Data science, network science, educational data science, social networks
Affiliation
Budapest University of Technology and Economics, Faculty of Natural Sciences, Institute of Mathematics, Department of Stochastics
Bio
Roland Molontay is an applied mathematician and data scientist specializing in network science, machine learning, and data-driven analysis of complex social and biological systems. He obtained his PhD in Applied Mathematics from the Budapest University of Technology and Economics (BME), graduating summa cum laude in structural network analysis. His doctoral training included international research at Brown University and interdisciplinary studies in quantitative economics.
Following his PhD, he pursued an academic career at BME, progressing from assistant lecturer to associate professor in data science. His international experience includes visiting positions in the United States (Indiana University Bloomington) and collaborations across Europe and Asia.
In addition to research and teaching, he holds significant academic leadership roles. He is the founder and head of the Human and Social Data Science Lab (HSDSLab) at BME, he serves as Deputy Director of the Institute of Mathematics at BME and Director of the Institute of Biostatistics and Network Science at Semmelweis University. In these roles, he coordinates research strategy, fosters interdisciplinary collaboration, and supports institutional development in data science and network medicine. His work is strongly connected to industry and public-sector collaboration, including joint research projects with Nokia Bell Labs and national educational data science initiatives.
His research focuses on the intersection of network science, explainable artificial intelligence, and data-driven decision-making. Over the past five years, his contributions include:
Explainable machine learning models in healthcare, education, and energy systems
Educational data science and learning analytics, including predictive and interpretable models for dropout and admissions
Theoretical and applied network science, including structural analysis, anomaly detection, and social media dynamics
AI-supported biomedical prediction models
He has authored 52 peer-reviewed publications, many in top-ranked (Q1/D1) international journals, with over 1,000 independent citations. A defining feature of his work is strong collaboration with students and mentees, often serving as senior author, reflecting his commitment to scientific mentorship and capacity building.
His work has received extensive recognition, including the Bolyai János Research Scholarship (2025–2027), PD OTKA fellowship (2022–2025), Academy Youth Award (2026), MTA Publication Award of Excellence (2023), Bárány Róbert Award (2022), and Farkas Gyula Memorial Award (2020). He has also received multiple teaching and supervision awards, as well as a Best Paper Award (2025) at an international educational technology symposium. He is an elected member of the Young Academy of the European Mathematical Society and serves on the European Regional Committee of the Bernoulli Society.
Beyond research, he actively contributes to the scientific community by organizing international events such as the Biomedical Data Science Summer School and Conference, and participating in editorial and review activities. He has played a key role in developing data science curricula at BME and is actively engaged in outreach and science communication, strengthening the connection between research, education, and societal impact.
