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Hello my name is Giancarlo Rinaldini, I am an American Software Engineer and speak Chinese, Spanish and Portuguese. I have a Master of Science in Cybersecurity and a Bachelor of Science in Computer Science with a minor in Chinese from Southern Methodist University. Nevertheless, I still have an insatiable thirst for knowledge and dedication to this field which has paved the way for a promising career in the realm of technology. I posses a sharp and analytical mind, capable of unraveling complex coding puzzles and identifying vulnerabilities in software systems. A deep understanding of computer science principles, algorithms, and programming languages equips me with the skills to tackle challenging cybersecurity issues with finesse and precision.I have experience in various programming languages, including Python, C++, Java, and JavaScript.
I am passionate about natural language processing generally, with specific interests in natural language understanding/generation and information extraction. I currently work with backend systems and developer operations, architecting services with Kubernetes, Docker, and WebAssembly to support Abstract Wikipedia. I worked on Google's Speech and Keyboard team for approximately three years, during which time I built language models for a wide array of human languages. During this time, I honed my skills with and understanding of machine learning algorithms, in particular state-of-the-art neural network research. Before that, I had various internships in which I built full pipelines to extract information like topics, named entities, and relationships between entities. Much of the text with which I worked came from medicine (doctor's notes, etc.); I believe that areas such as medicine hold great promise as socially beneficial uses of NLP technology. In these projects, I primarily relied on older NLP technologies like SVNs, CRFs, HMMs, and Maximum Entropy classifiers. I am currently experimenting with the use of evolutionary programming in NLP, attempting to bridge the gap between human and machine-internal semantic representations.
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