Developing trustworthy AI across designed to survive real data, real deployment, and real-world use.
I am a physician and PhD candidate working at the intersection of medicine, mathematics, and machine learning. I began my research journey early, moving from statistics and clinical research into machine learning in 2019, and have since built work spanning outcome prediction, NLP, computer vision, time-series modelling, LLM training, evaluations and inference, and AI systems for practical deployment in healthcare, finance, real estate, and beyond. My focus is not just model performance, but building state-of-the-art methods and systems that are rigorous, interpretable, and usable in the real world.
Profile
I work end to end: from defining the research question and data strategy, to designing the methods, building the systems, and carrying them through to deployment. My background as a physician, combined with years of work in statistics, research, and machine learning since 2019, lets me operate across levels that are often separated: medicine, mathematics, modelling, software, and infrastructure. I build work that has to hold up against real data, real workflows, and real implementation constraints, using tools such as SQL, R, Python, Java, and Rust across both local and cloud environments. Whether the task is clinical prediction, multimodal AI, or large-scale research infrastructure, I approach it as both a researcher and a builder.
Current fronts
- Fair and transparent renal outcome prediction
- Medical multimodal models and post-training
- Clinical NLP and chest X-ray language/vision workflows
- Medical statistics, time-series analysis, and registry-scale research
- Agentic AI systems and real-world deployment workflows
- Building Quintessence
Doctoral focus
Prediction of renal outcomes with fairness, transparency, and clinically usable evaluation.
Project Quintessence
An independent international research infrastructure connecting collaborators across three continents to build ambitious work in clinical AI, open systems, and real-world technical projects.
Large-scale registry work
Ongoing work on the biggest single-institute liver transplant registry in Europe.
Peer-reviewed papers
4
Recent clinical and translational work across nephrology, prediction, and registry-scale medicine.
Conference presentations
9
Awarded work across internal medicine, cardiology, neurology, nephrology, and plenary sessions.
Core collaborators
6
Academic, nonprofit, and technical collaborators across clinical AI, research infrastructure, and systems work.
Doctoral focus
PhD
Prediction of renal outcomes with fairness, transparency, and clinically usable evaluation.
Project Quintessence
Visit
Open the foundation site for research infrastructure, open systems, and collaborative technical work.
Suggested research
AKI
Journal of Clinical Medicine / 2026Forecasting ICU Acute Kidney Injury with Actionable Lead Time Using Interpretable Machine Learning: Development and Multi-Center Validation
NPI
BMC Infectious Diseases / 2025The Impact of COVID-19 Non-Pharmaceutical Interventions on Notifiable Infectious Diseases in Poland: A Comprehensive Analysis from 2014-2022
Suggested writing
Collaborators and institutional anchors.
Collaborator
Medical University of Warsaw
Collaborator
Khalifa University
Collaborator
European Renal Association
Collaborator
Quintessence Health AI
Collaborator
SimplePod
Collaborator
Voltage Park
Contact
Open to research collaboration, speaking, and serious technical work in clinical AI.
If the overlap is prediction, multimodal systems, agentic workflows, robotics vision, or research infrastructure, email is the fastest route.