About Alex
PhD in Applied Machine Learning/Computeer vision. Main field of application - asset integrity, material degradation.
I have worked with major infrastructure operators in Europe on integration of computer vision in the workflows.
Currently CTO of Mendelgate. I am doing custom computer vision models and tools for various asset integrity tasks.
What I can:
- Expertise in computer vision for quality control, corrosion detection, and materials analysis.
- Hands-on experience creating AI prototypes that work with real industrial data.
- Rapid delivery: working models, scripts, or interactive demos in 1–3 weeks.
- Guidance to scale prototypes into production-ready tools.
Typical projects / deliverables I handle:
- Detecting and grading corrosion, defects, or surface anomalies from images.
- Automated visual inspection pipelines for lab or production environments.
- AI-assisted analysis of microscopy or industrial imaging data.
- Retrieval-augmented assistants to help teams query visual or technical data quickly.
Proof-of-concept AI models that demonstrate ROI in days.
English
Native or bilingual
French
Conversational
Russian
Native or bilingual
Can work on-site
Paris (up to 50km)
Experience
- MendelgateCTOTECHJanuary 2025 - Today (1 year and 6 months)Paris, FranceI lead development of custom computer vision models for asset integrity and predictive maintenance.
- Centre National de la Recherche Scientifique (CNRS),Doctoral Researcher — Machine Learning & Computer VisionFebruary 2023 - December 2025 (2 years and 10 months)Paris, France• Developed computer vision and ML pipelines for automated corrosion and asset integrity analysis• Applied data-driven modeling to experimental materials science for reproducible ML based assessment• Integrated ML models into real experimental workflows considering data quality, physical constraints, and interpretability• Collaborated across disciplines, translating domain problems into deployable ML solutions• Research interests: explainable AI, scientific ML, vision-based analysis of physical pro cesses
- NEW EIGData Scientist / Quantitative ResearcherAugust 2023 - December 2024 (1 year and 4 months)• Designed and validated statistical and ML models for risk and probability estimation in financial systems• Handled high-noise, real-world data with robust validation and uncertainty quantification• Translated business and risk requirements into quantitative ML solutions for decision making• Operated across full ML lifecycle: data analysis, model building, evaluation
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Education
- PhD, Materials Science & MachineUniversit ´ e Paris Cit2026PhD, Materials Science & Machine
- MSc, Financial Engineering /WorldQuant University2022MSc, Financial Engineering /