Mona Diab is a Research Scientist with Facebook AI and she is also a full Professor of CS at the George Washington University where she heads the CARE4Lang NLP Lab. Before joining FB, she led the Lex Conversational AI project within Amazon AWS AI. Her interests span building robust technologies for low resource scenarios with a special interest in Arabic technologies, (mis) information propagation, computational socio-pragmatics, NLG evaluation metrics, and resource creation. She has served the community in several capacities: Elected President of SIGLEX and SIGSemitic. She currently serves as the elected VP-Elect for ACL SIGDAT, the board supporting EMNLP conferences. She has delivered tutorials and organized numerous workshops and panels around Arabic processing. She is a cofounder of CADIM (Consortium on Arabic Dialect Modeling, previously known as Columbia University Arabic Dialects Modeling Group), in 2005, which served as a world renowned reference point on Arabic Language Technologies. Moreover, she helped establish two research trends in NLP, namely computational approaches to Code Switching and Semantic Textual Similarity. She is also a founding member of the *SEM conference, one of the top tier conferences in NLP. She currently serves as the senior area chair for multiple top tier conferences. She has published more than 250 peer reviewed articles.
Kai-Wei Chang is an assistant professor in the Department of Computer Science at the University of California Los Angeles (UCLA). His research interests include designing robust machine learning methods for large and complex data and building fair, reliable, and accountable language processing technologies for social good applications. Dr. Chang has published broadly in natural language processing, machine learning, and artificial intelligence. His research has been covered by news media such as Wires, NPR, and MIT Tech Review. His awards include the Sloan Research Fellowship (2021), the EMNLP Best Long Paper Award (2017), the KDD Best Paper Award (2010), and the Okawa Research Grant Award (2018). Dr. Chang obtained his Ph.D. from the University of Illinois at Urbana-Champaign in 2015 and was a post-doctoral researcher at Microsoft Research in 2016. Additional information is available at http://kwchang.net
Nathan is a Developer Advocate at Hugging Face. He is a machine learning hacker with a passion for both making machine learning more accessible and building compelling products backed by technology. Nathan started as a Data Scientist for PwC, where he built interpretable financial anomaly detection software at scale for Fortune 500 companies. After that, he worked as an Open Source Research Engineer for Pytorch Lightning, a rapidly growing Python package for training PyTorch models on any hardware. Most recently, He was an AI Product Manager at Grid AI, a platform that enables researchers to easily run code in the cloud from their laptops.
Jayeeta is a Data Scientist with 5+ years of industry experience. She recently led six-week NLP workshops in association with Women Who Code, Data Science track. Jayeeta has also been a speaker at International Conference on Machine Learning (ICML 2020), MLConf EU, WomenTech Global Conference, and Data Summit Connect. She works extensively on NLP projects where she gets to explore a lot of state-of-the-art models and build cool products and firmly believes that data is the best storyteller. Recently, Jayeeta joined MediaMath, a leader in the programmatic AdTech domain. Prior to this, she worked at Indellient, Omnicom, Deloitte, and Volvo Group. Jayeeta is also engaged with some amazing organizations to promote and inspire more women to take up STEM. Jayeeta received her Master of Science in Quantitative Methods and Modeling from City University of New York, NY, and Bachelor of Science in Economics and Statistics from West Bengal State University, India.
Website – https://jayeetap.wixsite.com/helloworld
Kumaran Ponnambalam is an AI and Big Data leader with 15+ years of experience. He is currently the Director of AI for Webex Contact Center at Cisco. He focuses on creating robust, scalable AI platforms and models to drive effective customer engagements. In his current and previous roles, he has built data pipelines, ML models, analytics, and integrations around customer engagement. He has also authored several courses on the LinkedIn Learning Platform in Machine Learning and Big Data areas. He holds an MS in Information Technology and advanced certificates in Deep Learning and Data Science.
Leonardo De Marchi holds a Master in Artificial intelligence and has worked as a Data Scientist in the sports world, with clients such as the New York Knicks and Manchester United, and with large social networks, like Justgiving. His previous experience includes Head of Data Science and Analytics in Bumble, the largest dating site with over 500 million users, heading the team through an acquisition and an IPO.
He is also the lead instructor at ideai.io, a company specialized in Reinforcement Learning, Deep Learning and Machine Learning training. He is also a contractor for several companies and for the European Commission, as an expert in AI and Machine Learning. As an author he wrote “Hands On Deep Learning” and he authored an online training course for O’Reilly, Introduction to Reinforcement Learning. In the academic world, he also helped set up the PhD center on Interactive Artificial Intelligence and will take part in the Inner Assessment Board to assign funding to Irish research in AI.
NLP Fundamentals(Full-Day Training)
Zhenya Antić is an NLP consultant and founder of Practical Linguistics Inc. Her projects include document summarization, information extraction, topic modeling and sentiment analysis of consumer reviews, and document similarity. She is the author of the recently published Python Natural Language Processing Cookbook. Zhenya holds a PhD in Linguistics from the University of California Berkeley and a BS in Computer Science from the Massachusetts Institute of Technology.
Introduction to NLP and Topic Modeling(Workshop)
Ravi Ilango is a Lead Data Scientist at a silicon valley startup in stealth mode. He is passionate in developing deployable deep learning solutions. Previously he was at StatesTitle and at Foghorn Systems as a Sr. Data Scientist and has over 10 years of experience at Apple as a data Scientist & at Applied Materials in Supply Chain Program Management. Ravi has a Graduate Certificate in Data Mining & Machine Learning from Stanford and completed a Masters Program in Aeronautics and Production Engineering from IIT Madras. He has a BS in Mechanical Engineering, Madras University.
Yashesh Shroff is a Lead Strategy Planner at Intel where he focuses on enabling the AI ecosystem on heterogeneous compute. Recently, as a product manager, he was responsible for the AI and media/game graphics software ecosystem showcasing Intel’s latest-gen graphics architecture (10nm). He has over 15 years of technical and enabling experience, spanning optical modeling, statistical analysis, and capital equipment supply chain at Intel. He has over 20 published papers and 4 patents. He has a Ph.D. in EECS from UC Berkeley and a joint MBA from UC Berkeley Haas & Columbia Graduate School of Business.
Chris is an author of eBooks, tutorial videos, and example code on a variety of Machine Learning topics–particularly on challenging subjects in NLP. He’s best known for his word2vec blog posts (recommended reading for Stanford’s NLP class), BERT architecture YouTube series, and example code for a variety of BERT applications. Chris earned his B.S. from Stanford in 2006 as a software engineer, and has been working in the areas of computer vision, machine learning, and NLP since 2012. His writing and speaking styles are characterized by levity and positioning himself as a fellow learner rather than an authority. Chris loves to create the tutorials that he wishes he could have read–with an emphasis on thoroughness, while still being easy-to-follow. You’ll often find his simple and colorful illustrations reused around the web. His example code follows the same principles–working code is always a great start, but he further prioritizes explanation and readability, with thoughtful organization and detailed comments at every step.
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