May 9th - May 11th

Natural Language Processing Track

Learn the latest models, advancements, and trends from the top practitioners and researchers behind NLP
Register your interest for East 2023

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 Past NLP Speakers

Research EngineerHugging Face

Patrick von Platen

Patrick von Platen is a research engineer at Hugging Face and one of the core maintainers of the popular Transformers library. He specializes in speech recognition, encoder-decoder models and long-range sequence modeling. Before joining Hugging Face, Patrick conducted research in speech recognition at Uber AI, Cambridge University, and RWTH Aachen University.

:hugging_face: Transformers & :hugging_face: Datasets for Research and Production(Training)

Machine Learning EngineerDigits

Hannes Hapke

Hannes Hapke works in machine learning at Digits. Prior, he was a senior machine learning scientist for Concur Labs at SAP Concurfor Concur Labs at SAP Concur, where he explored innovative ways to use machine learning to improve the experience of a business traveler. Hannes has also solved machine learning and ML infrastructure problems in various industries including healthcare, retail, recruiting, and renewable energies. He was recognized as a Google Developer Expert for ML and has co-authored two machine learning publications: “Building Machine Learning Pipeline” by O’Reilly Media and “NLP in Action” by Manning Publications.

Keep Your Friends Close, and Keep Your Vectors Closer – Using Similarity-based Machine Learning for FinTech Applications at Digits(Talk)

Director of AIideai.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(Training)

Lead Data ScientistJohn Snow Labs

Veysel Kocaman, PhD

Veysel is a well known thought leader in healthcare NLP and works as a Lead Data Scientist and ML Engineer at John Snow Labs, improving the Spark NLP for the Healthcare library and delivering hands-on projects in Healthcare and Life Science. He is a seasoned data scientist with a strong background in every aspect of data science including NLP, machine learning, deep learning, and big data with over ten years of experience. He’s also pursuing his Ph.D. in ML at Leiden University, Netherlands, and delivers graduate-level lectures in Auto ML and Distributed Data Processing. Veysel has broad consulting experience in Statistics, Data Science, Software Architecture, MLOps, Machine Learning, and AI to several start-ups, boot camps, and companies around the globe. He also speaks at Data Science & AI events, conferences and workshops, and has delivered more than a hundred talks at international as well as national conferences and meetups.

Spark NLP for Healthcare: Modular Approach to Solve Problems at Scale in Healthcare NLP(Half-Day Training)

 

Staff Data ScientistChegg

Sanghamitra Deb, PhD

Sanghamitra Deb is a Staff Data Scientist at Chegg, she works on problems related school and college education to sustain and improve the learning process. Her work involves recommendation systems, computer vision, graph modeling, deep NLP analysis , data pipelines and machine learning. Previously, Sanghamitra was a data scientist at a Accenture where she worked on a wide variety of problems related data modeling, architecture and visual story telling. She is an avid fan of python and has been programming for more than a decade. Trained as an astrophysicist (she holds a PhD in physics) she uses her analytical mind to not only work in a range of domains such as: education, healthcare and recruitment but also in her leadership style. She mentors junior data scientists at her current organization and coaches students from various field to transition into Data Science. Sanghamitra enjoys addressing technical and non-technical audiences at conferences and encourages women into joining tech careers. She is passionate about diversity and has organized Women In Data Science meetups.

Intro to NLP: Text Categorization and Topic Modeling(Workshop)

Technology Research DirectorElsevier Labs

Sujit Pal

Sujit Pal is an applied data scientist at Elsevier Labs, an advanced technology group within the Reed-Elsevier Group of companies. His areas of interests include Semantic Search, Natural Language Processing, Machine Learning and Deep Learning. At Elsevier, he has worked on several machine learning initiatives involving large image and text corpora, and other initiatives around recommendation systems and knowledge graph development. He has co-authored Deep Learning with Keras (https://www.packtpub.com/big-data-and-business-intelligence/deep-learning-keras) and Deep Learning with Tensorflow 2.x and Keras (https://www.packtpub.com/data/deep-learning-with-tensorflow-2-0-and-keras-second-edition), and writes about technology on his blog Salmon Run (https://sujitpal.blogspot.com/).

