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Schedule Interview NowBeing part of Softaims has allowed me to see the full spectrum of what technology can achieve when guided by empathy, discipline, and creativity. Each assignment, regardless of size, represents an opportunity to bring clarity to complexity and to turn ambitious ideas into tangible outcomes. I’ve come to realize that successful development isn’t just about writing code—it’s about listening carefully, understanding deeply, and designing thoughtfully. Every client brings unique challenges, and I make it a priority to align my work with their goals, ensuring that the end result is both effective and lasting. Softaims fosters an environment where collaboration is not optional—it’s essential. The collective expertise within the team pushes me to think beyond conventional boundaries, to question, refine, and innovate. I believe that this process of shared learning and experimentation is what makes our solutions resilient and impactful. My ultimate goal is to build technology that feels effortless to use yet powerful in function. I approach every task with the mindset that small details can make a big difference. Through continuous refinement and dedication, I aim to contribute to the kind of work that not only serves today’s needs but anticipates tomorrow’s possibilities.
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The goal of this project was to build and evaluate a neural network framework for classification of lesions in breast ultrasound images. A classification model based on k-Nearest Neighbour (k-NN) algorithm was built to serve as an evaluation baseline. 4 neural network models were then built using the TensorFlow and Keras deep learning libraries; A fully connected neural network, a custom Convolutional Neural Network (CNN) and two transfer learning networks based on retraining InceptionV3 which is a state of the art general purpose image classification CNN. Neural network approaches outperformed the k-NN. The CNN manages to achieve a low false negative rate and high true positive rate hence a high sensitivity of 0.85. The transfer learning networks underperform due to data limitations.
• Client wanted a web app for reserving seats at an event. Users are presented with the seat layout of the event and can then select a table, pick a seat number then reserve it after verifying their identity using their email address and a ticket number. • Backend can dynamically re-allocate a seat if a user selects a different seat. Completed project successfully. Code has been open sourced with the clients permission
My client wanted a simple web app to monitor a micro electrical grid for a hospital in a remote part of uganda. My contribution; • Developed the front and backend for a web app for remotely monitoring micro electricity grids. • Assisted in the design and coding of a representative grid using an Arduino Mega 2560 board, Transformers, Relays, a SIM800L GSM module, current and voltage sensors. Project open sourced with permission
A python backend was required for classifying images in a remote location online. My contribution; • Developed an application that classifies images in a user’s cloud storage into specified categories. • Application downloads images from a dropbox folder then classifies and moves them to new directories which are then uploaded back up to dropbox. Utilized: Python, REST API design, Flask, TensorFlow, Keras, Pillow image processing library, ResNet50 Convolutional Neural Network (CNN) architecture, Drop Box API, microservice architecture, unit and integration testing, token authentication, software design patterns Code has been open sourced with permission
• Led team of 4 developers to build an android mobile phone application for a client who was seeking a Minimum Viable Product to apply for a patent. • Distributed app coding work among team members, led code reviews and built the apps backend. Project completed successfully, patent pending
Bachelor of Science (BS) in Computer engineering
2014-01-01-2018-01-01