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Colombia
1 year of experience
I'm a founder and developer based in Colombia, driven by a simple goal: building technology that actually solves hard problems. Whether I'm mapping out complex traffic flows or diving deep into medical AI, I believe the best code is the kind that has a real-world impact. Currently, I'm pouring my heart into OncoTriage, a project designed to help doctors catch lung cancer more accurately. There’s something powerful about turning lines of code into a 'glass cockpit' that can help save lives. I'm a firm believer in building in public, staying grounded in functional results, and pushing the limits of what a small, dedicated team can achieve from a home workstation. When I’m not in the zone with a pair of headphones on, I’m likely looking for the next 'impossible' challenge to tackle.

OncoTriage is a clinical decision support system designed to detect and triage lung nodules in chest X-rays with high reliability. Developed solo for the 2026 AMD Hackathon, it addresses the lack of transparency in automated diagnostics by implementing Bayesian Deep Learning. The system utilizes a Bayesian EfficientNet-B4 backbone. By employing MC Dropout, the model generates a predictive distribution rather than a single point estimate, allowing for the calculation of epistemic uncertainty. This effectively quantifies the model's confidence for every detection. In clinical settings, this allows the system to flag low-confidence predictions for priority human review, reducing the risk of false negatives inherent in standard "black-box" AI. In addition to this, in order to handle the intensive computational requirements of Bayesian inference, OncoTriage is optimized for AMD Instinct MI300X instances. Leveraging AMD’s high-bandwidth memory (HBM3) and the ROCm stack, the system achieves the rapid inference times necessary for real-time clinical triage. The environment is fully containerized via Docker, ensuring seamless scalability across high-performance compute clusters. The Mission: OncoTriage represents a shift toward accountable, transparent AI. By bridging the gap between raw computational power and clinical safety, it provides radiologists with a reliable partner in oncological screening—transforming raw data into uncertainty-aware medical intelligence.
10 May 2026