Apr 19th - Apr 21st

Natural Language Processing Track

Learn the latest models, advancements, and trends from the top practitioners and researchers behind NLP
Register & Save 75%

NLP has seen rapid advances in recent years. With some of the sharpest minds in data science presenting, get the latest insights, natural language processing training, trends, and discoveries in data science languages, tools, topics – and beyond.

Connect with some of the most innovative people and ideas in the world of data science, while learning first-hand from core practitioners and contributors. Learn about the latest advancements and trends in NLP, including pre-trained models, with use-cases focusing on deep learning, speech-to text, and semantic search.

Some of Our Previous NLP Speakers

Chief Science OfficerHugging Face 🤗

Thomas Wolf, PhD

Thomas leads the Science Team at Huggingface Inc., a Brooklyn-based startup working on Natural Language Generation and Natural Language Understanding.

After graduating from Ecole Polytechnique (Paris, France), he worked on laser-plasma interactions at the BELLA Center of the Lawrence Berkeley National Laboratory (Berkeley, CA). Got accepted for a PhD at MIT (Cambridge, MA) but ended up doing his PhD in Statistical/Quantum physics at Sorbonne University and ESPCI (Paris, France), working on superconducting materials for the French DARPA (DGA) and Thales.

Thomas is interested in Natural Language Processing, Deep Learning, and Computational Linguistics. Much of his research is about Natural Language Generation (mostly) and Natural Language Understanding (as a tool for better generation).

An Introduction to Transfer Learning in NLP and HuggingFace Tools(Workshop)

Dir. | VP | Laureate ProfessorARC Centre for Cognitive Computing in Medical Technologies | ACL | Uni. Melbourne

Tim Baldwin, PhD

Tim Baldwin is a Melbourne Laureate Professor in the School of Computing and
Information Systems, The University of Melbourne, and also Director of the ARC
Centre for Cognitive Computing in Medical Technologies and Vice President of
the Association for Computational Linguistics. His primary research focus is
on natural language processing (NLP), including social media analytics, deep
learning, and computational social science.

Tim completed a BSc(CS/Maths) and BA(Linguistics/Japanese) at The University
of Melbourne in 1995, and an MEng(CS) and PhD(CS) at the Tokyo Institute of
Technology in 1998 and 2001, respectively. Prior to joining The University of
Melbourne in 2004, he was a Senior Research Engineer at the Center for the
Study of Language and Information, Stanford University (2001-2004). His
research has been funded by organisations including the Australia Research
Council, Google, Microsoft, Xerox, ByteDance, SEEK, NTT, and Fujitsu, and has
been featured in MIT Tech Review, IEEE Spectrum, The Times, ABC News, The
Age/SMH, Australian Financial Review, and The Australian. He is the author of
well over 400 peer-reviewed publications across diverse topics in natural
language processing and AI, with over 16,000 citations and an h-index of 60
(Google Scholar), in addition to being an IBM Fellow, ARC Future Fellow, and
the recipient of a number of best paper awards at top conferences.

Fairness in Natural Language Processing(Talk)

C.V. Starr Distinguished Professor of Finance | Visiting ScholarHofstra University

Ahmet K. Karagozoglu, Ph.D.

Dr. Ahmet Karagozoglu is the C.V. Starr Distinguished Professor in Finance & Investment Banking at Hofstra University and a visiting scholar at the Volatility and Risk Institute at New York University. He is also the founding academic director of the Martin B. Greenberg Trading Room at Hofstra’s Zarb School of Business since 2005. He was a visiting research professor at NYU’s Stern School of Business in 2019.
Dr. Karagozoglu’s primary research interests are in the areas of financial derivatives, risk management and market microstructure. His recently published articles focus on idiosyncratic volatility and news; short-sale constraints and information asymmetry; volatility and social media sentiment; credit risk and CDS markets; stress testing and model validation. Currently, he is investigating, with Dr. Nazli S. Alan and Dr. Robert F. Engle, the language complexity of earnings calls transcripts and volatility, using NLP, and the impact of COVID-19 pandemic on volatility in global equity markets.

Language Complexity and Volatility in Financial Markets: Using NLP to Further our Understanding of Information Processing(Track Keynote)

Dean | FellowSchool of Computing Technologies, RMIT University | Australasian Institute of Digital Health

Professor Karin Verspoor

Professor Karin Verspoor is Dean of the School of Computing Technologies at RMIT University in Melbourne, Australia and a Fellow of the Australasian Institute of Digital Health. Karin’s research primarily focuses on the use of artificial intelligence methods to enable biological discovery and clinical decision support, through extraction information from clinical texts and the biomedical literature and machine learning-based modelling. In addition to roles in academia and government research facilities in the US and Australia, Karin spent 5 years in technology start-ups developing AI and NLP systems.

