Welcome

Hi, I’m Wasik Billah. I am an Electrical and Electronic Engineering graduate from the Islamic University of Technology (IUT), Bangladesh.

Research interests

My research focuses on machine learning for engineering systems and on optimization methods that work alongside machine learning. I am currently looking for PhD opportunities and would be glad to hear from you.

My Works

• Load forecasting and scheduling: I benchmarked seven models (XGBoost, LightGBM, Random Forest, Gradient Boosting, KNN, SVR, MLP) on hourly feeder data from 10 cities in the Rangpur division. XGBoost performed best, with an R² of 0.944 and a MAPE of 2.69%. I used K-means clustering on the forecast to split the day into peak and off-peak hours. Hybrid metaheuristic schedulers HGWOCSO then moved household appliances to off-peak hours. In my papers, these schedulers reduced the peak-to-average ratio by up to 52.3% and electricity cost by up to 15.6%.
• Medical data: A comparison of classifiers on demographic and lab data to detect Hepatitis C, where AdaBoost came out most effective.
• Image classification: A seagrass classifier built on a frozen pretrained VGG19 base, with Batch Normalization, Dropout and a softmax layer on top.
• Battery health estimation: A machine learning pipeline that estimates lithium-ion battery State-of-Health from NASA discharge data (34 cells). It uses physics-based features, and the best model, Random Forest, reached an RMSE of 0.086.

How I work

I like to break down a problem into small parts and solve them step by step. I like to visualise the whole pipeline. I am also very punctilious when it comes to taking notes (I use Obsidian extensively).

Publications

Education

Secondary Education ▸

Experience

Projects

Explore my technical projects on GitHub.

Membership

Extracurriculars ▸