Pavel Baikalov

Built and evaluated ML models (LightGBM, XGBoost, Random Forest, PLSR) for predicting soil organic carbon from Vis-NIR spectral data, achieving field-level R² > 0.7. Developed end-to-end Python pipelines — spectral preprocessing to model training and reporting. Contributed to EU-funded PHENET project. Supervised 1 PhD student in machine learning and spectral analysis.

Built semantic segmentation models (U-Net, SegNet, PyTorch) for root phenotyping from RGB images: Dice 0.72 (homogeneous) and 0.68 (complex data), with R² 0.95–0.96 for root length prediction. Developed CV pipelines with preprocessing, augmentation, and false positive reduction (FPR 0.045). Mentored undergrad and master's students in Python and data analysis. Published 3 peer-reviewed papers.

Developed control software for the AC-21 Automatic Minirhizotron System (Python, PyQt5), including GUI for camera movement, timing, and positioning — deployed in a commercial scientific instrument. Programmed IoT hardware (Raspberry Pi, Arduino) for sensor data, camera control, and motor automation. Built image analysis tools for root research. Contributed to Future Arctic and ATTRACT "NextMR-IAA" projects.

Worked in Medical Imaging. Developed image preprocessing pipelines (normalization, binarization, noise removal) for immunofluorescent microscopy images of iPSC-derived neurons. Applied classical computer vision techniques (OpenCV, scikit-image) to prepare data for cell segmentation.

Conducted bioinformatics analysis of genetic sequences. Analyzed the relationship between insertion/deletion bias and point mutation bias in DNA sequence evolution. - Contributed to a protein conformation prediction model project called Torsional Network Model (TNM), focusing on analyzing its accuracy.

Master thesis: Semantic segmentation of peripheral blood leukocytes using neural networks http://hdl.handle.net/10609/90265

Studied General Biology with a strong emphasis on laboratory techniques and statistics. Completed courses such as: - Data Analysis - Biochemistry - Genetics - Immunology - Integrated Laboratory - Programming Tools in Biochemistry and Molecular Biology - Genomes Organization and Functi