I’m currently pursuing my Master’s in Bioinformatics, with growing experience in data analysis, NGS workflows, and API development.
I enjoy building tools that transform raw biological data into meaningful insights, and I’m especially curious about how AI/ML can advance research in life sciences.

I am currently exploring the world of Genomics, Proteomics, Cancer Biology, and Chemoinformatics.
My main research interests rest in research collaboration, bioinformatics pipelines, and applied machine learning in biology.

Recently, I worked on a project exploring data APIs of various bioinformatics databases such as UniProt, NCBI, and KEGG, integrating them in a minimal dashboard for easier access—aimed at making scientific workflows more reproducible, accessible, and faster.

Outside of research and academics, I enjoy reading books, clicking photos, making videos, and learning instruments.

• Develop bioinformatics tools and pipelines that turn raw data (NGS, sequence, structural) into reproducible insights.
• Build robust API wrappers (UniProt, NCBI, KEGG) and data pipelines to simplify biological data access and integration.
• Design interactive dashboards (Streamlit) and automated reporting workflows (ReportLab/Python) for faster research decisions.
• Utilize data analysis and computational methods for sequence/structural analysis, protein–ligand interactions, and drug discovery.
• Explore the role of AI/ML in life sciences, with interests spanning protein modeling, systems biology, and cancer bioinformatics.
• Highlight clarity, collaboration, and reproducibility in every project—ensuring code, data, and documentation are research-ready.

• Python (Biopython, Matplotlib)
• R (ggplot2, edgeR, DESeq2)
• Shell scripting (Bash, Linux environment)
• Jupyter Notebook
• Miniconda

• NCBI (BLAST, GEO, SRA)
• UniProt, PDB, Pfam
• Ensembl Genome Browser
• ExPASy suite (ProtParam, Translate, SwissSidechain)
• STRING (protein-protein interactions)
• MEGA (phylogenetic analysis)

• RStudio (statistical analysis, visualization)

• PyMOL, Chimera, ChimeraX
• AlphaFold DB
• Modeller
• SWISS-MODEL
• AlphaFold Protein Structure Database
• Robetta (Baker Lab)
• Structure Validation / Comparison
• SWISS-MODEL Structure Assessment
• PROCHECK

• TensorFlow
• Keras
• Scikit-learn
• Pandas
• NumPy

• Google Colab / Kaggle notebooks
• Cluster computing basics (batch job submission, Linux servers)

• Markdown (GitHub-friendly docs, READMEs)

• HTML(for portfolio/CV pages)
• Git & GitHub (version control, project hosting)