Computer scientist who automates the boring stuff, teaches machines to see, and builds the interfaces around it all.
I can't start anything without a reason. So step one is finding it, and step two is building the thing without losing sight of why it exists.
It all started with nosy questions. How does a TV broadcast end up in your living room? How does a bank know what's in your account? Animations, games, the internet: nearly everything I loved led back to a computer, and I wanted in. I couldn't pick just one corner of it, so I picked computer science.
These days I'm in my third year at Uganda Christian University, majoring in AI and Robotics with data science alongside it, because you can't perfect automation without understanding data. The plan is simple: start each year as a completely different person at everything I do.
How I work. I design the system and make the calls. AI tools write the first draft of the code, then I review it, change it and test it until it does exactly what I meant.
Cameras record far more than anyone can watch. I'm investigating whether computers can flag suspicious behaviour from body movement alone, and whether training on noisy real-world footage helps.
Captions fall apart in noisy rooms, and deaf and hard-of-hearing people pay the price. On a team of six, I'm helping build a system that reads lips alongside audio.
A PRISMA-guided review of how computer vision detects suspicious activity in surveillance video. Yes, I read all the papers.
A philosophy paper on what happens when efficiency becomes an optimisation problem and the thing being optimised includes people.
Cameras record far more than anyone can watch. I'm investigating whether computers can flag suspicious behaviour from body movement alone, and whether training on noisy real-world footage helps.
Captions fall apart in noisy rooms, and deaf and hard-of-hearing people pay the price. On a team of six, I'm helping build a system that reads lips alongside audio.
A PRISMA-guided review of how computer vision detects suspicious activity in surveillance video. Yes, I read all the papers.
A philosophy paper on what happens when efficiency becomes an optimisation problem and the thing being optimised includes people.
Cameras record far more than anyone can watch. I'm investigating whether computers can flag suspicious behaviour from body movement alone, and whether training on noisy real-world footage helps.
Captions fall apart in noisy rooms, and deaf and hard-of-hearing people pay the price. On a team of six, I'm helping build a system that reads lips alongside audio.
A PRISMA-guided review of how computer vision detects suspicious activity in surveillance video. Yes, I read all the papers.
A philosophy paper on what happens when efficiency becomes an optimisation problem and the thing being optimised includes people.
Cameras record far more than anyone can watch. I'm investigating whether computers can flag suspicious behaviour from body movement alone, and whether training on noisy real-world footage helps.
Captions fall apart in noisy rooms, and deaf and hard-of-hearing people pay the price. On a team of six, I'm helping build a system that reads lips alongside audio.
A PRISMA-guided review of how computer vision detects suspicious activity in surveillance video. Yes, I read all the papers.
A philosophy paper on what happens when efficiency becomes an optimisation problem and the thing being optimised includes people.
Cameras record far more than anyone can watch. I'm investigating whether computers can flag suspicious behaviour from body movement alone, and whether training on noisy real-world footage helps.
Captions fall apart in noisy rooms, and deaf and hard-of-hearing people pay the price. On a team of six, I'm helping build a system that reads lips alongside audio.
A PRISMA-guided review of how computer vision detects suspicious activity in surveillance video. Yes, I read all the papers.
A philosophy paper on what happens when efficiency becomes an optimisation problem and the thing being optimised includes people.
Messy data from separate sources goes in. Clean, validated, analysis-ready tables come out. No sighing at spreadsheets required.
I plan my days in a paper notebook. This tool turned 33 pages of those notes into a working Notion system that keeps running even when my computer is off.
On a team of two, I worked on predicting hourly air pollution in Ugandan cities from low-cost sensor data, then the model went live behind a dashboard.
A minimalist daily planner that floats on your Ubuntu desktop, with a translucent glass window, weekly objectives and a focus timer.
A small automation pipeline that gives this site an actual pulse.
An intern check-in app that swaps the paper sign-in sheet for one button, and shows supervisors who's in today.
During my internship at the Uganda Civil Aviation Authority, I built a web-based system for handling passenger complaints, getting passengers, service providers and Authority staff into one workflow.
Same internship, different challenge: a simple Java desktop replica of an airport operational database, for practising how flight records are amended.
A network diagnostics platform, built by a small team at the Africa's Talking hackathon, that helps customers and internet providers find exactly where a connection failed.
A team project that uses computer vision to tell ripe tomatoes from unripe ones.
A Linux desktop client that wants to get along with Google Quick Share.
Machine learning competition entries, from predicting financial stress using mobile money behaviour to predicting biodiversity using climate data.
Posters I designed for the Honours College at Uganda Christian University. Less Python, more Inkscape.
My first 3D project, modelled and rendered in Blender. Everyone starts somewhere.
Messy data from separate sources goes in. Clean, validated, analysis-ready tables come out. No sighing at spreadsheets required.
I plan my days in a paper notebook. This tool turned 33 pages of those notes into a working Notion system that keeps running even when my computer is off.
On a team of two, I worked on predicting hourly air pollution in Ugandan cities from low-cost sensor data, then the model went live behind a dashboard.
A minimalist daily planner that floats on your Ubuntu desktop, with a translucent glass window, weekly objectives and a focus timer.
A small automation pipeline that gives this site an actual pulse.
An intern check-in app that swaps the paper sign-in sheet for one button, and shows supervisors who's in today.
During my internship at the Uganda Civil Aviation Authority, I built a web-based system for handling passenger complaints, getting passengers, service providers and Authority staff into one workflow.
