Mikko Happonen

Biomedical engineer, MSc (Tech), focusing on data analysis and sensor technology. AI algorithms, models, tools. ML in computational drug discovery (CI ≥ 0.83), DL in biosignals (AUROC ≥ 0.84).

Master’s Thesis Worker at Health Technology Group, University of Turku (2025-01 – 2025-04)

• Computational Drug Discovery | Built an FM model and pipeline (AI) in Python for pairwise modelling of drug–target data as a part of research project. Achieved CI ≥ 0.83 across 7 datasets.

Research Assistant at Health Technology Group, University of Turku (2024-06 – 2024-12)

• Biosignal Classification and Compression | Built CNN and XGB models (ML) in Python for PCG classification and compression analysis. Preserved AUROC ≥ 0.84 despite reduced data precision.
• Biosignal Segmentation | Built a U-Net model (DL) in Python for ECG and PPG segmentation using synthetic data and HPC evaluation. Demonstrated generalisation to real-world signals.
• Biosignal Processing Toolkit | Developed a reusable Python toolkit for biosignal research work.
• Healthcare Database System | Developed a food diary management system in Python for TYKS.

MSc (Tech) in Biomedical Engineering

• Major in Neurotechnology
• Minor in Psychology
• Thesis on Side Information in Drug–Target Interaction Prediction – University of Turku (2024-08 – 2025-07)

BSc (Tech) in Biomedical Engineering

• Major in Health Technology
• Minors in Mathematics and Physics
• Thesis on Convolutional Neural Networks in Magnetic Resonance Imaging of Brain Tumours – University of Turku (2021-08 – 2024-05)