Zoltan is a Techno-Wizard and commands his machines to do his bidding. He considers himself a student of life and continually aims at deepening his understanding of the devices that run our world. He is a passionate user of open-source technology and believes that it empowers its users to take control of their digital experience. Zoltan runs Linux on his laptop, computing machine, media servers, Synapse (matrix) server, and home server which he uses to self host this website, and a crypto news scrapper. He is currently building a crypto sentiment analysis visualization tool that he plans to self host.

Zoltan is a candidate for a Master's of Computational Neuroscience at the Bernstein Center for Computational Neuroscience (BCCN) in Berlin where he hopes to expand his knowledge and skills to better serve the health sector through conducting research in neuroscience.


Zoltan has completed a Bachelor's of Applied Science Honour's Civil Engineering/International Studies in Engineering Option degree (With Distinction, Dean's Honours List) from the University of Waterloo, and ranked first in his class. During his undergraduate studies he conducted research at the University of Waterloo and in Zurich at ETH, which led to his first publication (link).


From May of 2018 to May of 2019, Zoltan conducted research with Toronto's University Health Network's Guided Therapeutics Lab (GTx) where he demonstrated his ability to work diligently by creating gesture recognition software in Python with the aim of developing a non-contact human computer interface, saving surgeons time by allowing them to review scans without having to unscrub. He was responsible for the design, development and application of a pipeline that incorporated machine learning models for collected training, validation, and test sets for different hand gestures. Random Forests, Multi-layer Perceptrons, Support Vector Machines and other algorithms were implemented in an ensemble model to improve the accuracy (to 99.5%) and generalization of the previously existing hard coded gesture recognition software.

His most recent research assignment was with Charité — Universitätsmedizin Berlin for the Psychology and Psychiatry department. At the Ritter Lab, Zoltan performed analysis of psychiatric data using Random Forests and Feature Importance (Gini importance and SHAP values); enabling health care professionals to predict the efficacy of deep brain stimulation treatment, used to treat patients with severe Parkinson's disease, and providing them with metrics to consider the contribution of input variables to each prediction, improving model interpretability. He taught an intro to Machine Learning course offered to Charité staff as well as conducted research into the effects of noise on classification of MRIs.


Alongside his academic career, Zoltan has worked as a Full-Stack Developer, which has given him the tools to increase his overall tech literacy and directly supports his transition into Digital Wizardry. Zoltan is now actively taking on clients. See (portfolio)