Edward Kim

Edward Kim has a diverse and extensive work experience in the field of machine learning and materials informatics. Edward is currently working as a Manager of Machine Learning at Cohere, where they lead and manages a team of machine learning engineers. Edward'swork primarily focuses on exploratory applications of large language models, including conversational AI, learning from human feedback, and efficient evaluation of these models. Edward is also involved in building open-source libraries, generative AI models, API endpoints, and human-LLM interactive data generation pipelines.

Edward's previous role was as an Adjunct Professor at the University of Toronto, where they specialize in the intersection of large language models and materials informatics. Prior to that, they worked at Xero as a Principal Applied Scientist, leading and managing a team of scientists to create machine learning products serving millions of users worldwide. Edward'sportfolio of ML initiatives spanned across various domains such as machine vision, natural language processing, MLOps, and responsible AI. Edward utilized technologies like Tensorflow, MLflow, and AWS services.

Before their time at Xero, Edward worked as a Senior Data Scientist at Citrine Informatics, where they solved industry challenges in materials and chemicals development using machine learning. Edward also led key internal R&D efforts, improved data science workflows, and co-authored multiple academic communications. Additionally, Edward gained research experience as a Research Assistant at the Massachusetts Institute of Technology, focusing on computational predictive synthesis techniques for inorganic materials using machine learning, natural language processing, and data mining methods. Edward developed a scalable and automated machine learning pipeline using modern deep learning libraries during this role.

Edward's earlier experience includes working as a Machine Learning Consultant at Pfizer, where they designed and implemented various machine learning approaches to solve large-scale business problems. Edward utilized techniques such as recurrent/convolutional neural networks, unsupervised word embeddings, transfer learning, and domain-specific named entity recognition. Edward also wrote code for machine learning microservices, provided data visualizations, and collaborated with agile development practices.

In summary, Edward Kim has a strong background in machine learning, with expertise in large language models, materials informatics, and diverse applications across industries.

Edward Kim holds a Doctor of Philosophy (PhD) degree in Materials Science from the Massachusetts Institute of Technology. Prior to their PhD, they obtained a Bachelor of Science (BS) degree in Nanoscience from the University of Guelph. In addition to their educational background, Edward has also completed the Leadership Training for Results certification provided by Dale Carnegie Training in September 2019.