Katya Vladislavleva


I am a data scientist living in Belgium and changing the edge of what's possible in predictive analytics


I am on a mission to help the world adopt advanced analytics and take optimal data-driven decisions. This poses two interesting challenges. First, advanced analytics needs to be made more accessible, and easy to use (for non-analysts), providing clear actionable results. Second, the world needs to be helped to start thinking, getting hunches, testing them, and getting insights.

My efforts go to the development of new algorithms and interactive workflows to solve problems using data and science.

I am managing director at Evolved Analytics Europe and chief data scientist and partner at Evolved Analytics LLC. Passionate about science and technology, I also teach graduate courses and give lectures on data science and data-driven problem solving.

I have a Ph.D. in data-driven problem solving from Tilburg University, Netherlands, Professional Doctorate in Engineering from Eindhoven University of Technology (Netherlands), and M.Sc. in intelligent systems from Lomonosov Moscow State University (Russia).

I love solving hard problems, working with great people, looking for simplicity in complex things, and making the complicated look simple.

I like people who have passion for what they do. I see challenges as opportunities, try to make the world better, and enjoy making my dreams come true.

Life Stats

thousands predictive models generated this week
smiles induced today
high-priority items on the To Do list

Eugène Ionesco

"It is not the answer that enlightens, but the question."


My research is in the area of predictive modeling and data science with a focus on data-driven problem solving. It can be split into the following categories (not mutually exclusive): (1) uncovering relationships in given data, (2) selecting data features that matter, (3) data acquisition, curation, modeling, and interpretation, (4) data balancing, (5) symbolic regression for system identification, (6) system modeling and optimization. Lately, I've been trying to reinvent myself by diving into the field of interactive visualization.

The list of notable scientific publications can be found on Google scholar.

For the most recent information about the projects and data-driven solutions, please, visit evolved-analytics.be. Below I mention a few published examples, which facilitated generation of new technology.

Optimizing a Cloud contract portfolio using Genetic Programming-based Load Models (non-linear time series prediction with large time windows)

Collaborators: Sean Stijven, Ruben Van den Bossche, Kurt Vanmechelen, Jan Broeckhove (University of Antwerp, Antwerp, Belgium) an Mark Kotanchek (Evolved Analytics LLC) (2013). See our book chapter.

Empirical analysis and feature selection in individual-based simulation for the spread of infectious diseases: prevention strategies, risk reduction, and cost optimization

Collaborators: Lander Willem, Sean Stijven, Niel Hens, Jan Broeckhove, and Phillip Beutels, University of Antwerp, Antwerp, Belgium (September 2010 - April 2014). See our articleq

Comparing ensemble-based forecasting methods for smart-metering data

Collaborators: Oliver Flasch, Martina Friese, Thomas Bartz-Beielstein, Olaf Mersmann, Boris Naujoks, Jörg Stork, Martin Zaefferer (Cologne University of Applied Sciences, Germany), 2013

Predicting video quality perception & Construction of new quality metrics

Collaborators: Nicholas Staelens, Dirk Deschrijver, Tom Dhaene (Ghent University & iMinds, Belgium)

Predicting wind energy output of wind farms: Important variables and their correlations

Collaborators: Tobias Friedrich (Friedrich-Schiller-Universität Jena, Germany), Frank Neumann & Markus Wagner (University of Adelaide, Australia), 2013

Learning from Sensory Evaluation Data: Important ingredients, panel segmentation & optimal formulations

Collaborators: Una-May O’Reilly & Kalyan Veeramachaneni (Massachusetts Institute of Technology, USA), 2009-2010

Academic Projects

In 2008-2012 I supervised several very interesting graduate projects. In all but the first one in the selected list below I was the promotor.

Connected House: Preprocessing and Pattern Exploration of User Behavior w.r.t. Hot Water Usage

Master student: Chinnappa Subramoniam Narendhran

Co-supervisors: Olaf Meersmann, Thomas Bartz-Beielstein (Cologne University of Applied Sciences, Germany)

Empowering Knowledge Computing with Variable Selection: regression random forests vs. symbolic regression

Master thesis of Wouter Minnebo and Sean Styven (University of Antwerp, Belgium)

Brute-force model space exploration for linear regression and variable selection on GPUs

Bachelor thesis of Joachim van der Herten (University of Antwerp, Belgium)

Dynamic Link Library for Computing a Variety of Statistical Moments for Modern Portfolio Theory

Bachelor thesis of Pieter Kerstens (University of Antwerp, Belgium)

Co-promotor and Problem owner: Ignace Van de Woestyne (IÉSEG School of Management, University College Brussels, Belgium)

GPU-based balancing of given multi-dimensional input-response data

Bachelor thesis of Sean Styven (University of Antwerp, Belgium)

Enhancement and Efficient implementation of Robust Multi-Objective Optimization using Swarm Intelligence

Bachelor thesis of Wouter Minnebo (University of Antwerp, Belgium)


I have several (beautiful) graduate courses prepared and tested. For inquiries and booking, please, send me an email. For tutorials and talks, please, check the website of our company


Feature Selection For Regression

This one-day training is on feature selection and feature importance in data-driven modeling for hard regression problems. Variable selection is a process of identifying influential variables (features, attributes) in a real or simulated system, that are discriminative and necessary to describe the system's performance characteristics.

