About

Myle Ott is an accomplished NLP Research Engineer with over 10 years of experience in the field of Natural Language Processing. He is currently associated with Facebook AI Research (FAIR) in New York City, where he focuses on machine translation, language modeling, and large-scale NLP projects. Myle is the lead developer for fairseq, which is a popular open-source sequence modeling toolkit. Myle has an impressive educational background, having completed his Ph.D. in Computer Science from Cornell University in 2013. Before that, he obtained his Masters of Engineering in Computer Science from the same institution in 2007. He also holds a Bachelor of Science degree in Computer Science from California State University. Prior to his current role, Myle worked as a Graduate Research Assistant in Natural Language Processing at Cornell University, where he published several papers on detecting and estimating the prevalence of fake online reviews, exploring the efficacy of topic modeling approaches to multi-aspect sentiment analysis tasks, and proposing a structured model for jointly learning discourse and sentiment at varying levels of granularity. Myle also worked as a Summer Research Intern in the Multilingual Natural Language Processing Group at IBM, where he designed and developed an information retrieval system for searching microblog posts on Twitter. He represented IBM in the microblog track of NIST's Text REtrieval Conference (TREC 2012). Myle's technical skills include research scientist, NLP, AI, information retrieval, Pytorch, and sentiment analysis. He has a strong publication record, and a full list of his publications is available on his website at myleott.com. Myle's expertise in NLP and machine learning has made him a valuable asset to the industry, and he continues to make significant contributions to the field through his work at Facebook AI Research.