Misha Siddiqui

Developed deep learning and spatial analysis pipelines integrating imaging and multi-omics data to identify disease biomarkers. Validated models, collaborated cross-functionally, published findings, mentored juniors, and ensured compliance.

Delivered training in coding, AI, and machine learning to students. Designed and facilitated interactive learning exercises and mentored students on programming and problem-solving projects.

Produced written content on healthcare and scientific topics, synthesising technical information into clear insights.

Conducted experimental analyses using ELISA, PCR, and flow cytometry.

Applied quantitative modelling to analyse treatment response and generated insights into metabolic signatures associated with therapy outcomes.

Developed quantitative analytical method using chromatography and mass spectrometry. Designed experimental workflow for metabolite detection in clinical samples.

Developed assay for more efficient diagnosis of eosinophilic esophagitis.

Supported international academic program delivery in Riyadh, including tutor coordination, instructional oversight, student guidance, academic reporting, and participation in induction/orientation sessions. Ensured pedagogical quality and provided administrative and welfare support in compliance with host institution policies and Saudi regulations.

Activities and societies: Authored four peer-reviewed publications in computational biology and AI-driven disease modelling; collaborated on interdisciplinary projects in chronic kidney disease and metabolic disease research.

Conducted doctoral research at a world-leading cancer research institute, selected for a competitive UKRI-funded PhD studentship in partnership with AstraZeneca. Developed expertise at the intersection of AI, biomedical science, computational biology, bioinformatics, statistical and pharmaceutical research.

Conducted advanced research in clinical and fundamental immunology, selected for a competitive research project focusing on statistical modeling of metabolites.

Grade: First-class honours

Activities and societies: Strengthened analytical problem solving through research and lab work, and developed clear scientific communication skills.

Built a multidisciplinary understanding of human biology and disease, combining biomedical sciences with clinical principles. Developed strengths in data interpretation, statistical analysis, and scientific evaluation.

Completed data analysis industry training with AiCore.

Completed data analysis industry training with Brainnest.

Completed coursework in R basics and statistics, and R programming.

Completed the Complete Python Bootcamp course.

Completed the Machine Learning course on Coursera.

Completed courses in Mathematics for Machine Learning: Linear Algebra, Multivariate Calculus, and Dimensionality Reduction with Principal Component Analysis.

Completed the Fundamentals of Deep Learning course.