Dmytro Chumachenko is an Associate Professor at the Department of Mathematical Modelling and Artificial Intelligence of National Aerospace University “Kharkiv Aviation Institute” (Ukraine). Currently, he is a scholar at the Balsillie School of International Affairs.

Dmytro obtained a Bachelor of Science degree in “Applied Mathematics” and Master’s degrees in “Social Informatics” and “Project and Program Management” at the National Aerospace University “Kharkiv Aviation Institute”. Dmytro obtained PhD degree in Systems and Means of Artificial Intelligence at the Kharkiv National University of Radioelectronics and continued his research as a Post-doctoral Research Fellow at the Ubiquitous Health Technologies Lab of the University of Waterloo.

Currently, he has appointments as a Research Associate at the Massachusetts Institute of Technology (USA), a Research Affiliate at the University of Waterloo (Canada), a Visiting Researcher at the Max Planck Institute for Demographic Research (Germany) and an Invited Lecturer at the Wildau Technical University of Applied Sciences (Germany).

Dmytro is Chair of the Education and Science subcommittee of the Expert Committee on Artificial Intelligence Development under the Ministry of Digital Transformation of Ukraine and Chair of the Board of the Ukrainian Scientific and Educational IT Society. He also contributes to increasing the quality of IT higher education as a member of the Education Committee of the Kharkiv IT Cluster, an expert of the Ministry of Education and Science of Ukraine, and an expert of the National Agency for Higher Education Quality Assurance (Ukraine).

He is an Editor in Radioelectronics and Computer Systems journals and an Associate Editor in Machine Learning and Artificial Intelligence, Frontiers of Big Data, and Frontiers of Artificial Intelligence.

His research focuses on simulating the spread of infectious diseases and applying AI tools and methods for public health and strategic communications. During his appointment as Balsillie Scholar, his research addressed the investigation of data used for epidemic simulation from both technical and policy perspectives.