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Senior Researcher in BioAI

Software Engineering Group, University of Bern

Research and development of AI-driven bioinformatics tools within the Innosuisse-funded BioAI4LCMS project, focusing on scalable, adaptive “Bioinformatics-as-a-Service” solutions for biopharmaceutical data analysis. Contributing to software architecture, algorithm design, and cross-institutional collaborations, and supporting additional research initiatives at the intersection of machine learning, bioinformatics, and software engineering.

• Bioinformatics
• Artificial Intelligence
• Software Engineering

Thesis Researcher

University Children's Hospital Basel (UKBB)

Development of a machine learning pipeline to model neonatal gas exchange and predict the oxyhaemoglobin dissociation curve shift in preterm infants, enabling AI-driven assessment of bronchopulmonary dysplasia severity. Design of an end-to-end workflow & deployment of two tools: a Streamlit annotation platform for clinicians and a React/Flask web app for real-time clinical use.

• Physiology-Based Predictive Modeling
• Machine Learning
• Medical Informatics

Software Developer in Healthcare

CISTEC AG, Zurich, Switzerland

Development and optimization of components of the KISIM hospital information system used by major Swiss hospitals. Design, testing, and documenting new and existing system functionalities, performing analysis and specification for client implementations, and administering and configuring customer systems, as well as bug analysis and resolution, maintaining technical documentation, and collaborating closely with project leads to ensure smooth deployment and continuous improvement of clinical software solutions.

• Software Development
• Healthcare IT
• System Optimization

Master of Science Medical Informatics

Fachhochschule Nordwestschweiz, Basel, Switzerland

Medical Informatics

Final Grade: 5.8

• AI / ML: Machine Learning in Medicine, Explainable AI & Bias Mitigation in Healthcare, AI in Drug Discovery
• Biostatistics & Advanced Analysis: Data Analytics, Multi-omics, Data Integration, Signal Processing, Development of 1D & 2D Digital Biomarkers
• Data Privacy: GDPR/HIPAA, Cybersecurity & Cyber Resilience
• Regulatory Compliance & Data Governance: Medicines & Medical Devices Development (EMA/FDA), GCP & GMP, EDC, Real-World Evidence
• Digital Health & Business Transformation: Digital Transformation in Healthcare, Digitalisation of Business Processes in Healthcare
• Clinical Decision Support Systems: HL7, FHIR, DICOM, PACS, EHR/EMR Systems
• Programming & Software Engineering: Python, R, SQL, NoSQL, Git, Docker

Master of Veterinary Medicine

University of Zurich, Zurich, Switzerland

Veterinary Medicine

Final Grade: 5.2

• Clinical Practice: Hands-on diagnostic, therapeutic, and surgical training across species.
• Research Skills: Study design, data analysis, and evidence-based interpretation.
• Communication: Client interaction and interdisciplinary teamwork.
• Public Health: Insight into zoonoses, epidemiology, and One Health.
• Ethics: Competence in animal welfare and professional responsibility.

Bachelor of Veterinary Medicine

University of Zurich, Zurich, Switzerland

Veterinary Medicine

Final Grade: 4.4

• Foundations: Core knowledge in anatomy, physiology, and pathology.
• Preclinical Skills: Basic diagnostics, laboratory methods, and animal handling.
• Scientific Thinking: Introduction to research and data interpretation.
• Clinical Insight: Early exposure to case-based reasoning and veterinary ethics.

Roche Best Graduate Award MSc Medical Informatics

Fachhochschule Nordwestschweiz (FHNW)

Awarded for outstanding overall performance in the MSc Medical Informatics, with a final grade of 5.8.

Best Poster Presentation Award

University Children's Hospital Basel (UKBB)

Awarded for outstanding scientific communication of innovative research on machine learning models for neonatal gas exchange at the UKBB Research Day 2025.

BioAI4LCMS

An industry–academia collaboration (Genedata, University of Bern SEG, HES-SO Valais) to build AI-assisted LC–MS software that automates method development and data interpretation. Using self-learning models and heuristic optimization, the platform refines data processing, peak detection, and noise reduction to deliver faster, reproducible, and scalable analyses across instruments and labs.

• Adaptive LC–MS workflow automation
• Self-learning processing & peak detection
• Heuristic optimization for method setup
• Integration with Genedata Expressionist
• Standardization and scalability across labs

CEPEF-HARAT

A doctoral project with the University of Zurich, the Swiss Data Science Center, and the CEPEF consortium to develop the first ML-based tool for predicting mortality risk in equine anaesthesia. Built on the CEPEF4 dataset (>48k cases from 94 centres), the work cleans and structures heterogeneous data, applies NLP/LLMs to free-text fields, and advances explainable models for transparent, clinically useful risk assessment.

• CEPEF4 dataset (>48k anaesthesia cases)
• NLP/LLMs for free-text structuring
• Explainable AI (e.g., SHAP, LIME)
• Clinical risk stratification for horses
• Collaboration with UZH, SDSC, CEPEF

NeoCurve

An MSc project at UKBB that predicts the oxyhaemoglobin dissociation curve (ODC) shift as a physiologic marker of gas-exchange efficiency in preterm infants. A data-efficient, interpretable ML approach estimates ODC shift from as little as one or two SpO₂–PiO₂ points, validated across multi-centre datasets and deployed as a clinician-oriented web tool.

• ODC shift from 1–2 SpO₂–PiO₂ points
• Physiology-grounded, interpretable model
• External validation across datasets
• Streamlit annotation platform
• React/Flask bedside decision-support app