Data Science Supporting Clinical Decision Making: What, Why, How?(Talk)

Chair, Department of StatisticsColumbia University

Tian Zheng, PhD

Tian Zheng is Professor and Department Chair of Statistics at Columbia University. She develops novel methods for exploring and understanding patterns in complex data from different application domains. Her current projects are in the fields of statistical machine learning, spatiotemporal modeling and social network analysis. Professor Zheng’s research has been recognized by the 2008 Outstanding Statistical Application Award from the American Statistical Association (ASA), the Mitchell Prize from ISBA and a Google research award. She became a Fellow of American Statistical Association in 2014. Professor Zheng is the recipient of the 2017 Columbia’s Presidential Award for Outstanding Teaching. From 2018-2020, she has been the chair-elect, chair and past-chair for ASA’s section on Statistical Learning and Data Science.

Topic-Adjusted Visibility Metric for Scientific Articles(Track Keynote)

Group Member | Associate Professor AI-NLP-ML Research Lab | IIT Patna

Dr. Sriparna Saha

Dr. Sriparna Saha is currently an Associate Professor in the Department of Computer Science and Engineering, Indian Institute of Technology Patna, India. She is the author of a book published by Springer-Verlag. She has authored or coauthored more than 290 papers. Her current research interests include deep learning, natural language processing, machine learning, information extraction, text mining, bioinformatics, and multiobjective optimization. Her h-index is 28 and the total citation count of her papers is 4938 (according to Google scholar). She is also a senior member of IEEE. Her name is included in the list of eight leading women scientists in the area of AI in India published by INDIAai which is the National AI Portal of India – a central hub for everything AI in India and beyond, a joint initiative of MeitY, NeGD, and NASSCOM, the website aims to be the trusted content powerhouse in the backdrop of India’s journey to global prominence in Artificial Intelligence. Her name is also included in the list of the top 2% of scientists of their main subfield discipline (Artificial Intelligence and Image Processing), across those that have published at least five papers ( a survey conducted by Stanford University). She is the Associate Editors of IEEE/ACM Transactions on Computational Biology and Bioinformatics, Expert Systems with Applications, PLOS ONE, and IEEE Internet Computing journal. She is the recipient of the Lt Rashi Roy Memorial Gold Medal from the Indian Statistical Institute for outstanding performance in MTech (computer science). She is the recipient of the Google India Women in Engineering Award, 2008, NASI YOUNG SCIENTIST PLATINUM JUBILEE AWARD 2016, BIRD Award 2016, IEI Young Engineers’ Award 2016, SERB WOMEN IN EXCELLENCE AWARD 2018, and SERB Early Career Research Award 2018. She is the recipient of DUO-India fellowship 2020, Humboldt Research Fellowship, Indo-U.S. Fellowship for Women in STEMM (WISTEMM) Women Overseas Fellowship program 2018 and CNRS fellowship.

On Summarization Systems(Talk)

David Talby: NLP for healthcare
CTOJohn Snow Labs

David Talby, PhD

David Talby is a chief technology officer at Pacific AI, helping fast-growing companies apply big data and data science techniques to solve real-world problems in healthcare, life science, and related fields. David has extensive experience in building and operating web-scale data science and business platforms, as well as building world-class, Agile, distributed teams. Previously, he was with Microsoft’s Bing Group, where he led business operations for Bing Shopping in the US and Europe and worked at Amazon both in Seattle and the UK, where he built and ran distributed teams that helped scale Amazon’s financial systems. David holds a Ph.D. in computer science and master’s degrees in both computer science and business administration.

