• 18th Workshop on Algorithms and Models for the Web Graph, 2023

• Social Simulation Conference, 2021

• Conference: Contemporary Methods and Practices in Credit Scoring, 2021

• 17th Workshop on Algorithms and Models for the Web Graph, 2020

• Workshop on Hypergraphs — Theory and Applications, 2020

• Summer School on Data Science Tools and Techniques in Modelling Complex Networks, 2019

• Blockchain Technology Symposium BTS2018

• Methods and Applications of Operations Research 2018 Conference

• International Conference on Group Decision & Negotiation 2015

• European Social Simulation Conference 2013

• Methods and applications of advanced data analysis (The Polish National Agency for Academic Exchange NAWA)

• New Economy Lab (The Polish National Agency for Academic Exchange NAWA)

• Raising Open and User-friendly Transparency-Enabling Technologies for Public Administrations (Horizon 2020)

• Methodology of construction of macroeconomic forecasts taking into account economic agent heterogenity (National Science Centre, Poland)

• Structure of the social network, willingness to cooperate, and individual earnings: simulation model and empirical results (National Science Centre, Poland)

A collection of interactive online projects using the Julia language: Hands-on Data Science with Julia.

• Data Preprocessing: concentrates on exploratory analysis and data preparation for modeling; the user during the project will learn how to work with data frames from DataFrames.jl package and will be shown how data can be visualized

• K-means and DBSCAN Clustering: begins with preparation of a data set for clustering using k-means and DBSCAN algorithms; for both methods the user will find optimal hyperparameter values and will validate the obtained results taking into account the comparison of grouping results from both algorithms

• Dimensionality Reduction with PCA, t-SNE and UMAP: continues the analysis of the data set from the previous project; data are reduced to two- and three- dimensions using the PCA, t-SNE, and UMAP algorithms; using this approach it is possible to visualize the data set along with results of clustering using k-means and DBSCAN algorithms; for t-SNE and UMAP algorithms the user will employ the PyCall.jl package which allows to call Python libraries

• Regression Using GLM and DecisionTree: is aimed at teaching how regression models can be created; the user performs the prediction of housing prices using linear regression and decision trees; in the project a process of hyperparameter tuning is peformed along with validation and comparison of created models using the GLM.jl and DecisionTree.jl packages

• Classification with XGBoost: shows the task of classification using the XGBoost.jl library; during the project the user has a possibility to train an XGBoost models along with chioce of hyperparameters and implementation of custom variable importance algorithm; the results of the algorithm are next compared with variable importance calculation procedure that is in-built into XGBoost package, which allows to get additional business insights

Here you can find information about a book on solving mathematical problems I have written with Paweł Prałat. Additionally a free companion material giving an introduction to the Julia language is available there for download as a PDF.

The Julia Express

EventSimulation.jl

localsolver package

An implementation of algorithms presented in the paper Refined knowledge-gradient policy for learning probabilities (Operations Research Letters, 43, 2015) can be downloaded here.

An implementation in Java language of the Asynchronous Knowledge Gradient algorithm can be downloaded here.

Documentation of the algorithm is presented in paper:

B. Kamiński, P. Szufel: Asynchronous Knowledge Gradient Policy for Ranking and Selection, Winter Simulation Conference, Proceedings of the 2014 Winter Simulation Conference, A. Tolk, S. Y. Diallo, I. O. Ryzhov, L. Yilmaz, S. Buckley, and J. A. Miller, eds., s. 3785-3796, 2014

WSE - Warsaw Simulation Engine

Together with Przemyslaw Szufel we take part in development of Project PyCX.

It is a simple simulation environment written in Python.