Nikko Carlo Yabut Email and Phone Number

With over a decade of experience in ICT consulting and Telecommunications Engineering, I recently earned a Master of Engineering in Artificial Intelligence degree. My expertise lies in integrating AI into IT and Telecommunications to drive smart city development.Passionate about automation and system integrations, I consistently enhance efficiency and connectivity through innovative solutions. Graduating Magna Cum Laude with a BS in Electronics and Communications Engineering, my strong academic and professional background enables me to make significant contributions to the industry.I am committed to leveraging my skills to shape the future of smart cities through innovative AI solutions.---------------------------------------As a Master of Engineering in Artificial Intelligence, I have acquired extensive expertise in both Classical Machine Learning and Advanced AI algorithms, along with proficiency in ML Operations. Key Accomplishments:Classical Machine Learning: Developed a strong foundation in Feature Engineering and Selection, Cross-Validation techniques, Regressions, Kernel Methods, Dimensionality Reduction, Clustering, Density Estimation, Anomaly Detection, Decision Trees and their variations, Gaussian Processes, and Bayesian Optimization. For our final project, we created an 'Explainable AI for Stroke Prediction' system.Advanced AI and MLOps: Gained expertise in Supervised Learning, MLP, CNNs, Transformers, AutoEncoders, GANs, LLMs, and VLMs. As practical applications, we built projects including the development of a custom chatbot trained on a web-scraped dataset using a DGX server, and a voice-activated assistant (akin to Alexa) leveraging an ASR-LLM-TTS pipeline optimized for deployment on a Jetson Orin Nano. Additionally, we deployed a PyTriton server for real-time object detection using Yolov8 on live video streams.Capstone Project: Spearheaded a project to enhance the Open-Vocabulary Object Detection capability of a VLM. The innovative models and methods proposed during this project surpassed the mean Average Precision (mAP) scores of the leading generalist VLMs available at the time, showcasing advancement in the field.