Welcome to my GitHub! I’m a molecular and cellular biologist with over 20 years of experience in academic research and recent expertise in applied data science and machine learning. I combine deep scientific knowledge with modern analytical tools to tackle complex problems—particularly in biology and biomedicine—but my passion extends beyond these fields. I’m driven by the power of big data and eager to contribute to data-driven projects that deliver insights, solutions, and impactful results across various domains.

📄 View My CV and full publications list

Here’s a selection of personal, academic, and study-related projects that reflect my journey into applied data science:

• 🧬 RNA-seq Prostate Cancer Clinical Outcome Analysis

  • Identified clinical biomarkers using RNA-seq data, applying differential gene expression analysis and machine learning models (Logistic Regression, Random Forest, CNN on tabular data converted to images).

• 🍬🩸 Diabetes Prediction with Machine Learning (coming soon)

  • Built supervised models (Logistic Regression, XGBoost) to predict diabetes risk from women patient health data.*

• 🎵 Audio Classification with Deep Learning

  • Developed CNN-based classifier for urban sound datasets classification (audio vs speach) using spectrograms, CNNs, and PyTorch *

• 👁️ 🐶 Computer Vision Internship Project (in progress, private)
  Behavioral tracking of dogs using YOLO and DeepLabCut.

• Data Scientist, DIRU, Zürich University (2024–2025)
  Applied deep learning to behavioral imaging.

• Staff Scientist, Bern University (2012–2023)
  Led multi-omics research (RIP-Seq, proteomics, genetics) and supervised MSc/PhD students.

• PhD in Natural Sciences, Max Planck Institute / Kassel University (2004)
  Cap-independent translation in Drosophila; summa cum laude.

• CAS in Applied Data Science, Bern University (2023–2024)
  Specialized in machine learning, bioinformatics, and Python/R workflows.

📊 Data Science / ML

• Python: Pandas, NumPy, matplotlib, seaborn, plotly, Scikit-learn, TensorFlow, PyTorch
• R: ggplot2, bioinformatics packages
• Other tools: SQL, ImageJ/Fiji, GraphPad, CVAT labeling software, Jupyter Notebooks, Git
• Machine learning: classification, regression
• Deep learning / computer vision: CNNs, autoencoders, diffusion models

** 🧪 Bio & Lab**

• Transcriptomics: RNA-seq, RIP-seq, RT-qPCR, NGS
• Proteomics, in vitro translation, protein expression, purification
• Imaging: confocal microscopy, immunofluorescence
• Model organisms: Drosophila, mammalian cells
• CRISPR/Cas9, fly genetics

Lead or co-author on peer-reviewed publications in journals including eLife, Development, J. Cell Sci, and PLoS Genetics.
Topics span RNA transport, translational control, tubulin modifications, and gene regulation.

See full list → CV & Publications

I’m open to collaborations, data-driven research projects in all disciplines.

📧 paulavazq@gmail.com
📍 Bern, Switzerland
🌐 Website / CV