🔥🚀I am passionate about Data Science (DS), Machine Learning (ML), Artificial Neural Networks (ANNs), Computer Vision (CV), Natural Language Processing (NLP), Large Language Models (LLMs), Large Vision-Language Models (LVLMs), and Agentic AI.

💰📈My mission is to empower my team(s) to tackle complex challenges through advanced analytics and automation. I transform raw data into actionable insights and products, leveraging AI/ML algorithms, statistical models, and data visualizations to reduce costs, promote sustainability, and optimize profitability.

🤖⚠️At EXP, I lead data- and AI-driven R&D to advance construction automation and energy management. I employ Convolutional Neural Networks (CNNs) to detect construction failures, calculate heat loss from IR thermography images, and estimate window-to-wall ratio for building energy simulations. I develop AI, computer vision, and analytics solutions that streamline construction workflows, reduce costs, and improve engineering decision-making. Using CNNs, semantic segmentation, and YOLO detection, I automate identification of cracks, missing sealant, membrane failures, and other envelope defects from construction imagery. I deploy Large Vision-Language Models (LVLMs) to generate automated field-review insights from site images, accelerating reporting and strengthening QA/QC.

📊💰I also build ML models to predict budget-overrun risks, apply EDA to track employee utilization and identify high-performance staffing KPIs, and support engineers with data-aware simulation inputs by predicting missing material and environmental parameters. My work bridges AI automation with practical engineering needs to improve operational efficiency, reduce risk, and enhance building performance.

🧠💡At Scale AI, I evaluated LLM performance for coding-intensive roles, as I contributed to several projects like Beagle Coding, Coders Full Stack, and Observation Concrete, Pheonix, Valkyrie, etc.,. In addition, I evaluate large language models by creating Mutually Exclusive Collectively Exhausitve (MECE)-structured rubrics that produce verifiable True/False rewards for reinformcement Learning from Verifible Rewards (RLVR), improving model reliability and consistency. I also design Model Customization Instructions (MCIs), user prompts, and multi-turn scenarios to test alignment under tension. I build supervised fine-tuning datasets (“training by showing”), provide Reinformcement Learning from Humann Feedback (RLHF) data, and craft complex Python/SQL prompts to induce and correct model failures. I also implement Model Context Protocol (MCP) tool-calling pipelines and assess external API integrations, reducing manual search and analysis time.

👤🏷️At Telus Digitals (formerly Telus International AI), I provided high-quality labeled data for tasks like Named Entity Recognition (NER) and Region of Interest (ROI) annotation, supporting CV and LLM training. This work also established Human-Level Performance (HLP) benchmarks for robust AI evaluation.

🏠💨At UofT, I improved indoor air quality (IAQ) and sustainability by ML and data analytics. Using AI/ML, I predicted HVAC operations based on temperature and humidity changes, forcasted thermal comfort in multi-unit residential buildings (MURBs), and introduced Rapid Quantitative Filter Forensics (RQFF) to expedite airborne contaminant analysis, enabling efficient post-field HVAC filter forensics and laboratory coordination🔍.

💵🩺 My other activities extends to finance, retail, healthcare, and beyond, where I’ve worked on projects like fraud detection, sales optimization, customer churn prediction, breast cancer tumor detection, sentiment analysis, machine translation, self-driving cars, and sports analytics. I also specialize in MLOps, Big Data, and recommender systems, delivering tailored solutions across sectors.

Programming (Python, SQL, VBA, C/C++)

DS, ML, & Deep Learning (Pandas, Numpy, Scikit-Learn, TensorFlow, PyTorch, OpenCV, CuDF, XGBoost, Polars)

Plotting & Visualization (Matplotlib, Seaborn, Plotly, Pandas, Bar Chart Race)

Text Mining & NLP (nltk, SpaCy, TextBlob)

Statistics (Scipy, StatsModels)

MLOps & Cloud (Docker, FastAPI, Flask, Azure, AWS, Databricks)

Big Data (PySpark, Spark SQL, Polars, CuDF)

RDBMS (MS SQL Server)

Climate Change & Environment (MeteoStat, PyThermalComfort)