teaching
Courses
University of Tartu
Master’s curriculum in Computer Science
- Machine Learning, MTAT.03.227 — teaching assistant (practice sessions, homework and exam grading)
- Special Course in Machine Learning, MTAT.03.317 — instructor (seminar planning, moderation, grading)
- Deep Generative Models (Spring 2019/2020)
- Competitive Data Science (Fall 2020/2021)
Cleveron Academy / EEK Mainor (Lecturer)
Bachelor’s curriculum in Robotics
- Machine vision and signal processing, RT-034
- Machine Learning, RT-035
- Artificial Neural Networks, RT-033
Supervision
Master theses
2022-2023
(to be defended in June 2023)
- Pavel Chizhov, Self-Supervised Image Denoising Using Transformers
- Denys Kaliuzhnyi, Cell Detection in Histopathology: Exploring the Effect of Incomplete Annotations
2021-2022
- Joonas Ariva, Fast Fourier Convolutions in Self-Supervised Neural Networks for Image Denoising (A grade)
2020-2021
- Dmytro Urukov, Improving Microscopy Image Segmentation with Object Detection (A grade)
Practical training
(not including students who continued to work on a master’s thesis)
- Tarun Khajuria, Robust Microscopy Image Segmentation at Varied Magnifications
- Farid Hasanov, Cell Detection in Sparsely Annotated Histopathology Images
Course projects
Projects for Neural Networks and Transformers courses in UT.
- Dmytro Urukov, Real-time Cell Counting in Microscopy Images Using Neural Networks
- Arnel Pällo, Kaisa Saarkoppel, Visual Transformers for Image Denoising
- Mateus Surrage Reis, Braian Olmiro Dias, Transformer Comparison for Cell Semantic Segmentation from Brightfield Microscopy
- Kaarel Roopärg, Cell Detection and Classification in Testis Histopathology Images (code)
- Pavel Chizhov, Denys Kaliuzhnyi, Denys Krupovych, Self-Supervised Cell Discovery in Histopathology Images Using Vision Transformer
- Harry-Anton Talvik, Large-scale Comparative Study of Neural Network Architectures for Medical Image Segmentation