Hello, I'm
Ph.D. Computer Science · Applied LLM Data Scientist, Amsterdam UMC · 70+ citations · EU citizen
I'm an applied LLM and data scientist with a Ph.D. in Computer Science from the University of Groningen (2024) and postdoctoral experience at Amsterdam UMC's Translational AI Lab. I build and ship LLM-powered and foundation-model systems that solve concrete problems, from GPT-CEP, an applied LLM pipeline that automatically generates and refines event-processing rules from data patterns, to transformer-based foundation models trained on 1.2M+ clinical records.
I work across the full applied ML stack: pre-training and fine-tuning foundation models, designing evaluation and embedding frameworks, and building the data pipelines and MLOps workflows that take models from prototype to production. Recent work includes federated, embedding-based prediction systems built with the University of Cambridge, and LLM-assisted rule generation over high-volume streaming data with Apache Kafka and Flink.
My PhD and postdoctoral training gave me the depth to design and evaluate these systems rigorously; my focus now is applying that depth to build production-grade LLM and ML products. I hold research affiliations with the University of Amsterdam, University of Oslo, and TU Ilmenau.
Almere, Netherlands EU Citizen · No visa required
Applied LLM system for automated complex event processing rule mining: an LLM reasons over latent data patterns and generates interpretable detection rules, combined with rule-based backends over live Kafka/Flink pipelines with monitoring and feedback loops.
Transformer-based foundation model trained from scratch on 1.2M+ clinical records. Custom tokenizer, pre-training loop with masked objectives, multi-task fine-tuning heads, and full evaluation harness with W&B tracking.
Transformer-based foundation model for high-dimensional mass spectrometry signals. Pre-training on unlabelled spectra, fine-tuning for classification and retrieval. Custom positional encodings for variable-length spectral inputs.
Geometry-based framework for analysing learned embedding spaces from foundation models. Clustering, label-free correspondence metrics, nearest-neighbour retrieval, and visualisation pipelines for discovering latent clinical structure.
Adaptive pattern-level privacy protection in complex event processing systems. Enforces privacy at the pattern level (not data level) using CEP-like processing in Apache Flink. Published in Elsevier Information Systems (2024).
Pre-training transformer models from scratch, fine-tuning, PEFT/LoRA, domain adaptation, and production LLM systems for structured tasks — including GPT-CEP, an applied LLM pipeline for automated rule generation.
ML pipelines on large-scale clinical datasets, patient classification, biomarker discovery, and clinical decision support systems.
Privacy-preserving distributed training across institutions, federated evaluation frameworks, embedding-based FL, and governance mechanisms.
End-to-end ML pipelines, experiment tracking with W&B, reproducible workflows, Docker, and production-quality system design.
Real-time event processing with Apache Kafka and Flink, LLM-assisted rule generation (GPT-CEP), complex event pattern detection, and high-throughput distributed pipelines.
Pattern and anomaly detection in high-volume data streams, temporal pattern analysis, distributional shift detection, and risk scoring.
Amsterdam UMC · Translational AI Lab
2024 – Present · Amsterdam, Netherlands
University of Amsterdam · IDEAL Lab & Complex Cyber Infrastructure Group
2020 – 2024 · Amsterdam, Netherlands
University of Oslo · Analytical Solutions & Reasoning Group
2020 – 2024 · Oslo, Norway
University of Groningen · Distributed Systems Group
2020 – 2024 · Groningen, Netherlands
TU Ilmenau · Distributed Systems & Operating Systems Group
2020 – 2024 · Ilmenau, Germany
University of Groningen, Netherlands
2020 – 2024
Thesis: "Adapting Event Processing in Dynamic IoT Applications: Meeting Evolving Requirements for Quality, Privacy, and Rule Autonomy"
Specialization: Networks & Information Systems for IoT
Research: trade-off between quality monitoring and privacy protection in event processing systems
Foundation in software systems and engineering principles
70+ citations · Google Scholar, June 2026
Towards Federated LLM-Powered CEP Rule Generation and Refinement
18th ACM International Conference on Distributed and Event-based Systems (DEBS) · 2024 · 3 citations
A Comparative Framework for Evaluating Foundation Models on Tabular Data: A Case Study in Healthcare
npj Digital Medicine
Embedding-Based Federated Learning with Runtime Governance for Iron Deficiency Prediction
FL-ICML Workshop on Federated Learning in the Age of Foundation Models (FLICS) · Accepted 2026 · with University of Cambridge
A Label-Free Correspondence Score for Validating Learned Patient Representations in Clinical Tabular Data
Journal of Biomedical Informatics
From Latent Biomarkers to Clinical Rules: Embedding-Guided Rule Mining and Attribution-Based Translation for Interpretable Tabular Learning
Artificial Intelligence in Medicine
Embedding Space Discovery: Geometry-Based Clinical Data Analysis for Patient Similarity
Under Submission
Patient Classification via Learned Embedding Spaces: Application to Congenital Hypothyroidism Screening
CIBB 2026
App-CEP: Adaptive Pattern-Level Privacy Protection in Complex Event Processing Systems
Information Systems, Elsevier · 2024 · 4 citations
Pattern-Level Privacy Protection in Event-Based Systems
SN Computer Science, Springer · 2025
AQUA-CEP: Adaptive Quality-Aware Complex Event Processing in the Internet of Things
17th ACM International Conference on Distributed and Event-based Systems (DEBS) · 2023 · 16 citations
Towards Adaptive Quality-Aware Complex Event Processing in the Internet of Things
18th IEEE International Conference on Mobility, Sensing and Networking (MSN) · 2022 · 8 citations
Driving Towards Efficiency: Adaptive Resource-Aware Clustered Federated Learning in Vehicular Networks
22nd Mediterranean Communication and Computer Networking Conference (MedComNet) · 2024 · 7 citations
Towards Pattern-Level Privacy Protection in Distributed Complex Event Processing
17th ACM DEBS · 2023 · 3 citations
Presented at the Bloodcount Consortium Workshop at Cambridge's Department of Applied Mathematics & Theoretical Physics. Day 1: presented our federated learning framework for blood count data, built around the real constraints of trusted research environments, using the Flower (flwr) framework with embeddings from the DeepCBC foundation model. Day 2: joined the Foundation Model Workgroup. Ended with a dinner at Trinity College's Great Hall. Great discussions and a strong reminder of why this consortium matters.
View on LinkedInOur paper on embedding-based federated learning with runtime governance for iron deficiency prediction has been accepted at the FL-ICML Workshop (FLICS) 2026, in collaboration with the University of Cambridge. Looking forward to sharing the work.
View on LinkedInThree papers currently under review: a comparative framework for evaluating foundation models on clinical tabular data (npj Digital Medicine), a label-free metric for validating learned patient representations (Journal of Biomedical Informatics), and a method for translating learned biomarkers into interpretable clinical rules (AI in Medicine).
View on LinkedInOpen to applied LLM, data science, and foundation model roles, as well as research collaborations. EU citizen, no visa required.