Muhammad A.

🔬 I’m a Data Scientist (Computational Biology) with a Master’s in Bioinformatics and Systems Biology from New York University, where I received the highly competitive and very prestigious Fulbright Scholarship. With over 3 years of intensive research experience at NYU and EMBL, I specialize in building clean, reproducible data pipelines that turn complex genomic, transcriptomic, proteomic, epigenomic (and/or other -omic) data into clear, actionable insights—ready for publication or real-world application. Publications: Ali, M., Li, F., & Katari, M. S. (2025). SkeletAge: Transcriptomics-based Aging Clock Identifies 26 New Targets in Skeletal Muscle Aging. bioRxiv, 2025.07.28.667277. doi.org/10.1101/2025.07.28.667277. 🧬 Genomics & Multi-Omics Analysis: • Genomic QC (FastQC, MultiQC) • Alignment (STAR, HISAT2, Kallisto, Salmon) • Bulk RNA-seq and single-cell RNA-seq (Seurat, DESeq2, edgeR) • DNA methylation analysis (Illumina Infinium 450k/EPIC, SeSAME) • ChIP-seq and ATAC-seq analysis (peak calling, motif enrichment) • CITE-seq and immune receptor profiling (VDJ, cell-surface proteins) • Variant calling pipelines (GATK, bcftools, samtools) • Functional analysis: GO, KEGG, Reactome (clusterProfiler, gprofiler2) • De novo transcriptome and genome assembly (Trinity, SPAdes, STAR) • Orthology/homology mapping (biomaRt, Ensembl, OrthoDB) • Pathway and network modeling (igraph, STRINGdb, Cytoscape) 🧑🏻‍💻 Machine Learning & Statistical Modeling: • Predictive modeling (classification, regression, survival analysis) • Feature selection, hyperparameter tuning, regularization (LASSO, Ridge, Elastic Net) • Dimensionality reduction (PCA, UMAP, t-SNE) • Clustering • Deep learning (CNN, RNN, LSTM, Attention, Transformers, Vision Transformers (ViT), GNN) • Self-supervised learning (contrastive learning, masked autoencoders) 📊 Pipeline Development & Data Analysis: • Programming & Computing: R, Python, Bash scripting, SQL, Docker containerization, High-Performance Computing (Linux/HPCC) • Machine Learning & AI: PyTorch, scikit-learn, feature importance analysis, model interpretability (CAM), boosting, bagging, tree-based methods. • Bioinformatics Tools: DESeq2, Seurat, SeSAME, STAR, samtools, GATK, Cytoscape • Wet Lab Techniques: Yeast two-hybrid (Y2H), bisulfite sequencing, DNA/RNA extraction, Gateway cloning • Nextflow and Snakemake workflow management, reproducible computational pipelines • Interactive data visualization (ggplot2, seaborn, Shiny, Streamlit) 🌟 Research & Industry Experience: - Data Scientist at AgeRate (Industry): • Developing advanced epigenetic clocks for biological age profiling and EpiScore generation. • Creating and refining bioinformatics pipelines for precision health applications. - Researcher at NYU: • Developed an aging clock (SkeletAge) to identify 26 novel aging-related genes and potential drug targets. • Compiled an extensive aging-transcriptomics dataset (N=534) to accelerate future research. • Conducted high-throughput yeast two-hybrid (Y2H) screening and ChIP-seq analyses for heterochromatin formation studies. - European Molecular Biology Laboratory (EMBL): • Analyzed epigenetic age acceleration in mouse models • Optimizing bisulfite-PCR and pyrosequencing protocols. 🏅 Honors & Recognition: • Fulbright Master’s Scholarship • NYU GSAS Full Tuition Scholarship • Rector’s Gold Medal for Outstanding Research (NUST) 💡 Why Work With Me? ✅ Proven Results: My work contributes directly to scientific publications, funding successes, and real-world biotech solutions. ✅ Speed & Accuracy: Fully reproducible results typically delivered within days, aligned closely with client research goals. ✅ Clear Collaboration: Open, regular communication ensuring your project stays aligned with your objectives from start to finish. Bioinformatics | Genomics | RNA-seq | Single-cell RNA-seq | DNA Methylation | Variant Calling | Multi-Omics Integration | Machine Learning | Predictive Modeling | Statistical Analysis | Survival Analysis | Deep Learning | Nextflow | Snakemake | Python | R | Bash | Docker | Data Visualization | Epigenomics | Transcriptomics | Biomarker Discovery | Epigenetic Clock | Aging Clocks | ATAC-seq | ChIP-seq | WGS | Proteomics