Sheraz Ahmad

I've built machine learning and deep learning workflows for high-dimensional biological, biomedical, and neural datasets at the Functional Informatics Lab, National Centre for Bioinformatics. My work spans preprocessing, feature extraction, model training, evaluation, visualization, and reusable Python analytical pipelines.

I developed NeuroKonnect, a web-based platform for automated EEG preprocessing, functional-connectivity analysis, machine learning, visualization, and neurological risk profiling. I also built OpGene, an Optuna-based automated machine learning framework that standardizes model training, benchmarking, optimization, and evaluation for biological data.

Across transcriptomic, multi-omics, single-cell RNA-seq, and EEG data, I use Python and R to turn complex scientific questions into reproducible computational workflows. I'm particularly interested in applied machine learning, predictive analytics, AI products, intelligent automation, and data-driven software development.