Projects

This page highlights selected software systems and academic projects that I have developed during my research and graduate studies. These projects demonstrate my interests in artificial intelligence, machine learning, software development, graph learning, and quantum communication.

Research Software & Systems

Mental Health Android Application

Role: Android Application Developer

I contributed to the development and maintenance of an Android application used in our mental health research project. The application allows participants to report their daily mood and anxiety while collecting behavioral data from smartphones. These data are used to study depression treatment outcomes using machine learning models.

My Contributions

• Added new features to the Android application.
• Improved the user interface and application performance.
• Fixed bugs and maintained the application.
• Supported the collection of behavioral data for research.

Technologies

Android • Java • Mobile Development

Clinician Web Portal

Role: Full-Stack Developer

I designed and developed a secure web portal for clinicians involved in our mental health research project. The portal allows clinicians to review participant information, monitor behavioral trends, and manage research data through an easy-to-use web interface.

My Contributions

• Designed and implemented the web interface.
• Developed the back-end system.
• Managed data storage and retrieval.
• Built tools for clinicians to review participant data.

Technologies

Python • Web Development • Database Systems

Professor’s Academic Website

Role: Website Developer and Administrator

I designed, developed, and currently maintain my research advisor’s academic website. The website presents research projects, publications, news, and other academic information for students and collaborators.

My Contributions

• Designed the website structure.
• Updated publications and research information.
• Managed website content and maintenance.
• Improved the organization and presentation of academic information.

Technologies

HTML • CSS • JavaScript

Academic Course Projects

Dynamic Mobility Network Learning

Course: Advanced Computer Networks

This project investigated how Graph Neural Networks and Reinforcement Learning can improve decision making in dynamic mobility networks. The goal was to help communication networks adapt to changing environments by learning mobility patterns over time.

Highlights

• Developed Graph Convolutional Network (GCN) models.
• Applied Reinforcement Learning for adaptive network management.
• Studied Continual Learning for changing network environments.

Technologies

PyTorch • TensorFlow • Graph Neural Networks (GCN) • Reinforcement Learning • Continual Learning

Reinforcement Learning for Autonomous Racing (AWS DeepRacer)

Course: Advanced Machine Learning

In this project, I developed reinforcement learning agents using the AWS DeepRacer simulator. The objective was to train an autonomous racing car by designing reward functions and optimizing reinforcement learning algorithms.

Highlights

• Trained autonomous driving agents.
• Designed custom reward functions.
• Compared different reinforcement learning algorithms.

Technologies

Python • TensorFlow • AWS DeepRacer • PPO • DQN • SAC

Traffic Flow Prediction

Course: Introduction to Machine Learning

This project focused on predicting traffic flow using graph-based deep learning models. I combined Graph Convolutional Networks with spatial-temporal learning methods to improve traffic prediction accuracy.

Highlights

• Built graph-based traffic prediction models.
• Integrated spatial and temporal information.
• Improved prediction performance using deep learning.

Technologies

Python • PyTorch • Graph Convolutional Networks • Spatial-Temporal Modeling

Online Sexual Harassment Detection

Course: Big Data Analysis

This project developed machine learning models to detect online sexual harassment from text data. Transformer-based language models and natural language processing techniques were used to classify harmful online content.

Highlights

• Built text classification models.
• Applied transformer embeddings.
• Performed sentiment and language analysis.

Technologies

Python • Natural Language Processing • Transformers • Machine Learning