Charles Hinton

Designed and optimized algorithms for Google's innovative products, focusing on enhancing natural language processing models and automated code generation. Collaborated with cross-functional teams to create AI-driven solutions that are technically sound and user-friendly.

Assisted students with challenging computer science concepts, including dynamic programming and graph theory. Developed coding exercises and supplementary materials to deepen student understanding and make the learning process more interactive.

Assisted students with challenging computer science concepts, including dynamic programming and graph theory, through one-on-one and group sessions. Developed coding exercises and supplementary materials to deepen student understanding and make the learning process more interactive.

Contributed to developing new features for macOS and iOS, specifically improving integration between macOS and third-party applications to enhance performance and user experience. Gained experience with code reviews and the Agile development process, making tangible impacts on the final product.

My doctoral research is centered around creating and fine-tuning machine learning algorithms, especially for tasks like natural language processing and code generation. I’m exploring how AI can be trained to understand and generate code more effectively, which aligns perfectly with Outlier’s focus.

I focused on artificial intelligence and software engineering, diving deep into machine learning, deep learning, and advanced algorithms. My thesis on using reinforcement learning to optimize code compilation was a pivotal moment, showing me the incredible potential of AI in enhancing software development.

My time at Berkeley laid the foundation for my career, giving me hands-on experience with a variety of programming languages and a solid grasp of core computer science principles. I completed several key projects, each one deepening my understanding of software development and computational theory.