and show a systematic decrease in the absolute validation error, improving over benchmark results.

Hyperparameter optimization remains core problem in training of deep architectures and any implementation of AutoML pipeline. Despite many recent advances most of the approaches intrinsically linked to the sampling of hyperparameter space or greedy search. We show that at a negligible additional computational cost, results can be improved by sampling nonlocal paths instead of points in hyperparameter space. To this end, we interpret hyperparameters as controlling the level of correlated noise in training, which can be mapped to an effective temperature. We then perform training in the joint hyperparameter/model-parameter space with an optimal training protocol corresponding to the path in this space. We observe faster training and improved resistance to overfitting and show a systematic decrease in the absolute validation error, improving over benchmark results.

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

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

Dmitrienko Valery

Doctor of Technical Science, professor of Computers and Programming Department, National Technical University "Kharkiv Polytechnic Institute"

Direction of scientific researches: development of fundamental theory of optimal control and artificial neural networks more

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, NTU “KhPI”

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

Workshop "Image Understanding: Segmentation and Objects Counting"

Details.
The registration to the workshop will be provided later only for the participants, who bought a ticket for the conference.

Details.
The registration to the workshop will be provided later only for the participants, who bought a ticket for the conference.

Ksenia Demska

Machine Learning Engineer, SoftServe

more

Yuriy Pryyma

Data Scientist, SoftServe

more

Markiyan Kostiv

Data Science Lead, SoftServe

more

16:15 17:05

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

Pavlo Vyplavin received his PhD in 2011. He worked at IRE NASU, Ukraine and at University of Campinas, Brazil. Currently he is CTO at It-Jim, Kharkiv, Ukraine. He is a technical leader of company’s research in computer vision and image processing including both classical and ML/DL-based algorithms. Pavlo has extensive academic and commercial experience, more than 60 publications and has delivered talks at international level events. more

From Unstructured Data to a Knowledge Graph

The knowledge graph is a way information could be stored and efficiently retrieved. In this topic, we would describe a process of creating one of those, by extracting information from a large set of unstructured content. We would cover a process going through raw scans (segmentation and classification for a right content extraction) to concepts detection and dependencies building by performing decomposition of a text using morphology trees.

The knowledge graph is a way information could be stored and efficiently retrieved. In this topic, we would describe a process of creating one of those, by extracting information from a large set of unstructured content. We would cover a process going through raw scans (segmentation and classification for a right content extraction) to concepts detection and dependencies building by performing decomposition of a text using morphology trees.

Petro Ivaniuk

ML Engineer, SoftServe

more

Taras Hnot

Data Scientist, SoftServe

more

17:10 18:00

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

18:20 18:35

Prize drawings

18:35

Little party & networking

Register
Prices

Prices

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

Speakers

Machine Learning Engineer, SoftServe

Ksenia Demska

Machine Learning Engineer, SoftServe

Ksenia Demska

Doctor of Technical Science, professor of Computers and Programming Department, National Technical University "Kharkiv Polytechnic Institute"

Dmitrienko Valery

Doctor of Technical Science, professor of Computers and Programming Department, National Technical University "Kharkiv Polytechnic Institute"

Dmitrienko Valery

Direction of scientific researches: development of fundamental theory of optimal control and artificial neural networks

Data Science Lead, SoftServe

Markiyan Kostiv

Data Science Lead, SoftServe

Markiyan Kostiv

Data Scientist, SoftServe

Yuriy Pryyma

Data Scientist, SoftServe

Yuriy Pryyma

Data Scientist (Ph.D.) at SoftServe

Bohdan Pavlyshenko

Data Scientist (Ph.D.) at SoftServe

Bohdan Pavlyshenko

Bohdan combines academic theories and practical approaches in the data science area. His current scientific interest lies in the area of quantitative linguistics, machine learning, predictive analytics, computer vision, social network mining, business intelligence, time series analytics, numeric modeling, risk assessment, reliability theory, financial modeling. He has practical experience in retail and supply chain analytics, customer’s behavior analytics. In predictive analytics models, he combines machine learning and Bayesian inference that is an effective approach for forecasting and risk assessment in the business processes with non-Gaussian statistics. He works on the state-of-the-art predictive analytics solutions, taking part in Kaggle competitions where he has a Master degree and 3 gold medals for top positions in leaderboards. As a teammate, he won one Kaggle competition (“Grupo Bimbo Inventory Demand”) among nearly two thousand teams.

Data Scientist, SoftServe

Taras Hnot

Data Scientist, SoftServe

Taras Hnot

ML Engineer, SoftServe

Petro Ivaniuk

ML Engineer, SoftServe

Petro Ivaniuk

CTO, It-Jim

Pavlo Vyplavin

CTO, It-Jim

Pavlo Vyplavin

Pavlo Vyplavin received his PhD in 2011. He worked at IRE NASU, Ukraine and at University of Campinas, Brazil. Currently he is CTO at It-Jim, Kharkiv, Ukraine. He is a technical leader of company’s research in computer vision and image processing including both classical and ML/DL-based algorithms. Pavlo has extensive academic and commercial experience, more than 60 publications and has delivered talks at international level events.

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, NTU “KhPI”

Povorozniuk Anatoliy

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

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, NTU “KhPI”

Mezentsev Nikolay

PhD, professor of Computers and Programming Department, NTU “KhPI”

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

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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