Etele Kovács
Machine Learning & Computer Vision Engineer
Machine-learning and computer-vision engineer, studying Data Science at the IT University of Copenhagen. I build ML systems end to end — from messy raw data to models, tools, and interfaces people actually use. Two of the research tools below are in daily use by the scientists they were built for.
Projects
DAPI Microscopy Biomass Pipeline
Mar 2026 – Aug 2026Research tool · Python · Cellpose · OpenCV · SLURM
- Turned DAPI fluorescence microscopy into cell counts and biovolume (µm³): fine-tuned Cellpose on the ITU HPC cluster (SLURM + Apptainer) and ported the reference Zeder biovolume algorithm from the field-standard tool's decompiled C#.
- Shipped as an offline desktop app with live microscope capture and CSV export — in daily use by researchers.
Pico-Algae Detection and Counting
Dec 2025 – Aug 2026Python · PyTorch · OpenCV · Computer Vision
- Built a 6-channel Faster R-CNN that detects and counts pico-algae in paired microscopy images; self-annotated 16,181 objects in CVAT and tuned post-processing to cut count MAE from 3.94 to 2.42.
- Shipped as an offline desktop app — installer, interactive review editor, live microscope capture — whose corrections export straight back into training data. In daily use by researchers.
NER on Code-Switched Spanish-English Tweets
Mar 2026 – May 2026Group project (ITU) · Python · PyTorch · Transformers · SLURM
- Four-person NLP course project with a paper: built the training/evaluation pipeline and a language-aware bias layer that lifted XLM-RoBERTa from span F1 0.636 to 0.649 on the LINCE benchmark (67K tweets), with SLURM cluster training.
Automobile Insurance Claims Risk Modeling
Oct 2025 – Dec 2025Group project (ITU) · Python · NumPy · scikit-learn · PyTorch
- Three-person BSc exam project on ~678K French motor policies: built a neural network from scratch in NumPy, its PyTorch reference, PCA, and the preprocessing/EDA pipeline; a Negative Binomial GLM won (test RMSE 3.03) — on noisy, zero-inflated data the right statistical model beat the complex ones.
Education
BSc, Data Science
IT University of Copenhagen — coursework in machine learning, applied statistics, algorithms, databases, and large-scale data analysis.
Skills
PythonPyTorchOpenCVFaster R-CNNCellposeHugging Face Transformersscikit-learnstatsmodelsPandasNumPyFastAPIFlaskReactCVATHPC (SLURM)ML pipelinesModel evaluation & validationData annotation & dataset design
Languages
English (C1)German (B2)Hungarian (Native)