Actively co-supervising and mentoring doctoral, master’s, and bachelor’s students on Machine learning and data science topics.

Co-supervised MS thesis completed:

1. “Hybrid LSTM-CNN Framework for Predictive Anomaly Detection in Operational Technology Networks”, Julia Slazak (2025).

2. “Power Consumption Prediction in Smart Manufacturing Using Machine Learning”, Ali Abdelsalam (2025).

3. “A Comparative Benchmark of Fairness Metrics in Machine Learning”, Md Asif Shahariar (2025).

4. ”A Comparative Study of Machine Learning Models for Anomaly Detection in Vessel Operational Data”, Ali, Usama (2024).

5. ”Anomaly Detection for Time Series Data from Internet of Things Devices”, Raza, Syed Shamir (2024).

6. ”Industrial Metaverse Dynamics: Metaverse Strategies for Material Flow Optimization in Industry Evolution”, Lignell, Anni (2024)

7. ”Prediction of Camera Calibration and Distortion Parameters Using Deep Learning and Synthetic Data”, Chaudhry, Faiz (2024). 

8. ”Forecasting Electric Vehicle Charge Point Availability using Machine Learning Models", Khan, Abid Nawaz (2024).

9. "Credit Card Fraud Detection using One-Class Classification Algorithms", Zaffar, Zaffar (2023).

Supervised Bachelor’s Projects completed:

1. “Forecasting Heart Rate Variability Using Wearable Sensor Data: Zero-Shot Evaluation of Foundational Time Series Models”, Luukas Peräkylä, (2025).

2. “Physiological Data Analysis for Stress Detection Utilizing 1D-CNNs”, Sami Vuolo, (2025).

3. “One-Class Classification for Gastrointestinal Endoscopy Images” Lauri Syrjynen, (2025).

4. “Machine Learning-Based Analysis of Electrocardiograms for Anomaly Detection” Tommi Salonen, (2025).