Research Projects

🖥️ Featured Projects

1. Automated Multi-Task Machine Learning for Ridership Prediction of Public Transportation Routes

• Proposed an efficient affinity-driven MTL framework that pre-selects task groups to maximize MTL performance gains.
• Achieved 15% lower prediction error than single-task models and ~7% MSE improvement over baseline MTL grouping methods. Validated on real-world public transit ridership data, demonstrating consistent performance gains across task groups.

2. Data-Driven Energy Optimization for Multi-Modal Transit Agencies Project Details

• Designed and developed a framework for predicting energy consumption for various transit vehicle types using multi-month sensor data, outperforming classic learning algorithms (decision trees and linear regression) by ~ 33% in MSE reduction.
• Achieved <5% prediction error for 6-hour trips, by aggregating sample-level predictions across time-series segments.
• Built a decision tree–based map-matching module linking noisy GPS to road geometry with 90% accuracy, enabling elevation and distance features.
• Models trained on multi-month, multi-vehicle telemetry data (6 vehicle types across 8 months); prediction outcomes informed energy-aware routing and scheduling strategies for transit agencies in Tennessee.
• Integrated into a distributed ML pipeline for real-time energy prediction

3. Neural Architecture and Feature Search for Predicting Public Transit Ridership Project Details

• Developed a neural architecture and feature search framework for route-specific ridership prediction using Automatic Passenger Count and weather data - jointly optimizing prediction error and model complexity
• Evaluated on real-world transit ridership data across ten routes, showing that route-specific neural network architectures and features outperform generally optimized models in prediction accuracy (9% lower error).
• Architectures optimized per route-task consistently yielded the best results compared to hand-designed baselines, demonstrating the benefit of customizing both model complexity and feature set to task-specific patterns in real-world transit systems.

4. Impact of COVID-19 on Public Transit Accessibility and Ridership

• Analyzed 3.3M+ transit boarding events from Nashville and Chattanooga by integrating farebox, GPS, and telemetry data; performed temporal, spatial, and socio-economic analyses to assess ridership declines across demographics, locations, and time-of-day patterns.
• Identified persistent COVID-19 impacts on transit accessibility, informing transit agencies’ strategies for equitable service restoration; findings published in Transportation Research Record.
• Findings informed adaptive strategies for transit agencies to prioritize equitable service restoration and plan for future disruptions.

5. Smart Contract Security Discussions

• Investigated security concerns and awareness in the smart contract developer community by analyzing Q&A discussions, blog posts, and associated source code from multiple platforms.

6. Bug-Bounty Ecosystem Analysis

• Examined Chromium bug bounty program data, including activity logs and rules descriptions, to characterize participant incentives, behaviors, and vulnerability reporting processes.

📊 Relevant Skills

• Languages: Python, Java (basic), C++ (basic), SQL, Bash
• ML Libraries: PyTorch, TensorFlow, Keras, Scikit-learn, NLTK, SciPy
• Data Analysis Libraries: Matplotlib, NLTK, Scipy, Pandas, Numpy,
• Development Tools: Git, Docker, Jupyter, VSCode, AWS (basic)
• Research Methods: Automated Machine Learning, Multi-Task Learning, Deep Learning, Neural Architecture Search, Exploratory Data Analysis, Statistical Modeling, Energy Optimization, Transit Ridership Prediction, Smart Contract Security