Human and Social Data Science Lab
Research keywords:
· machine learning
· anomaly detection
· data science
· network science
· human-centered data science
· social data science
· social networks
· educational data science
· health data analytics
· intelligent monitoring
Horizon Europe keywords:
· Artificial Intelligence and Robotics
· Next generation computing and data technologies
· Inclusive society
· Transformation of industry
Introduction of the Research Group
HSDSLab is a group of researchers at the Budapest University of Technology and Economics led by Roland Molontay. We are based in the Institute of Mathematics but we also have strong ties with the Faculty of Economics and Social Sciences. We do more than data science. HSDSLab advocates the use of data and network science methods and data-driven solutions within social and behavioral sciences, human resources, and human health. The Lab's mission is to translate fundamental research in data and network science into lasting impact in social sciences and humanities by bringing together people from a range of different disciplines. In addition, HSDSLab aims at bridging the gap between academia and industry by increasing the collaboration between researchers, methodology experts, and corporate experts. HSDSLab conducts both methodology-oriented basic research in data and network science and applied research with a human-centered and societal focus. The lab members develop statistical and computational methods to study the empirical, theoretical, and technical dimensions of all forms of data in social, policy, and business contexts. The Lab's research includes predictive analytics in human health and education, user behavior analysis, data-driven assessment and evaluation in higher education, social network analysis, structural characterization of complex networks, explainable artificial intelligence in human-centered and social sciences, scientometrics. theoretical, and technical dimensions of all forms of data in social, policy, and business contexts. The Lab's research includes predictive analytics in human health and education, user behavior analysis, data-driven assessment and evaluation in higher education, social network analysis, structural characterization of complex networks, explainable artificial intelligence in human-centered and social sciences, scientometrics. theoretical, and technical dimensions of all forms of data in social, policy, and business contexts. The Lab's research includes predictive analytics in human health and education, user behavior analysis, data-driven assessment and evaluation in higher education, social network analysis, structural characterization of complex networks, explainable artificial intelligence in human-centered and social sciences, scientometrics.
Watch our 3-minute introductory video:
Achievements
- developing an award-winning anomaly detection method in collaboration with Nokia Bell Labs: https://www.itbusiness.hu/technology/aktualis_lapszam/kiadvanyok/ict-nagykonyv-2021/award/anomaliak-nyomaban- organizing a hybrid workshop about our research in educational data science: https://hsdslab.math.bme.hu/workshop.html
Publications
Nagy, M., & Molontay, R. (2022) Network Classification Based Structural Analysis of Real Networksand their Model-Generated Counterparts. Network Science, 1-23
https://www.cambridge.org/core/journals/network-science/article/abs/network-classificationbased-structural-analysis-of-real-networks-and-their-modelgenerated-counterparts/ 44C79234EAF40C5F02A340E15CD8F638
Berezvai, Z., Lukats, GD, & Molontay, R. (2021). Can professors buy better evaluation with
lenient grading? The effect of grade inflation on student evaluation of teaching. Assessment &
Evaluation in Higher Education, 46:5, 793-808
https://www.tandfonline.com/doi/abs/10.1080/02602938.2020.1821866
Kovács, P., Nagy, M., Molontay, R. (2021) Comparative Analysis of Box-Covering Algorithms for
Fractal Networks. Applied Network Science, 6(73)
https://appliednetsci.springeropen.com/articles/10.1007/s41109-021-00410-6
Nagy, M., & Molontay, R. (2021) Comprehensive Analysis of the Predictive Validity of University
Entrance Score in Hungary. Assessment & Evaluation in Higher Education, 46:8, 1235-1253
https://www.tandfonline.com/doi/abs/10.1080/02602938.2021.1871725
Horváth, G., Kovács, E., Molontay, R., & Nováczki , S. (2020). Copula-Based Anomaly Scoring of
High-Dimensional Data with Application in Telecommunication Networks. ACM Transactions on
Intelligent Systems and Technology (TIST), 11(3), 1-26.
https://dl.acm.org/doi/abs/10.1145/3372274
Awards
Roland Molontay Gyula Farkas Memorial Award (awarded by J ́anos Bolyai Mathematical Society) (2020) BME Innovation Award of the Pro Progressio Foundation (2020) Research scholarship of the New National Excellence Program (ÚNKP) (2019, 2021) Pro Progressio Foundation's Award for Outstanding Supervisors of Scientific Student Projects (TDK) (2019) Outstanding Lecturer Award of the Faculty of Natural Sciences at BME (2019) Marcell Nagy Gyula Kőnig Research Prize (2022) Fulbright Award (2021)Journals
Assessment & Evaluation in Higher Education, IEEE Transactions on Learning Technology, Interactive Learning TechnologyNetwork Science, Applied Network Science, Journal of Complex Networks, Scientific Reports
Projects
Artificial Intelligence National Laboratory, Thematic Excellence ProgramIndustry relations
NOKIA Bell Labs, SDA Informatics, eKréta Informatics, eNET - Internet Research and Consulting, Translational Medicine CenterConferences
Roland MolontayProgram committee member of Complex Networks (2020, 2021, 2022)
Lead organizer of HSDSLab Educational Data Science workshop
Roland Molontay and Marcell Nagy
Member of the local organizing committee Geometry of Deterministic and Random Fractals:
Honoring the 60+1st birthday of Professor Károly Simon