27-29 November, Vilnius
Conference about Big Data, High Load, Data Science, Machine Learning & AI
Early Bird Ends In:
Tetiana Hladkykh Biography
Tetiana is a Senior Data Scientist with strong computer science background. Her area of scientific interest includes Data mining, Artificial Intelligence (genetic algorithms, neural network, and fuzzy logic), Mathematical Statistic, Computer Vision, Graph Theory, Game Theory and Computational mathematics. In 2007 Tetiana completed her PhD in Technical Science at The National Technical University “Kharkiv Polytechnic Institute”, where she was working on “Verification of electronic devices dynamic parameters based on K-valued simulation”.
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, including her recent works on ANN for Adaptive Resonance Theory (K-Valued Adaptive Resonance Theory of Neural Networks for Analyzing Operability of Computing Devices, 2014) and Pattern Recognition (Anomaly Detection – Unsupervised Approach, 2016).
Additionally, Tatiana was a research advisor in more than 26 scientific projects and publications in Computer Vision, Multi-criterion optimization and Economic planning and forecasting.
Dmytro Zikrach Biography
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. degree in Mathematics. 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.
He is aspiring Data Scientist who actively develops skills pertaining to this area, a fast learner with good analytical skills who keeps on learning things. An avid reader who believes in conceptualized learning and is interested in mining information and insights. Dmytro has great experience and research in Statistical Learning, Predictive Analytics, NLP, Time Series Analysis, Artificial Intelligence and Recommender Systems.
Moreover, Dmytro often takes part in various Data Science community competitions, hackathons, and challenges.
Employee Dismissal Predictive Analytics
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 the Dismissal Prediction Analytics tool aimed to notify direct people managers about risks of their employee leaving. Our data-driven solution with state-of-art engine that uses machine learning approaches to help managers proactively manage unwanted dismissals. App analyzes internal historical data, builds prediction, and continuously improves its accuracy with every new data point. Analysis and predictions are done monthly, with predictions going 3 months out, which gives any manager plenty of time to do damage control.
Machine Learning for non-Data Scientist: Smart Decisions Game
The purpose of our workshop “Machine Learning for non-Data Scientist: Smart Decisions Game” is the presentation of a new, game-based, approach to the designing ML systems. During the workshop, participants will play a card game that allows modeling the decision-making process regarding the choice of an algorithm for solving a particular type of problem, and after that, they will be able to implement it in practice.