Transformer Based Approaches to Named Entity Recognition (NER) and Relationship Extraction (RE)(Training)

 

Founder & CEOFusemachines

Sameer Maskey, PhD

Sameer Maskey is the Founder & CEO of Fusemachines Inc, an AI talent platform and services provider. Dr. Maskey has more than 18 years of experience in artificial intelligence, natural language processing, machine learning, data science and is Adjunct Associate Professor at Columbia University. After completing his PhD in Computer Science from Columbia University, he joined IBM Watson Research Center where he invented various statistical algorithms to improve speech-to-speech translation and question answering systems.

Natural Language Processing in Accelerating Business Growth(Talk)

Chief Scientist and Head of AIGot It AI

Chandra Khatri

Chandra Khatri is the Chief Scientist and Head of AI at Got It AI, wherein, his team is transforming the AI space by leveraging state-of-the-art technologies in order to deliver Self-Discovering, Self-Training, and Self-Optimizing products. Under his leadership, Got It AI is democratizing Conversational AI and related ecosystems through automation. Prior to Got-It, Chandra was leading various kinds of applied research projects at Uber AI such as Conversational AI, Multi-modal AI, and Recommendation Systems.

Prior to Uber AI, he was the founding member of the Alexa Prize Competition at Amazon, wherein he was leading the R&D and got the opportunity to significantly advance the field of Conversational AI, particularly Open-domain Dialog Systems, which is considered as the holy-grail of Conversational AI and is one of the open-ended problems in AI. Prior to Alexa AI, he was driving NLP, Deep Learning, and Recommendation Systems related Applied Research at eBay. He graduated from Georgia Tech with a specialization in Deep Learning in 2015 and holds an undergraduate degree from BITS Pilani, India.

His current areas of research include Artificial and General Intelligence, Democratization of AI, Reinforcement Learning, Language Understanding, Conversational AI, Multi-modal and Human-agent Interactions, and Introducing Common Sense within Artificial Agents.

Self-Supervised and Unsupervised Learning for Conversational AI and NLP(Workshop)

Principal Data ScientistOracle

John Peach

A modern polymath, John holds advanced degrees in mechanical engineering, kinesiology and data science, with a focus on solving novel and ambiguous problems. As a senior applied data scientist at Amazon, John worked closely with engineering to create machine learning models to arbitrate chatbot skills, entity resolution, search, and personalization.

As a principal data scientist for Oracle Cloud Infrastructure, he is now defining tooling for data science at scale. John frequently gives talks on best practices and reproducible research. To that end, he has developed an approach to improve validation and reliability by using data unit tests and has pioneered Data Science Design Thinking. He also coordinates SoCal RUG, the largest R meetup group in Southern California.

Tired of Cleaning your Data? Have Confidence in Data with Feature Types(Workshop)

Senior Data ScientistInstitute for Experiential AI at Northeastern University

Benjamin Batorsky, PhD

Ben is a Senior Data Scientist at the Institute for Experiential AI. He obtained his Masters in Public Health (MPH) from Johns Hopkins and his PhD in Policy Analysis from the Pardee RAND Graduate School. Since 2014, he has been working in data science for government, academia and the private sector. His major focus has been on Natural Language Processing (NLP) technology and applications. Throughout his career, he has pursued opportunities to contribute to the larger data science community. He has spoken at data science conferences , taught courses in Data Science, and helped organize the Boston chapter of PyData. He also contributes to volunteer projects applying data science tools for public good.

Neural Named-Entity Recognition pipelines with spaCy(Workshop)

M.S. Data Science at Harvard University, Z by HP Global Data Science Ambassador

Max Urbany

As Max progresses through his Master’s Program, he is particularly interested in intelligent digital accessibility design, along with the ethical analysis of existing predictive models. His passion for creating quality user-centered tools drives him to understand as much as he can about end users while leveraging what data can reveal.

Z by HP Panel Discussion on the Diverse Role of Data Science in Education(Talk)

VP, Enterprise AI / Data Science Solutions at Future Tech

Dan Chaney

Dan Chaney is the VP, Enterprise AI / Data Science Solutions, for Future Tech Enterprise, Inc., an award-winning global IT solutions provider. He oversees all sales, marketing, and technical activities focused on Future Tech’s comprehensive range of AI and data science workstation solutions. Prior to joining Future Tech, Dan spent 20 years at Northrop Grumman, most recently serving as the company’s Enterprise Director of IT Solution Architecture & Engineering. Dan earned his bachelor’s and master’s degrees in communication and computer science from the University of Kentucky. Dan is a Certified Information Systems Security Professional (CISSP) and adjunct instructor for the University of Louisville’s cybersecurity workforce program sponsored by the National Centers of Academic Excellence in Cybersecurity.