Same internship, different challenge: a simple Java desktop replica of an airport operational database, for practising how flight records are amended.
A network diagnostics platform, built by a small team at the Africa's Talking hackathon, that helps customers and internet providers find exactly where a connection failed.
A team project that uses computer vision to tell ripe tomatoes from unripe ones.
A Linux desktop client that wants to get along with Google Quick Share.
Machine learning competition entries, from predicting financial stress using mobile money behaviour to predicting biodiversity using climate data.
Posters I designed for the Honours College at Uganda Christian University. Less Python, more Inkscape.
My first 3D project, modelled and rendered in Blender. Everyone starts somewhere.
Messy data from separate sources goes in. Clean, validated, analysis-ready tables come out. No sighing at spreadsheets required.
I plan my days in a paper notebook. This tool turned 33 pages of those notes into a working Notion system that keeps running even when my computer is off.
On a team of two, I worked on predicting hourly air pollution in Ugandan cities from low-cost sensor data, then the model went live behind a dashboard.
A minimalist daily planner that floats on your Ubuntu desktop, with a translucent glass window, weekly objectives and a focus timer.
A small automation pipeline that gives this site an actual pulse.
An intern check-in app that swaps the paper sign-in sheet for one button, and shows supervisors who's in today.
During my internship at the Uganda Civil Aviation Authority, I built a web-based system for handling passenger complaints, getting passengers, service providers and Authority staff into one workflow.
Same internship, different challenge: a simple Java desktop replica of an airport operational database, for practising how flight records are amended.
A network diagnostics platform, built by a small team at the Africa's Talking hackathon, that helps customers and internet providers find exactly where a connection failed.
A team project that uses computer vision to tell ripe tomatoes from unripe ones.
A Linux desktop client that wants to get along with Google Quick Share.
Machine learning competition entries, from predicting financial stress using mobile money behaviour to predicting biodiversity using climate data.
Posters I designed for the Honours College at Uganda Christian University. Less Python, more Inkscape.
My first 3D project, modelled and rendered in Blender. Everyone starts somewhere.
Messy data from separate sources goes in. Clean, validated, analysis-ready tables come out. No sighing at spreadsheets required.
I plan my days in a paper notebook. This tool turned 33 pages of those notes into a working Notion system that keeps running even when my computer is off.
On a team of two, I worked on predicting hourly air pollution in Ugandan cities from low-cost sensor data, then the model went live behind a dashboard.
A minimalist daily planner that floats on your Ubuntu desktop, with a translucent glass window, weekly objectives and a focus timer.
A small automation pipeline that gives this site an actual pulse.
An intern check-in app that swaps the paper sign-in sheet for one button, and shows supervisors who's in today.
During my internship at the Uganda Civil Aviation Authority, I built a web-based system for handling passenger complaints, getting passengers, service providers and Authority staff into one workflow.
Same internship, different challenge: a simple Java desktop replica of an airport operational database, for practising how flight records are amended.
A network diagnostics platform, built by a small team at the Africa's Talking hackathon, that helps customers and internet providers find exactly where a connection failed.
A team project that uses computer vision to tell ripe tomatoes from unripe ones.
A Linux desktop client that wants to get along with Google Quick Share.
Machine learning competition entries, from predicting financial stress using mobile money behaviour to predicting biodiversity using climate data.
Posters I designed for the Honours College at Uganda Christian University. Less Python, more Inkscape.
My first 3D project, modelled and rendered in Blender. Everyone starts somewhere.
Messy data from separate sources goes in. Clean, validated, analysis-ready tables come out. No sighing at spreadsheets required.
I plan my days in a paper notebook. This tool turned 33 pages of those notes into a working Notion system that keeps running even when my computer is off.
On a team of two, I worked on predicting hourly air pollution in Ugandan cities from low-cost sensor data, then the model went live behind a dashboard.
A minimalist daily planner that floats on your Ubuntu desktop, with a translucent glass window, weekly objectives and a focus timer.
A small automation pipeline that gives this site an actual pulse.
An intern check-in app that swaps the paper sign-in sheet for one button, and shows supervisors who's in today.
During my internship at the Uganda Civil Aviation Authority, I built a web-based system for handling passenger complaints, getting passengers, service providers and Authority staff into one workflow.
Same internship, different challenge: a simple Java desktop replica of an airport operational database, for practising how flight records are amended.
A network diagnostics platform, built by a small team at the Africa's Talking hackathon, that helps customers and internet providers find exactly where a connection failed.
A team project that uses computer vision to tell ripe tomatoes from unripe ones.
A Linux desktop client that wants to get along with Google Quick Share.
Machine learning competition entries, from predicting financial stress using mobile money behaviour to predicting biodiversity using climate data.
Posters I designed for the Honours College at Uganda Christian University. Less Python, more Inkscape.
My first 3D project, modelled and rendered in Blender. Everyone starts somewhere.
Intelligent systems · In progress · 2026-10 · Team project
Captions fall apart in noisy rooms, and deaf and hard-of-hearing people pay the price. On a team of six, I'm helping build a system that reads lips alongside audio.
Computer vision · Speech recognition · PyTorch · MediaPipe · pytest
Captions fall apart when rooms get noisy, and deaf and hard-of-hearing people pay the price. Noise can't touch lip movements, so they can keep captions usable when the audio gives up.
I'm the model lead. So far I've built the mouth cropping pipeline and the audio encoder, and refined the data on our GPU workstation.
The data is verified and the preprocessing pipeline is tested. Training hasn't started, so there are no results to show yet.