👨🏾‍💻 I’m currently an undergraduate CS + AI Student and aspiring Researcher/SWE.

🔬 My research interests are in ML/DL, Data Science, Robotics, Computer Vision, Quantitative Research & Finance

👨🏾‍🎓 When I graduate, I would like to pursue a pathway either as a Researcher in Artificial Intelligence/Robotics, Machine Learning/Robotics/Software Engineer, Quantitative Developer, Data Scientist…

📚 I am also interested in assisting others on their path in the world of Computer Science/Artificial Intelligence and Academia.

Selected Experience

👨🏾‍🔬 Professional Experience

Kalvin is an undergraduate Computer Science with Artificial Intelligence student attending UoL in the School of Computer Science and Informatics. Kalvin works as a Machine Learning Software Engineer at LASER, a student-led research group focused on research in Space-based Engineering, supported by the Electronics and Electrical Engineering department at UoL. He is currently working on the Rideshare Experiment, Ground Station Electronics and Postflight Analysis Tracker Softwarein the Electronics/Programming Subsystems Team for PL-26 (Propulsion Unit for LASER’s Spacecraft and Aerospace Research Lite) where he is the joint Project Lead for UoL LASER’S Unity Rise PL-26 Rocket Team 25/26. His latest efforts include working as a part-time Software Avionics Engineer at LASER (Liverpool Association for Space Engineering research) working mainly on LIFTS v1 (LASER’s Integrated Flight Tracking System) as part of LASER’s Unity Rise PULSAR Rocket Team 24/25.

🔬 Research

Kalvin is an active independent researcher with a strong focus in machine learning, deep learning, computer vision and robotics. He maintains a technical blog where he publishes his research findings, summarises academic papers and shares insights and tutorials on advanced topics related to Artifical Intelligence and Computer Science.

• VEGA Aerospace Rideshare Experiment: VEGA (Vegetation Evalation from Ground to Air) is multispectral computer vision rideshare payload to classify vegetation as healthy or not which is designed for embedded systems. Developed for the UKSEDS NRC and Mach competitions, this initiative involves engineering edge-computed, real-time computer vision pipelines capable of custom NDVI (Normalised Difference Vegetation Index) algorithm generation and topographical contour mapping.
• Adversarial Machine Learning: Expanding his expertise into AI security, Kalvin engages in hands-on experimentation with neural network vulnerabilities, successfully developing adversarial attack and defence algorithms.
• Foundational Deep Learning (MNIST): Kalvin implemented a feed-forward neural network (Multilayer Perceptron) from scratch in Python, utilising only core libraries such as NumPy, Matplotlib, Pandas and tqdm. Designed to recognise and classify numerical handwritten digits from the MNIST dataset, this project served to solidify a deep understanding of core mathematics and algorithms, including forward propagation, backpropagation and activation functions (ReLU, Softmax).

To accompany this work, Kalvin’s GitHub repository contains the full codebases, Jupyter Notebooks and corresponding research papers communicating the theory and results of these projects. Feel free to explore the literature and the accompanying repositories.

💻 Projects