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PhD in Theoretical Biophysics

MSc in Physics

BSc in Chemistry

Principle Machine Learning Engineer

• Building robust and scalable data pipelines — Designing and maintaining end-to-end data infrastructure for clinical VR datasets, spanning ingestion, validation, versioning, and serving layers across multi-regional deployments.
• Developing machine learning models — Researching and implementing models for eye-tracking signal processing and clinical screening applications, including time-series classification, self-supervised learning, and psychometric score derivation from raw biometric data.
• Implementing internal AI tools — Building knowledge graph systems, deep research pipelines, and autonomous data science agents to accelerate clinical research workflows and reduce manual analytical overhead.
• Developing the scientific product strategy — Owning the full arc from research conceptualization and study design through regulatory-aware productionisation, bridging clinical science and engineering stakeholders.

Intentional Career Break

• Open-source project development in AI4Science
• Agentic AI and knowledge graphs

Machine Learning Engineer

• Led research and development for biomedical imaging and spatial data analysis, resulting in three different features offered as part of company's SAAS.
• Developed robust object (cell and tissue) segmentation models using deep neural networks, significantly reducing project delivery times.
• Led internal and customer projects involving statistical modeling of high-dimensional image data.

Postdoctoral Researcher

• Main focus on understanding the structure and aggregation dynamics of the intrinsically disordered proteins related to the neurodegenerative diseases.
• Developed a virtual drug discovery data pipeline for compounds preventing disordered protein aggregation.
• Developed machine learning methods for molecular dynamics force field development.
• Investigated the effect of mutations in protein sequences and their relevance for renal diseases.

PhD Student

• Investigated the role of membrane-embedded small sugar molecules in facilitating membrane adhesion using molecular dynamics simulations.
• Developed a full-atomistic molecular dynamics force field to accommodate different water types.
• Developed statistical methods for predicting the forces between small sugar molecules during binding.

NMRLipids (FAIRMD) Project

• Core designer and developer of the open-source NMRLipids databank for molecular dynamics simulation data. Currently it is the world's largest open databank on lipids.

scikit-mol

• Contributor to a project aiming to seamlessly combine machine learning library scikit-learn and cheminformatics library RDKit to streamline machine learning workflows for cheminformatics.

IDPdatabank

• Core designer and developer of the open-source IDPDatabank that can be used to validate classical and generative AI structure predictions.