Summary
Syed Baker is a Research Fellow and experienced single-cell data scientist with 11 years of computational biology expertise, based at the University of Manchester. He develops algorithms and R/Shiny tools for analyzing single-cell RNA-seq, ATAC-seq, multi-omics and spatial transcriptomics, and leads bioinformatics support within a core facility. His recent work integrates H&E image features with transcriptomics using deep learning (Detectron2/R-CNN) to localize cell types and infer pathway enrichment in contexts such as liver fibrosis, esophageal cancer, and pancreas development. He also applies GWAS to single-cell data to prioritize enhancers linked to Type 2 Diabetes and is building methods to infer gene regulatory networks from multi-modal single-cell datasets. Known for clear communication with biological collaborators, he combines rigorous PhD-level modeling methods (including a constrained square-root unscented Kalman filter from his PhD) with practical machine learning pipelines for translational research.
11 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy (PhD), Computational Biology, Cum Laude, Doctor of Philosophy (PhD), Computational Biology, Cum Laude at Leibniz Institute of Plant Genetics and Crop Plant Research
MIT, Information Technology, MIT, Information Technology at University of Dhaka
Bacher of Science, Computer Science, Bacher of Science, Computer Science at East West University
Bengali, German, Arabic, English