Undergraduate Honours Projects

I supervise undergraduate honours projects in machine learning, reinforcement learning, and robotics. These projects are designed to provide students with hands-on experience in modern AI research, including problem formulation, implementation, evaluation, and scientific communication.

Students will gain experience with PyTorch, reinforcement learning frameworks, robotics platforms, and real-world experimental design.

Available Project Areas

Reinforcement Learning (RL)

Single-Agent Reinforcement Learning
Develop and evaluate RL algorithms in simulated environments. Projects may explore:

• Sample efficiency and exploration strategies
• Safe and constrained decision-making
• Goal-conditioned learning
• Learning under limited or noisy data

Multi-Agent Reinforcement Learning
Study how multiple agents learn to cooperate or compete. Possible directions:

• Coordination and communication between agents
• Decentralized vs. centralized training
• Emergent behaviors in complex environments

Robotics and Sim-to-Real Learning

Sim-to-Real Transfer with Crazyflie Drones
Develop RL or control methods in simulation and transfer them to real Crazyflie quadrotors. Topics include:

• Domain randomization
• Robust control policies
• Perception-based control

Robotic Manipulation with Kinova Arms
Design learning-based approaches for robotic manipulation tasks:

• Grasping and object interaction
• Vision-based control
• Policy learning from limited data

Mobile Robotics with TurtleBots
Develop navigation and decision-making algorithms for ground robots:

• Autonomous navigation and planning
• Sensor-aware decision-making
• Reinforcement learning for robotics

Data-Efficient and Adaptive Learning

Fine-Grained Active Measurement in RL
Investigate how agents can selectively acquire observations when sensing is costly:

• Observation-cost-sensitive decision making
• Adaptive data collection strategies
• Exploration under sensing constraints

Continual Reinforcement Learning
Design agents that can learn across multiple tasks without forgetting:

• Lifelong learning
• Transfer across environments
• Avoiding catastrophic forgetting

Machine Learning for Challenging Data Settings

Learning from Imbalanced Data
Develop deep learning methods that perform well when data is skewed or rare:

• Class imbalance and rare event detection
• Synthetic data generation
• Robust evaluation methods

Privacy-Preserving Health Data Generation
Design generative models for sensitive datasets:

• Privacy-preserving synthetic data
• Evaluation of utility vs. privacy trade-offs
• Applications in healthcare datasets

Expectations

Students undertaking honours projects are expected to:

• Work independently with regular supervision
• Read and understand research papers
• Implement and evaluate machine learning models
• Present results in written and oral form

Strong programming skills (Python) and familiarity with machine learning are expected. Prior experience with deep learning or reinforcement learning is an asset but not required.

How to Apply

If you are interested in an honours project, please email me.

Please include:

• A transcript (unofficial is fine)
• A brief statement of interest
• Any relevant experience (courses, projects, GitHub)