Hi, I'm Rick A. Reijnders, PhD.
A
Rooted in biotech, refined in AI. I fuse deep science with real-world clarity; building predictive, scalable, and automated data systems.
Core: Analytical, pragmatic, solution-focused.
Superpowers: Machine learning, multi-omics integration, statistical modeling.
Tools of choice: R (expert), Python, SQL, Linux, C.
Projects: AI for neurodegeneration (Lewy body, PTSD, Parkinson’s), industrial automation, and custom R packages.
Style: Clear communicator, process optimizer, pattern hunter.
Edge: I make complex data actionable.
Trajectory: From wet-lab to workflow architect. I design data solutions that think ahead.
Mindset: Strategic gamer meets builder-tinkerer. Drones, code, woodwork, and wires.
About
I am a Data Scientist and Computational Methodologist with a PhD in machine learning, multi-omics integration, and AI-driven statistical modeling. With extensive experience in big data processing, predictive modeling, and workflow automation, I develop robust, scalable AI-driven methodologies for data integration and decision-making.
I started my journey with fascination in laboratory science and technology began at ROC Leeuwenborgh, where I built a strong foundation in wet-lab techniques, analytical methods, and experimental precision. This hands-on experience fueled my curiosity about biological systems and data-driven research. However, it was during my Bachelor of Applied Science in Biomedical Sciences at Hogeschool Zuyd that I truly discovered my passion for bioinformatics.
Eager to dive deeper, I pursued an MSc in Systems Biology at Maastricht University, where I uncovered the transformative power of multi-omics integration, computational modeling and machine learning. I developed a broad toolbox of skills, including advanced statistical analysis, machine learning, and data visualization. These tools enabled me to analyze complex biological networks and extract meaningful insights from high-dimensional and complex datasets.
Since September 2020, I have been conducting PhD research at Maastricht University, applying systems biology and machine learning to investigate the impact of post-traumatic stress disorder (PTSD). My research is driven by a relentless curiosity and a commitment to unraveling the biological mechanisms underlying PTSD, with the ultimate goal of improving diagnostics and treatment strategies.
I am proud to be affiliated with the Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience (MHeNs), Faculty of Health, Medicine and Life Sciences, Maastricht University. My journey is fueled by an explorers mindset, pragmatic vision, and the drive to push boundaries in data-driven research. Whether I am building cutting-edge analytical pipelines or developing new methodologies for integrative data analysis, I am always striving to make a meaningful impact.
Lately, I have been deeply intrigued by the rapid advancements in artificial intelligence, particularly large language models (LLMs). Their potential to revolutionize research and enhance data-driven insights is something I find incredibly exciting. I am actively exploring how AI can be seamlessly integrated into my work, and I look forward to leveraging these innovations to drive breakthrough discoveries.
Experience
- Developed unsupervised learning methods for Lewy body disease subtyping using spectral, hierarchical, and PAM clustering.
- Applied sparse Partial Least Squares Discriminant Analysis (s-PLS-DA) to distinguish molecular LBD subtypes.
- Engineered multi-trait Polygenic Risk Scores using LDAK and GWAS summary statistics.
- Identified candidate therapeutics through gene signature-based drug matching (LINCS1000, CMAP).
- Implemented scalable machine learning pipelines on HPC clusters for large-scale multi-omics analysis.
- Developed integrated methylome-wide and transcriptomic workflows with advanced normalization and pre-processing.
- Optimized performance using C to accelerate feature selection, clustering, and data transformation in R and Python.
- Developed predictive models using random forests, gradient boosting, SVM, and deep learning for biomedical data.
- Applied Bayesian inference, LASSO regression, and AI-driven feature selection to uncover disease patterns.
- Designed scalable data pipelines for preprocessing and integrating multi-source datasets.
- Utilized text mining and transformer-based models to enhance automated literature synthesis.
- Built multi-omics models to predict clinical outcomes in PTSD and Parkinson’s disease.
- Designed automated AI-driven workflows for diverse data science and research projects.
- Developed standalone AI tools for rule-based decision-making, optimizing industrial and economic processes.
- Built scalable databases and machine learning models for comprehensive data analysis.
- Provided consulting on integrating advanced analytics into strategic decision-making.
- Investigated the effects of chronic social defeat stress on DNA methylation, miRNA regulation, and gene expression.
- Integrated multi-omics data (methylation, miRNA, RNA-seq) to identify biomarkers for PTSD susceptibility.
- Developed and optimized bioinformatics workflows in R for high-throughput sequencing data analysis.
