Hi there! I'm Rafael Martínez, a Master’s student at Hult International Business School and an aspiring Data Analyst with a love for all things tech. I've had the chance to work on some exciting projects, like developing machine learning algorithms and leading OCR projects that make data easier to work with. I enjoy collaborating with global teams to create better digital experiences, and I'm always looking for new challenges to grow my skills. When I’m not coding or exploring the latest technologies, you’ll find me on the padel court or exploring new tools to add to my toolkit. Feel free to reach out if you want to chat about AI, tech, or anything in between!
Developed machine learning algorithms for real-time event detection and implemented OCR solutions, focusing on Deep Learning techniques. Collaborated on technical design and optimized data pipelines for improved performance.
Collaborated with a global AI team to enhance models and improve digital experiences across languages. Managed diverse data types, ensured quality, and contributed to system design while handling tasks independently.
As an AI Engineer in Real-Time Image and Event Recognition with the GATV research group, I developed computer vision models to identify and label buildings in live video streams and implemented OCR solutions for automatic bib number recognition at sports events. This role sharpened my skills in deep learning, data pipeline optimization, and technical system integration. Collaborating on the European NEMO project, I gained valuable experience in working with cutting-edge AI technologies, enhancing my problem-solving abilities and reinforcing the importance of teamwork in projects.
As a Online Data Analyst at TELUS International, I collaborated with a global AI community to enhance machine learning models, particularly in Natural Language Processing. My work involved managing diverse data types in multiple languages, ensuring data accuracy, and improving digital map content used by millions globally. This role honed my research skills, attention to detail, and ability to work flexibly while maintaining high standards. Contributing to system design discussions also deepened my understanding of AI-driven digital experiences.
This study explores real-time race bib detection using YOLOv8 models in 5G/6G edge computing, analyzing performance across datasets, focusing on processing time, efficiency, and accuracy.
Q-Learning, a model-free Reinforcement Learning algorithm, helps agents optimize decisions through trial and error, with real-world applications like warehouse robot path optimization.
Generative AI and LLMs revolutionize business operations by automating customer support, personalizing responses, and integrating seamlessly into data pipelines for efficiency.
San Francisco Bay Area, California
+1 (628) 333-0162
