Antti Airola
D.Sc. (Tech.)
ajairo@utu.fi +358 29 450 4193 +358 50 517 8711 Vesilinnantie 5 Turku : 456H |
artificial intelligence; data analytics; machine learning; health technology
Antti Airola, is an Associate Professor (tenure track) of data science in the area of Health Technology at the University of Turku. He has co-authored over 80 peer-reviewed articles, won multiple international data science competitions, and received several research excellence awards such as the the HATUTUS award for the best PhD thesis in the area of pattern recognition in Finland (2010 - 2011) and IEEE Computational Intelligence Society Outstanding Transactions on Fuzzy Systems Paper award (2015). He is currently working as responsible leader in several EU and Research Council of Finland funded research projects.
Airola's main research areas are in the area of machine learning and data science, especially their applications in the health domain.
Airola is currently responsible for teaching the course TKO_3103 Data Analysis and Knowledge Discovery, as well as thesis supervision work (BSc, MSc, PhD). He has developed materials for and taught in many courses in the area of data and computer science, is responsible for developing the curriculum for both national BSc and MSc Medical and health technology programmes, as well as the international MSc programme in Health technology, and directs the AI Academy that coordinates AI related teaching between the faculties.
- Continuous Radar-based Heart Rate Monitoring using Autocorrelation-based Algorithm in Intensive Care Unit (2025)
- IEEE Journal of Biomedical and Health Informatics
- Limited memory bundle DC algorithm for sparse pairwise kernel learning (2025)
- Journal of Global Optimization
- Response to Commentary by Dehaene et al. on Synthetic Discovery is not only a Problem of Differentially Private Synthetic Data (2025)
- Methods of Information in Medicine
- Stochastic limited memory bundle algorithm for clustering in big data (2025)
- Pattern Recognition
- Benchmarking Evaluation Protocols for Classifiers Trained on Differentially Private Synthetic Data (2024)
- IEEE Access
- Does Differentially Private Synthetic Data Lead to Synthetic Discoveries? (2024)
- Methods of Information in Medicine
- Empirical investigation of multi-source cross-validation in clinical ECG classification (2024)
- Computers in Biology and Medicine
- Evaluating Piezoelectric Ballistocardiography for Post-Surgical Heart Rate Monitoring (2024)
- Computing in Cardiology
- Finnish perspective on using synthetic health data to protect privacy: the PRIVASA project (2024)
- Applied Computing and Intelligence
- Predicting pairwise interaction affinities with ℓ0-penalized least squares-a nonsmooth bi-objective optimization based approach∗ (2024)
- Optimization Methods and Software
- Accelerating Image Processing Using Reduced Precision Calculation Convolution Engines (2023)
- Journal of Signal Processing Systems
- Budget-based classification of Parkinson's disease from resting state EEG (2023)
- IEEE Journal of Biomedical and Health Informatics
- Domain randomization using synthetic electrocardiograms for training neural networks (2023)
- Artificial Intelligence in Medicine
- Empirical evaluation of amplifying privacy by subsampling for GANs to create differentially private synthetic tabular data (2023)
- CEUR Workshop Proceedings
- Enhancing the Reliability of Wearable Cardiac Monitoring using Accelerometer Activity Data (2023)
- Body Sensor Networks Conference
- Evaluating Classifiers Trained on Differentially Private Synthetic Health Data (2023)
- Proceedings (IEEE International Symposium on Computer-Based Medical Systems)
- Cardiac Time Intervals Derived from Electrocardiography and Seismocardiography in Different Patient Groups (2022)
- Computing in Cardiology
- Generalized vec trick for fast learning of pairwise kernel models (2022)
- Machine Learning
- Higher Education during Lockdown: Literature Review and Implications on Technology Design (2022)
- Education Research International
- Machine Learning Approach for Preterm Birth Prediction Using Health Records: Systematic Review (2022)
- JMIR Medical Informatics
