Projects and Interests

Paper Implementations

Isolation Forest

July 2025 | Project Link

We've all learnt and implemented random forest; it's one of the first algorithms we encounter in ML. Most of the time, we strive to find patterns using such algorithms. Another paradigm is anomaly detection — and that’s how I came across Isolation Forests. In this project, I wrote the full Isolation Forest algorithm from scratch using NumPy. The model builds binary trees by recursively splitting randomly sampled data to isolate outliers quickly. I used synthetic 2D data (normal + uniform noise), calculated anomaly scores based on average path lengths, and visualized the decision boundaries.

Gravitational Search Algorithm

June 2025 | Project Link

Implemented GSA on the Heterogeneous Truck Fleet Optimization problem. Inspired by evolutionary algorithms introduced in my AI course, I explored GSA — a nature-inspired metaheuristic. I implemented it on the same truck fleet problem to compare its effectiveness with genetic algorithms and MIP.

Heterogeneous Truck Fleet Optimization

DSE316 | Project Link

• Developed a truck fleet optimization model minimizing delivery cost under capacity and time constraints.
• Implemented a Mixed Integer Program using PuLP and benchmarked it against genetic algorithm solutions.
• Converted real-world logistics into mathematical constraints for optimal allocation.

Projects

NLP Analysis of the Israel-Syria War’s Impact on Gold, Oil, and Financial Markets

Oct–Nov 2024 | ECO318 | Project Link

• Explored how war narratives affect energy and finance sectors using LDA topic modeling on news data.
• Connected macroeconomic reactions to geopolitical sentiment and volatility trends.

Electricity Consumption Forecasting Using Time Series Analysis

Sept–Nov 2024 | DSE315 | Project Link

• Used 15 years of UK electricity data to model seasonal patterns using SARIMA.
• Validated with ADF and Ljung-Box tests to confirm stationarity and independence.
• Compared forecast accuracy against ARIMA and exponential smoothing models.

Traffic Density Estimation and Vehicle Detection Using YOLOv8

Sept–Nov 2024 | DSE312 | Project Link

• Trained YOLOv8 on a custom dataset; achieved mAP@50 of 0.973 for vehicle detection.
• Developed a scalable inference pipeline to count vehicles and estimate traffic density from CCTV frames.

Interests

My primary interest lies in machine learning, especially after taking a core ML course. I also enjoy statistical NLP and am currently reading more about image-to-text systems and VLMs. Lately, I’ve grown fond of operations research and decision sciences, especially at their intersection with ML/AI. I hope to write more on these ideas in my technical blog as I go deeper.