Research

I am interested in Statistical Learning, Big Data and Machine Learning theory and applications. Developing successful and efficient algorithms for handling data related problems from various domains. I am currently focusing on representative pattern search and classification on time-series using mainly tree-based ensembles.

In addition to that I am involved in developing heuristic methods for multi-level optimization problems and participated in system dynamics studies.

Institutions

Institutions that I worked or studied in.

Ongoing Projects

I am currently working in 4 projects in addition to my M.Sc Thesis.

2016

Ensemble Based Fast Shapelet Approximation for Time Series Classification

A unique approach that aims to discover powerful representative patterns that can be used to classify time-series datasets in a computationally efficient way. And classification of the time-series using the information provided by these representative patterns.

2017

An Adaptive Large Neighborhood Heuristic for Multi-level Location Assignment and Picker Routing Problem

Application of ALNS algorithm in accordance with Tabu search algorithm to the multi-level storage location and picker routing problem. The aim of the algorithm is optimizing the placements of the items in the warehouse by minimizing the total distance travelled by all of the order pickers.

2017

Representative Sequence Generation for Time Series Classification using a Multivariate Approach

Very fast representative pattern generation using a tree-based multivariate technique.

Berk GÖRGÜLÜ

gorgulub [at] mcmaster [dot] ca