Mohammad Waqas

Owned and executed analytic strategy for multi-terabyte omics and AI/ML programs (WGS, metabolomic, proteomic) across UK Biobank, All of Us, NIAGADS, and MGB Biobank, enabling consistent cross-cohort inference at population scale.

Architected a novel parallelized WGS GWAS workflow on DNAnexus cloud infrastructure, achieving a 7x wall-time reduction, 3x cost reduction, and 5x storage reduction relative to existing pipelines.

Led identification and mitigation of statistical and methodological risks in large-scale association studies, including population structure artifacts, confounding, and calibration failures.

Individually initiated and led a methods-development program outperforming field standards for ancestry correction (PCA), demonstrating improved GWAS calibration via nonlinear population structure modeling.

Developed reusable command-line tooling to batch-process hundreds of thousands of files with consistent transformations, logging, and failure recovery in Linux/HPC environments and cloud platforms (AWS-backed DNAnexus).

Analyzed fMRI data to precisely map the primary somatosensory cortex, contributing to understanding brain function and neural pathways.

Utilized advanced computational tools and scripting (R, Python, bash) for efficient data processing and visualization in neuroimaging studies.

Executed end-to-end analysis lifecycles, from data ingestion to interpretation, for complex fMRI datasets.

Collaborated with principal investigators to design and implement experimental protocols for neuroimaging studies, ensuring data integrity and scientific rigor.

Conducted research to determine the impact of BIN1 on network hyperexcitability, a key pathological feature in Alzheimer's disease.

Utilized advanced laboratory techniques and data analysis to characterize gene function and its physiological effects.

Collaborated with a multidisciplinary team to advance understanding of neurodegenerative mechanisms and disease progression.

Presented findings at multiple research competitions and conferences, including the Ost Undergraduate Research Competition and the Comprehensive Neuroscience Center Retreat.

Analyzed large genomic datasets to identify novel genetic and sex-specific risk factors associated with Alzheimer's disease progression.

Executed comprehensive data analysis lifecycles, including data ingestion, quality control, and statistical modeling for genetic association studies.

Applied rigorous statistical diagnostics to ensure robustness and validity of findings in complex genetic analyses.

Presented research findings at the Harvard-Amgen Scholars Presentation, demonstrating strong scientific communication skills to a diverse audience.

Analyzed genomic data from bacteriophages to identify and characterize common repeat regions, contributing to microbiology research.

Applied computational methods for sequence analysis and data interpretation in a genomics context.

Participated in scientific discussions and presented findings at the UAB Spring 2018 Undergraduate Research Expo and NCUR 2018, enhancing scientific communication.