Software Engineer & AI Researcher
I build production software and research practical, efficient machine learning systems for real-world applications.
With 6+ years of software engineering experience and an MSc in Computer Science, I work across software engineering, artificial intelligence, computer vision and emerging Edge AI systems.
What I'm working on
My work currently sits at the intersection of software engineering, machine learning and efficient AI systems.
AI Research
DermaMNIST
Investigating skin-lesion image classification with deep learning, with particular attention to model behaviour, failure modes and out-of-distribution performance.
Edge AI
Hardware-Aware Deep Learning
MSc research exploring how deep-learning models can be optimized for different edge hardware configurations while balancing accuracy, latency, memory usage and computational efficiency.
Production Engineering
Software & Energy Systems
Building production software at BoxPower, working on applications and systems supporting energy infrastructure and related workflows.
Research
Exploring how machine learning systems can become more reliable, efficient and useful outside the laboratory.
DermaMNIST — Trustworthy Computer Vision
Computer Vision · Trustworthy AI · Deep Learning
DermaMNIST is a research and engineering project exploring deep-learning-based skin-lesion classification using the DermaMNIST dataset. The project goes beyond reporting classification accuracy by investigating model behaviour, limitations and failure cases.
Research Question
"How reliably does a deep-learning classifier perform when confronted with data that differs from the distribution on which it was trained?"
Why it matters
High test accuracy does not necessarily imply that a model is reliable in real-world conditions. Understanding failure modes and distribution shifts is therefore an important part of trustworthy machine learning.
Experiments & Results
Results available in research repository
Research Themes
Research project developed in the context of the OTH Regensburg International Summer School 2026: Trustworthy AI – Machine Learning Meets Blockchain.
Hardware-Aware Optimization of Deep Learning Models for Real-Time Edge Computer Vision
Investigating how deep-learning models can be optimized for deployment across different edge hardware configurations while maintaining an effective balance between predictive accuracy and system-level efficiency.
Accuracy
How much predictive performance is retained?
Latency
How quickly can inference be performed?
Memory
How much memory does the model require?
Energy & Size
How efficiently can inference be performed and how much can it be compressed?
┌─────────────┐
│ Dataset │
└──────┬──────┘
↓
┌─────────────┐
│ Base Model │
└──────┬──────┘
↓
┌─────────────┴─────────────┐
↓ ↓
Model Optimization Baseline Model
↓ ↓
Quantization Benchmark
Pruning Benchmark
Distillation ↓
↓ Compare Results
└─────────────┬─────────────┘
↓
Accuracy / Latency /
Memory / EnergyPotential Techniques
International Summer School — Trustworthy AI
OTH Regensburg · Germany · 2026
Trustworthy AI – Machine Learning Meets Blockchain
Intensive international programme exploring trustworthy artificial intelligence, machine learning and blockchain technologies, with practical work across AI and decentralized systems.
Selected Engineering
Experience
Six-plus years of building software products, platforms and technical systems.
- 2024 - Present
Software Engineer
BoxPower
Building production software for energy infrastructure and related technical workflows, contributing to applications that combine modern web engineering with complex domain-specific systems.
- 2023 - 2024
Frontend Developer
Envisio Live
Building and maintaining modern web applications and user experiences, with a focus on reusable frontend architecture, performance and product usability.
- 2019 - 2022
Frontend Developer
SmatPet Logistics
Developed frontend applications and digital experiences for logistics-related workflows, working across application architecture, UI development and integration with backend services.
- 2018 - 2019
QA Specialist
Blue Skies
Worked in quality assurance, developing an early foundation in software quality, testing, process discipline and defect identification.
- On-going
Master's Degree in Computer Science
University of East London, UK
I graduated after 6 months of studying. I immediately found a job as a front-end developer.
Education
MSc Computer Science
2027University of East London
Artificial Intelligence · Machine Vision · Big Data Analytics · Advanced Software Engineering · Cloud Computing
Selected Results
International Summer School: Trustworthy AI
2026OTH Regensburg
Trustworthy AI · Machine Learning · Computer Vision · Blockchain · 6 ECTS
BSc Agricultural Biotechnology
GraduatedKwame Nkrumah University of Science and Technology
Foundation in science, transitioning into technology and software engineering.
Technical Skills
AI & Machine Learning
Edge & Efficient AI
Software Engineering
Cloud & Infrastructure
Mobile
Research & Data
About me
I'm a software engineer with more than six years of experience building digital products and production software.
My career began in software development, where I developed a strong foundation in frontend engineering, application architecture and product development. Over time, my interests expanded toward artificial intelligence, computer vision and machine learning.
I'm currently completing an MSc in Computer Science, with a growing research focus on trustworthy and efficient AI. My recent work includes deep-learning experiments with DermaMNIST and research into hardware-aware optimization for real-time Edge AI.
I enjoy working at the boundary between software and intelligent systems — taking ideas from research and turning them into systems that can actually be tested, deployed and used.
Current interests: Edge AI · Computer Vision · Efficient Deep Learning · Trustworthy AI · Machine Learning Systems
Beyond the Code
My background is a little unconventional. I started my academic journey in Agricultural Biotechnology before moving into software engineering and eventually into computer science and AI.
That transition shaped how I approach technology: I enjoy crossing disciplines, learning unfamiliar systems and turning theoretical ideas into working software.
Career Transition
Open Source & Experiments
I use GitHub to document experiments, research projects and software engineering work.
View GitHub ProfileLet's build something meaningful.
I'm interested in software engineering, AI/ML systems, computer vision, Edge AI research and opportunities where engineering and intelligent systems intersect.