Focusing the research (and modeling) on relevant variables reduces the dimensionality of the original problem (by making the problem tractable), shortens time to market (by facilitating insights), improves generalization (by generating robust knowledge), and heavily cuts down the costs for development and deployment of data-driven solutions.

Our aim is to provide a critical and objective analysis of the feature selection problem for regression, with complicating factors of having noisy, imbalanced data, correlated and coupled variables, and possibly many redundant variables. This is a hands on course. All methods will be illustrated on toy and real-world examples. If you feel like you have an interesting challenging feature selection problem to be used as an example during the course - please contact katya@evolved-analytics.com (at least one week before the course).

SINCE 2010

Function Discovery with Symbolic Regression

On Demand as a full-day training

Symbolic Regression is a field of supervised learning by evolutionary algorithms, aimed at modeling given numeric input-response data. Unlike classical regression, which assumes a certain model structure and optimizes the parameters, symbolic regression searches for appropriate model structure and coefficients. Symbolic regression models are defined in a space of all possible explicit expressions of the response variable, given analytically, as functions of some of the input variables, constants and operators from a given set.

This one-day introductory course in Symbolic Regression presents symbolic regression as powerful methodology for industrial data analysis and data-driven modeling, and covers the state-of-the art strategies for efficient generation of plausible regression models, which are designed to optimize competing trade-offs of high accuracy, low complexity, improved generalization capabilities and trustworthiness.

The following topics of symbolic regression (also applicable to other iterative search methods) will be covered:

  • Model Representations;
  • Model Selection and Complexity control;
  • Strategies for fitness evaluation (including racing and goal softening);
  • Model-based feature selection and dimensionality reduction;
  • Model-based outlier detection;
  • Model ensembles and active design-of-experiments.
For more information, or on-site training contact us.
This course was a part of 2008-2009 Lecture Series of the Belgian Doctorate School on Computational Intelligence and Learning

SINCE 2009

Optimization Methods

On Demand as five-day, or a very intensive three-day course

The main objective of the course is to give participants a comprehensive overview of the variety of optimization methods, both classical and theoretically tractable methods of local (and some cases of global) unconstrained and constrained optimization with one objective, and "modern" heuristics for global optimization like stochastic search methods, including simulated annealing, and evolutionary algorithms, which are applicable to optimization of one or more objectives.

Participants are expected to be able to compare various optimization methods with each other, know their strong and weak points; give advice on appropriate optimization methods for a given problem, be able to find solutions for given problems using own or existing implementations, and elaborate on the quality of obtained solutions.

This course is an up to date, easily accessible introduction to various optimization problems and techniques. Basic techniques for local and global optimization are treated, single-objective and multi-objective optimization, and techniques for solving discrete optimization problems (such as stochastic iterative search methods). The course content can be extended to dynamic optimization if there is sufficient interest among participants.

Requirements: Participants should possess a good spacial awareness and a good knowledge of basic concepts of calculus and differential geometry (fluency in differentiation, integration, limits, sequences, and vectors, as well as manipulations with multi-dimensional surfaces will be assumed). A strong interest in computational intelligence and programming is encouraged. If the need be - basic knowledge of calculus can be given in a additional half-day training.

This course was taught at the post-graduate program in Computer Science of Antwerp University, Belgium in 2008-2011. Parts of it were given as invited lectures in Eindhoven University of Technology in 2010.

SINCE 2008

Numerical Methods in Applied Linear Algebra

On Demand as a trimester course

At the end of the course participants are expected to grasp the essential approached for working effectively with vectors and matrices, know how to effectively solve several types of systems of equations, understand the taxonomy of studied algorithms, situations when these algorithms are effective and ineffective, elaborate on the conditioning of offered problems and on the stability of algorithms available to solve these problems.

The course covers the following topics of applied linear algebra:

  1. Vector and Matrix norms
  2. Singular Value Decomposition of a matrix. Principle Component analysis as an important practical application.
  3. Conditioning and condition numbers.
  4. Floating-point arithmetic.
  5. Stability of an algorithm. Stability of the floating-point arithmetic.
  6. Systems of linear equations. Gaussian elimination without and with pivoting.
  7. Least-squares problems.
  8. QR factorization. Conditioning and Stability.

Requirements: The course assumes a good knowledge of basic calculus and linear algebra.

This course was taught at the graduate program in Computer Science (as Numerical Linear Algebra, and elements of Numerical Methods) of Antwerp University, Belgium in 2008-2011.

Samuel Karlin

"The purpose of models is not to fit the data but to sharpen the questions."


Email me

This is the preferred address. For professional questions you can also use the contact form @ Evolved Analytics.

Dr. E. Vladislavleva
Evolved Analytics Europe
Antoine Coppenslaan 27, bus 11
2300 Turnhout
Community Website on Symbolic Regression
Evolved Analytics

I am re-designing the website for our companies. The current version is oriented at analysts and contains many jewels for learning about data-driven modeling. The must-see sources are tutorials, DataModeler illustrations, testimonials, and DataModeler update notes.

Evolved Analytics Europe
Evolved Analytics .BE

To ensure a smooth transition, the new website is located at the .be domain. Please, help us find broken links, or inconsistencies if any. Your suggestions are very welcome! I will also greatly appreciate testimonials and endorsements of our work and software. Thank you!

Community Website on Symbolic Regression
Symbolic Regression

I created the website on symbolic regression with genetic programming and collect there interesting toy and real-world test problems and benchmarks. Send me an email if you want to contribute the problem, the data and the story of your approach.