Dimitri Percia David

By employing data science and machine learning, my goal is to provide technology-forecasting models and market-monitoring tools for cyber-defence. Starting from a socio-technical perspective of information systems, I apply network analytics, statistical learning models (machine learning) and microeconomics to the field of cyber-defence. Under such an approach, I aim to provide a data-driven dashboard to monitor how technologies: (i) emerge, (ii) attract broader attention (i.e., hype), (iii) develop and mature incrementally or disruptively, and (iv) become progressively investable, in particular from the perspective of cyber-defence. The above four points involve: 1. Analysing the production-capability networks, the innovation structures and dynamics underlying the technology lifecycle (complex-systems analysis of cascading processes, network analysis of collective intelligence, deep learning); 2. Modelling portfolios of emerging and/or disruptive technologies related to cyber-defence (real-options analysis, TRL analysis); 3. Applying real-option models to capture the net-present value of uncertain innovation portfolios for cyber-defence (financial viability of investments).

I earned my PhD in Information Systems from HEC Lausanne in early 2020, a work advised by PD Dr. Marcus Matthias Keupp (ETH Zurich) and conducted under the supervision Prof. Dr. Kévin Huguenin (Unil).

Prior to that, I worked in the commodities trading industry (Alvean, Cargill, and S&P Global Platts) as a data analyst and econometrician.

ORCID profile: https://orcid.org/0000-0002-9393-1490