Michał Suchocki
About

I take the thing apart to see how it works

I have been since I was a child, occasionally to my parents' alarm.

The years between engineering school and running my own company were a long apprenticeship in how technology actually reaches the world. I started at Samsung, running field tests on the first Galaxy phones. I moved to Huawei as a product manager and helped launch one of the first commercial LTE networks. I learned consultancy at Cybercom, building up telecom, automotive and IoT practices, and then ran a region for them out of Dubai.

I co-founded and helped lead Cyberteq across the Middle East and Africa — work that, at one point, had me helping run cyber-awareness sessions for schoolchildren in Kigali. I did technology strategy at Accenture, consulting at Detecon, and I have spent several years as a senior information-security and generative-AI architect advising Philip Morris International. Telecom, automotive, healthcare, critical infrastructure, consumer products: I have seen how each of them breaks.

I still teach — at WAT, at AGH in Kraków, and at the SGH Warsaw School of Economics. I write and speak about this in public when I can be useful, because the gap between what practitioners know and what decision-makers assume is, itself, a vulnerability.

I work in Polish and English, from Warsaw.

Credentials
dr inż. — WAT, 2018ISO 42001 lead auditorContributing author, Forbes Poland17+ years in ICT and cybersecurity
Publications
Classification of auditory brainstem response using wavelet decomposition and SVM network
Biocybernetics and Biomedical Engineering 36(2), 2016, 427–436 · DOI 10.1016/j.bbe.2016.01.003
Klasyfikacja słuchowych potencjałów wywołanych w oparciu o dekompozycję falkową i sieć SVM
Biuletyn WAT LXIV(4), 2015, 117–129
Computer analysis of auditory brainstem evoked potentials
IEEE SPA, 2011, 132–137
Doctoral dissertation — classification of auditory brainstem evoked potentials based on wavelet decomposition and the support vector machine
WAT, 2018 · clinically validated, 97% accuracy