• 🔬 Postdoctoral Researcher at SnT, Luxembourg
• 🎓 Two PhDs in Mathematics & Computer Science
• 📈 Google Certified Data Analyst
• 💡 Specialized in Algebraic Graph Theory, Bioinformatics Pipelines, Graph Neural Networks, & Network Science
• 🌱 Currently focusing on Automation in Bioinformatics, Graph Databases, & Scalable Systems for Biological Data

My skill set is a fusion of theoretical knowledge and practical expertise, particularly in bioinformatics:

📘 Programming Languages:

• Python: Advanced usage in bioinformatics, data science, and machine learning with libraries like TensorFlow, Keras, Scikit-learn, Pandas, NumPy, and Biopython.
• R: Utilized for statistical analysis and bioinformatics packages like ggplot2, dplyr, and edgeR.
• SQL: Employed for managing biological databases and querying large datasets.
• Bash: Scripting for automating bioinformatics workflows on Linux platforms.

🧬 Bioinformatics & Computational Biology:

• Expertise in developing and automating bioinformatics pipelines for processing and analyzing metagenomic and microbiome data.
• Experience with graph-theoretical approaches for analyzing complex biological networks and applying Graph Neural Networks (GNNs) for predictive and descriptive analytics in omics data.
• Proficient in tools like Nextflow and Docker for containerizing and scaling bioinformatics workflows.

☁️ Cloud Platforms:

• AWS & GCP: Applied cloud services for scalable storage and computation of large biological datasets.

📊 Data Analysis & Visualization:

• Proficient with Pandas and R for data manipulation and analysis in bioinformatics.
• Skilled in using visualization tools such as Matplotlib, Seaborn, ggplot2, and Cytoscape to derive insights from complex biological data.

🗄️ Graph Databases:

• Neo4j: Applied for storing and querying large-scale biological networks, facilitating advanced graph-based analyses.

🔧 Development Tools:

• Git: For version control and collaborative research coding.
• Linux: Comfortable with the Linux environment for development and deployment of bioinformatics applications.

🤖 Natural Language Processing (NLP):

• Experience with NLP techniques for analyzing scientific literature and extracting meaningful information from biological texts.

🗄️ Big Data Technologies:

• Utilized PySpark and Hadoop for handling and analyzing large-scale biological datasets in distributed environments.

• LinkedIn
• Medium
• Email: ehsan.estaji [at] gmail [dot] com