Thesis supervisor: Roland Molontay
Location of studies (in Hungarian): Human and Social Data Science Lab, Department of Stochastics, Institute of Mathematics, BME Abbreviation of location of studies: BME
Description of the research topic:
Interpretable machine learning models enable the identification of unusual patterns and outliers in data, providing insights into the underlying reasons behind anomalies. By enhancing the explainability of machine learning results, these models facilitate a deeper understanding of complex data patterns, thus improving the accuracy of anomaly detection systems. In addition, network science tools play a crucial role in anomaly detection by uncovering irregularities and deviations within intricate networks of interconnected data points.
The aim of the PhD program is to research and develop interpretable machine learning and network science methods that facilitate the easier detection of anomalies in both structured and unstructured data. The research can be diversified based on the applicant's interests and experience, examining log anomalies in industrial environments, anomalies related to cryptocurrencies, and anomalies associated with procurement procedures.
Requirements: An MSc degree in (applied) mathematics, physics, or computer science with a solid background in graph theory, algorithms, probability, statistics and machine learning. Strong programming skills (preferably in Python and/or R) are needed. Prior experience with complex networks and interpretable machine learning is an advantage.
Required language skills: English Number of students who can be accepted: 1
Deadline for application: 2024-05-31
2024. IV. 17. ODT ülés Az ODT következő ülésére 2024. június 14-én, pénteken 10.00 órakor kerül sor a Semmelweis Egyetem Szenátusi termében (Bp. Üllői út 26. I. emelet).
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