Hi! I am Maria and I am PhD candidate at the Data Science Group, at the Department of Computer and Systems Sciences, Stockholm University, exploring applications of AI in health. My research focuses on personalized medicine and patient phenotyping investigating the use of clustering and reinforcement learning to achieve that. Other research interests include agent based and system dynamics modelling with focus on epidemic surveillance. 

Phone: +08-16 49 09
E-mail: maria.bampa@dsv.su.se

Experience

Intern, Folkhälsomyndigheten (The Swedish Health Agency), September 2021 – Ongoing

Topic: Epidemic disease modelling, Reinforcement Learning for mitigation of pandemics and vaccine distribution

PhD Candidate, Department of Computer and System Sciences, Stockholm University, September 2019 – Ongoing

Topic: AI in health, patient phenotyping and personalized health, clustering, reinforcement learning

Research Assistant, Department of Computer and System Sciences, Stockholm University, April 2019 – August 2019

Topic: Learning from complex Electronic Health Records

Academic Experience

Teaching Assistant, Python for Data Mining in Computer and System Sciences, M. Sc. Course, Academic Years 2020-2022

Co-Supervisor of Master Theses,  Academic Years 2020-2022

Program Committee member at UAI, 2021-2022

Publications

Bampa, Maria, Panagiotis Papapetrou, and Jaakko Hollmén. “A Clustering Framework for Patient Phenotyping with Application to Adverse Drug Events.” 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 2020.

Bampa, Maria, and Panagiotis Papapetrou. “Mining Adverse Drug Events Using Multiple Feature Hierarchies and Patient History Windows.” 2019 International Conference on Data Mining Workshops (ICDMW). IEEE, 2019.

Bampa, Maria, and Panagiotis Papapetrou. “Aggregate-Eliminate-Predict: Detecting Adverse Drug Events from Heterogeneous Electronic Health Records.” arXiv preprint arXiv:1907.06058 (2019).