Development Team Lead / Accenture Inc.

Jun 2024 -Present

Lead developers, providing mentorship and fostering a collaborative environment to enhance team performance and skill development. Oversee the design, development, and deployment of AI-driven solutions, ensuring adherence to best practices in technical architecture and coding standards. Drive the technical strategy and architecture for scalable and efficient database design and cloud infrastructure, leveraging advanced tools and methodologies. Coordinate cross-functional requirements, working closely with stakeholders to align technical objectives with business goals.

Key Technologies

AI Developer / Accenture Inc.

Aug 2023 - Jun 2024

Developed a web app solution with integrated chat functionality, leveraging Retrieval-Augmented Generation (RAG) for advanced knowledge querying from user-uploaded databases. Responsible for coding, testing, and deploying code in the MS Azure Cloud environment under the Serverless architecture framework. Successfully transitioned the solution from company internal use to client projects, securing paying customers.

Key Technologies

Researcher/Instructor / Institute of Mathematics, University of the Philippines Diliman

Aug 2014 - Jun 2023

Co-authored 5 peer-reviewed academic publications and presented research findings at international conferences. Mentored over 1000 students, receiving an average to strongly positive feedback from 98% of mentees. Organized talks and meetings with faculty members, fostering collaborations with external institutions.

Key Technologies

Ano Ulam?: AI-Powered Filipino Meal Planning Web App

Ano Ulam? is a full-stack AI-powered web app that simplifies meal planning and grocery shopping by suggesting recipes based on available ingredients or generating shopping lists from a chosen dish. Built with a FastAPI backend and a responsive Next.js frontend, it integrates Google's Gemini AI to recommend localized Filipino recipes and cooking instructions, and uses the Cloudinary CDN to fetch relevant dish images. Users can filter by dietary preferences (e.g., vegan, gluten-free), view step-by-step instructions, and reduce food waste through personalized recommendations. The project emphasizes user experience, food accessibility, and intelligent automation in daily cooking.

Key Technologies:

QR Code Generative Imaging: Exploring ControlNet with Stable Diffusion

QR Code Generative Imaging explores the innovative combination of functional QR codes with artistic image generation using the Stable Diffusion neural network model and ControlNet. This project aims to create visually compelling images conditioned on QR code inputs, balancing aesthetics with functionality. By employing the AUTOMATIC1111 Stable Diffusion GUI and leveraging various checkpoints like Dreamshaper and RevAnimated, diverse styles and outputs were tested. Despite initial challenges, including significant processing times, the project successfully generated scannable QR codes embedded within intricate designs. These outputs have potential applications in enhancing the visual appeal of digital and physical presentations, adding a decorative element while maintaining QR code functionality.

Key Technologies:

NBASeriesML: Predicting Game 7 Outcomes in NBA Playoffs

NBA Series ML is a machine learning project that predicts outcomes of NBA Game 7 matchups using logistic regression trained on point differentials from 147 historical games. Initially applied to the 2023 Celtics–Heat series with a 55% Miami win prediction, the model was later extended to analyze the 2025 Oklahoma City Thunder–Indiana Pacers finals, outputting a 71% win probability for OKC. The model is implemented in Python with NumPy and trained via batch gradient descent, using six-game input vectors. This project showcases how data-driven approaches can enhance forecasting and strategy in professional basketball through interpretable statistical modeling.

Key Technologies: