Mohammad Noorchenarboo

Teaching graduate-level AI & ML courses within the Artificial Intelligence and Machine Learning program.

• Deep learning architectures: RNNs, CNNs, YOLO, GANs, Vision Transformers, Diffusion Models
• Large Language Models (BERT, GPT) — training, fine-tuning, and deployment pipelines
• Explainable AI using SHAP, LIME, and attention-based interpretation methods
• Mentoring students on applied AI research projects and advanced implementation practices

Developing predictive models using deep learning and transformer architectures for healthcare analytics.

• BERT & multimodal fusion models to forecast surgical process duration and outcomes
• Automated data workflows and integrated model outputs into real-time analytics dashboards
• Cross-functional collaboration resulting in peer-reviewed publications

ML models for intelligent grid analytics — anomaly detection and forecasting on large-scale time-series data.

• Anomaly detection and forecasting models for large-scale energy grid time-series
• Explainability and fairness methods for deep model transparency and accountability
• Automated data processing pipelines; contributed to peer-reviewed grid research

Supporting undergraduate and graduate courses in software engineering, data science, and machine learning.

• DATASCI 3000A: Introduction to Machine Learning (Sep–Dec 2025)
• Software Project & Process Management (Sep–Dec 2024)
• SE2250B: Software Construction (Jan–Apr 2024)
• Programming Fundamentals for Engineers (Sep 2022 – Jan 2023)

Built data pipelines and applied ML for large-scale genomic and clinical trial data analysis.

• Data pipelines and QC workflows for large-scale genomic and clinical trial datasets
• Multi-omics ML models integrated into interactive R Shiny applications
• Genotype–phenotype association studies using Python, R, and PLINK

Led statistical analysis using advanced and Bayesian methods, ensuring data integrity and actionable insights.

• Bayesian methods in R and Python for rigorous research analytics
• Designed statistical analysis plans, sample sizes, and experimental designs
• A/B testing and hypothesis-driven analysis; automated reporting workflows

Built statistical programs for biomedical data analysis in rare disease research.

• Statistical software for rare disease biomedical datasets ensuring data integrity
• Data visualizations and accessible statistical summaries for interdisciplinary teams

AI and healthcare analytics — deep learning, transformers, multimodal fusion, and explainable AI.

• Transformer and multimodal deep learning models for clinical prediction and surgical efficiency
• Explainable AI (SHAP, LIME, attention) for healthcare model transparency
• Peer-reviewed publications in collaboration with LHSC clinical partners
• Danny Ho Software Engineering Graduate Scholarship ($10,000) — May 2025

Advanced statistical modeling for clinical trials, longitudinal studies, and high-dimensional genomic data.

• Survival analysis, mixed-effects models, and causal inference methods
• Genomic and high-dimensional data analysis using R and Python

Optimization, simulation, and data-driven methods for supply chain and operational efficiency.

• Supply chain optimization and resource allocation modeling
• Simulation-based decision-making systems for industrial processes

Foundations in probability theory, statistical modeling, experimental design, and statistical computing.

• Probability theory, hypothesis testing, and statistical modeling
• R programming, survey design, and experimental study methods