Model Governance: A Checklist for Getting AI Safely to Production(Talk)

Data Science Lead, Google Developer Expert - MLPropulsion Academy Zurich, Google

Dipanjan (DJ) Sarkar

Dipanjan (DJ) Sarkar is a Data Science Lead at Applied Materials, leading advanced analytics efforts around computer vision, natural language processing and deep learning. He is also a Google Developer Expert in Machine Learning. He has consulted and worked with several startups as well as Fortune 500 companies like Intel and Open Source organizations like Red Hat. He primarily works on leveraging data science, machine learning and deep learning to build large- scale intelligent systems. He holds a master of technology degree with specializations in Data Science and Software Engineering. Dipanjan has been an analytics practitioner for several years now, specializing in machine learning, natural language processing, computer vision and deep learning. Having a passion for data science and education, he also acts as an AI Consultant and Mentor at various organizations like Springboard, where he helps people build their skills on areas like Data Science and Machine Learning. Dipanjan is also a published author, having authored several books on R, Python, Machine Learning, Social Media Analytics, Natural Language Processing, and Deep Learning. In his spare time he loves reading, gaming, watching popular sitcoms and football and writing interesting articles on https://medium.com/@dipanzan.sarkar and https://www.linkedin.com/in/dipanzan. He is also a strong supporter of open-source and publishes his code and analyses from his books and articles on GitHub at https://github.com/dipanjanS.

Advanced NLP: From Essentials to Deep Transfer Learning(Full-Day Training)

Research Officer, Digital TechnologiesNational Research Council

Sowmya Vajjala, PhD

Sowmya Vajjala currently works as a researcher in Digital Technologies at National Research Council, Canada’s largest federal research and development organization. She has worked in the area of Natural Language Processing (NLP) over the past decade in various roles – as a software developer, researcher, educator, and a senior data scientist. She recently co-authored a book: “Practical Natural Language Processing: A Comprehensive Guide to Building Real World NLP Systems”, published by O’Reilly Media (June, 2020), which was also translated into Chinese. Her research interests lie in multilingual computing and the relevance of NLP beyond research both in industry practice as well as in other disciplines, through inter-disciplinary research.

How to do NLP When You Don’t Have a Labeled Dataset(Tutorial)

Lead Instructorideai.io

Leonardo De Marchi

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)

Sr. Data ScientistJohn Snow Labs

Veysel Kocaman, PhD

Veysel Kocaman is a Senior Data Scientist and ML Engineer at John Snow Labs and has a decade long industry experience. He is also pursuing his PhD in CS as well as giving lectures at Leiden University (NL) and holds an MS degree in Operations Research from Penn State University. He is affiliated with Google as a Developer Expert in Machine Learning.

Introduction to Spark NLP(Talk)

Lead Data Scientist and Adjunct Lecturer in Data ScienceBrown University, Center for Computation and Visualization

Andras Zsom, PhD

Andras Zsom is a Lead Data Scientist in the Center for Computation and Visualization group at Brown University, Providence, RI. He works with high-level academic administrators to tackle predictive modeling problems, he collaborates with faculty members on data-intensive research projects, and he was the instructor of a data science course offered to the data science master students at Brown.

Missing Data in Supervised Machine Learning(Workshop)


More talks, hands-on workshop and training sessions
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You Will Meet

  • Some of the world’s best data science speakers

  • The brains and authors behind today’s most popular open data science tools, topics, and languages

  • Hundreds of attendees focused on data science

  • Chief Data Scientists

  • Thought leaders working in data science

  • Data Scientists and Analysts

  • Software Developers

  • CEOs, CTOs, CIOs

  • Data Visualization professionals

  • Venture Capitalists and Investors

  • Startup Founders and Executives

  • Attendees from Healthcare, Finance, Education, Business, Intelligence, and other industries

  • Big data and data science innovators

Why Attend?

Several of the best minds and biggest names in data science will be presenting

Network with attendees from leading data science companies to learn how others are tackling similar problems

Gain quality training in the hottest data science topics, tools, and languages

Learn the latest in data science from industry leaders without having to make room in the budget — tickets are surprisingly inexpensive

What You'll Learn

Talks & Workshops on these topics:

Topics

  • Natural Language Processing

  • NLP Transformers

  • Pre-trained Models

  • Text Analytics

  • Natural Language Understanding

  • Sentiment Analysis

  • Natural Language Generation

  • Speech Recognition

  • Named Entity Extraction

Models

  • BERT

  • XLNet

  • GPT-2

  • Transformers

  • Word2Vec

  • Deep Learning Models

  • RNN & LSTM

  • Machine Learning Models

  • ULMFiT

  • Transfer Learning

Tools 

  • Tensorflow 2.0

  • Hugging Face Transformers

  • PyTorch

  • Theano 

  • SpaCy

  • NLTK

  • AllenNLP

  • Stanford CoreNLP

  • Keras

  • FLAIR 

ODSC EAST 2022 | April 19th-21st

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