Alexandar Mechev

Alexandar Mechev has a diverse range of work experience, starting with their current role as a Data Engineer at Yieldify. Prior to that, they worked as a Data Engineer at Exact from November 2019 to August 2021.

Before transitioning into the field of data engineering, Alexandar pursued a PhD in Astronomy and Software Engineering at Leiden University from November 2015 to November 2019. During this time, they focused on High Performance Computing and parallelization in radio astronomy, specifically streamlining pipelines and conducting direction-dependent ionospheric calibration. Alexandar also developed software to process a large amount of data for the LOFAR radio telescope and integrated Workflow orchestration software with underlying shared infrastructure.

Alexandar's journey began with their role as a LEAPS Summer Student at Leiden University in June to August 2015. Here, they designed frame transfer smear correction algorithms for CCD, building a forward model and an inverse model to recover input scenes.

Prior to this, Alexandar worked at IMEC in July to August 2014 as an Integrated Optical Microscope Designer. Alexandar was responsible for building a LabView interface for a Ximea camera, controlling it effectively through reverse-engineering Ximea API hooks with LabView. Alexandar also developed an Arduino program to control two LED light sources.

In May to August 2012, Alexandar worked as a Telescope Instrumentation Researcher at Dunlap Institute for Astronomy and Astrophysics. Alexandar was involved in building, debugging, documenting, and supplementing the data reduction pipeline for the Gemini Planet Imager instrument. Alexandar also created test modules for calibration, documented modules, and commented code for user comprehension.

From June to August 2011, Alexandar worked on beam loss detector simulations at CERN as part of the Compact Linear Collider study. Alexandar used FLUKA simulations to test a Cerenkov fiber beam loss management system, comparing simulation results to theory and data and testing capture efficiencies.

In April to June 2011, Alexandar worked as a Radiation Shielding Designer at Institutet för Rymdfysik. Alexandar tested different spacecraft shielding scenarios for a Jupiter orbiter using GRAS, a GEANT4 application, and ran batch simulations on a computing cluster.

As a Research Assistant at Sudbury Neutrino Observatory from September to December 2010, Alexandar characterized the efficiency of purification for Linear Alkyl Benzene scintillator for the SNO+ upgrade. Alexandar performed infrared spectroscopy on samples to determine contaminant levels and gained experience in a class 100 clean room.

Finally, from September 2009 to April 2010, Alexandar worked as an Assistant with HPGe Detector Testing at TRIUMF. Alexandar characterized the response, crosstalk, accuracy, and preamp rise times for segmented HPGe detectors on TIGRESS and modified DAQ code to account for malfunctioning channels. Alexandar also created reports on detector performance, providing recommendations for passing, failing, or tuning amplifiers.

Alexandar Mechev completed their education in a chronological order. Alexandar started their education journey in 2007 at the University of Waterloo, where they pursued a Bachelor of Science (BSc) degree in Physics. Alexandar attended the University of Waterloo until 2012.

After completing their undergraduate degree, Alexandar went on to pursue a Master of Science (MSc) degree in Astronomy and Astrophysics from KU Leuven. Alexandar attended KU Leuven from 2013 to 2015.

Following their master's degree, Alexandar enrolled at Leiden University in 2015 to pursue a Doctor of Philosophy (PhD) degree in Astronomy. Alexandar completed their PhD in 2019 at Leiden University.

In addition to their formal education, Alexandar has obtained several certifications. In 2017, they received the "Advanced Linear Models for Data Science 1: Least Squares" certificate from Coursera Course Certificates. In 2017, Alexandar completed various Coursera courses including "How to Write and Publish a Scientific Paper (Project-Centered Course)," "Neural Networks and Deep Learning," "Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization," "Bayesian Statistics: From Concept to Data Analysis," "Structuring Machine Learning Projects," and "Convolutional Neural Networks."

In 2018, Alexandar completed the "Deep Learning Specialization" from Coursera. In 2019, they obtained certifications in "Object-Oriented Design" and "Introduction to Data Science in Python" from Coursera. In 2019, they also completed certifications in "Design Patterns," "Applied Plotting, Charting & Data Representation in Python," "Service-Oriented Architecture," and "Applied Machine Learning in Python" from Coursera.

In 2020, Alexandar obtained certifications in "Applied Data Science with Python," "Applied Social Network Analysis in Python," and "Applied Text Mining in Python" from the University of Michigan.

Most recently, in 2021, Alexandar obtained the certifications of "Microsoft Certified: Azure Fundamentals" from Microsoft and "Databricks Certified Associate Developer for Apache Spark 3.0" from Databricks.