Projects
Multi-omics integration to uncover biomarkers in cholestatic liver tissue.
Overview: Focused on integrating mRNA and miRNA expression profiles to uncover the molecular underpinnings of cholestatic liver conditions. The project identified potential biomarkers and therapeutic targets through high-throughput data integration.
- Multi-Omics Integration
- Data Analysis & Bioinformatics (R)
- Systems Biology & Statistical Modeling
- Biomarker Discovery
- Computational Data Integration
Applied machine learning to decode auditory signals and neural processing.
Overview: Investigated how the auditory cortex processes sound by employing computational modeling and machine learning to classify auditory signals and extract key spectro-temporal features.
- Machine Learning & Computational Modeling
- Signal Processing
- Data Science (Python, R)
- Neuroscience Data Analysis
- Algorithm Development
Examined the antimicrobial effects of silver ions on cells and bacteria.
Overview: Analyzed the antimicrobial properties of silver ions on SAOS-2 cells and bacteria (S. aureus, S. epidermidis, and P. aeruginosa), highlighting their potential in biomedical applications.
- Analytical Chemistry & Microbiology
- Cell Culture & Experimental Design
- Data Interpretation & Statistical Analysis
- Biomaterials Research
- Laboratory Techniques
Explored antibacterial potential with MIC/MBC assays and real-time sequencing.
Overview: This project explored the antibacterial potential of polyoxometalates using experimental microbiology and bioinformatics. It involved MIC/MBC assays, cell culture, and a Linux-based pipeline for processing sequencing data.
- Experimental Design & Cell Culture
- MIC/MBC Assays
- Linux Programming & Bioinformatics Pipeline
- Molecular Biology Techniques
- Real-Time Data Processing
Designed fluorescent beads for multiplex assays using quantum dot technology.
Overview: Designed and synthesized fluorescent magnetic beads for multiplex assays, optimized via spectral analysis, and developed a rapid fungal DNA extraction method validated by Real-Time PCR and gel electrophoresis.
- Nanotechnology & Material Synthesis
- Spectral Analysis
- Molecular Diagnostics
- Experimental Workflow Design
- Process Optimization
Investigated the effects of flavonoids on enzyme activity.
Overview: Explored how 6-chloro-2-(2,4-dichlorophenyl)-4H-chromen-4-one affects Glutathion S-transferase compared to Quercetin, elucidating the structure–activity relationship of flavonoids.
- Biochemical Assays & Enzymology
- Comparative Analysis
- Data Collection & Statistical Evaluation
- Research Methodology
- Laboratory Technique Optimization
Cloned and sequenced the GAPC gene from parsley to study genetic evolution.
Overview: Utilized molecular cloning and sequencing of the GAPC gene from Petroselinum crispum to investigate evolutionary changes, combining hands-on laboratory work with bioinformatics.
- Molecular Cloning & Gene Sequencing
- Bioinformatics & Evolutionary Analysis
- PCR and Restriction Enzyme Techniques
- Data Interpretation
- Laboratory Research
Enhanced data management through advanced Excel training and quality control initiatives.
Overview: Led Excel training sessions covering advanced formulas, pivot tables, macros, and created quality control sheets that streamlined workflows and improved statistical analysis efficiency.
- Data Management & Advanced Excel Functions
- Macro Programming & Process Optimization
- Training & Development
- Quality Control & Reporting
- Workflow Automation
Skills
Technical Skills
- AI & Machine Learning: Feature selection, predictive modeling, clustering, dimensionality reduction
- Big Data & Statistical Analysis: Time-series forecasting, regression, multivariate statistics
- Programming & Automation: R (8+ years, expert), Python, C, Linux (Bash scripting), SQL
- Data Processing & Integration: Multi-omics, text, image, economic data
- Methodology Development: AI-driven statistical techniques for robust data integration and predictive modeling
Education
PhD, Systems Biology & AI in biology
Maastricht University, Department of Psychiatry & Neuropsychology
Duration: 2020 – 2024
Thesis: From pieces to picture: systems biology, multi-omics and machine learning in complex brain disorders
Maastricht University, School for Mental Health and Neuroscience (MHeNs)
Duration: 2018 – 2020
Specialized in computational modeling and big data analysis.
Zuyd Hogeschool, Heerlen
Duration: 2014 – 2018
Focused on analytical methods, computational analysis, and statistical modeling.
MBO, Laboratory Technology – Biotechnology
Leeuwenborgh, Sittard
Duration: 2010 – 2014
Gained hands-on experience in data quality control, experimental design, and molecular analysis.