Z by HP Panel Discussion on the Diverse Role of Data Science in Education(Talk)

North America Business Development Manager, Z by HP

Kristin Hempstead

Kristin has been with HP for 11 years and is currently the North America business development manager for HP’s data science and artificial intelligence solutions focusing on federal, education, and public sector customers. She has an MBA from University in South Florida with a specialization in Finance and MIS and a BS in Agriculture from the University of Georgia.

Z by HP Panel Discussion on the Diverse Role of Data Science in Education(Talk)

Program Director, Open Source, Data & AI Technologies - Open Source EvangelistIBM

Sepideh Seifzadeh, PhD

Sepideh Seifzadeh is Data Scientist/Machine Learning Engineer with IBM Data Science Elite team, based in San Francisco. After studying her PhD in the field of Computer Engineering at the Center for Pattern Analysis and Machine Intelligence at the University of Waterloo, she started my journey as Big data & Analytics consultant, she then joined IBM as open source solution engineer and was working for IBM Canada for 2 years where she was honored to be awarded “the best of IBM” in 2018. She recently moved from Toronto to San Francisco and joined DSE team. She really enjoy being on the team as she is learning how to apply Data Science in real world applications and get the chance to meet with different customers in different industries and help them in their Data Science journey. Also, she enjoys working with the top talented people in the team and have a chance to learn from them.

Towards Data Scientist – Friendly Natural Language Processing(Workshop)

Senior Cognitive Software DeveloperIBM

Monireh Ebrahimi

Monireh Ebrahimi is a Senior Cognitive Software Developer at IBM’s Center for Open-Source Data and AI Technologies (CODAIT) in San Francisco where she works on Open Source, Data & AI Technologies and she has been awarded “Outstanding Technical Award” in 2021. She has obtained her Ph.D. from Data Semantics (DaSe) lab at Kansas State University with a major focus on Neuro-Symbolic Integration. Throughout her dissertation titled “Generalizable Neuro-Symbolic Reasoners”, she covers her recent works to bridge the neural and symbolic divide in the context of deep deductive reasoners. Her primary research interests include Deep Learning, Knowledge Graphs, Reasoning, Semantic Web, and Natural Language Processing. She is also really interested in applying NLP and Data Science in real world applications and get the chance to work with customers and partners in various industries and help them in their Data Science journey. She has organized several tutorials on “Current and Future Trends of Neural Knowledge Graph Representation and Reasoning” at IJCAI 2020, US2TS 2019, and US2TS 2020 and served as a PC member or reviewer for Artificial Intelligence (NeurIPS, AAAI, IJCAI, ICML, ICLR, JAIR), and Semantic Web conferences (ISWC, ESWC, TheWebCon) and received Most Outstanding Reviewer Award from WWW 2017.

Towards Data Scientist – Friendly Natural Language Processing(Workshop)

Research ScientisFacebook AI Research

Abdel-rahman Mohamed, PhD

Abdelrahman Mohamed (PhD) is a research scientist at Meta AI Research (previously, Facebook AI Research (FAIR)). He was a principal scientist/manager in Amazon Alexa and a researcher in Microsoft Research. Abdelrahman was part of the team that started the Deep Learning revolution in Spoken Language Processing in 2009. He is the recipient of the IEEE Signal Processing Society Best Journal Paper Award for 2016. His current research interest focuses on improving, using, and benchmarking learned speech representations, e.g. HuBERT, Wav2vec 2.0, TextlessNLP, and SUPERB.

Self-supervised Representation Learning for Speech Processing(Workshop)

Senior AnalystCSA Research

Arle Lommel, PhD

Dr. Arle Lommel is a senior analyst with independent market research firm CSA Research. He is a recognized expert in translation quality processes and interoperability standards. Arle’s research focuses on translation technology and the intersection of language and artificial intelligence as well as the value of language in the economy. Born in Alaska, he holds a PhD from Indiana University. Prior to joining CSA Research he worked at the German Research Center for Artificial Intelligence (DFKI) in its Berlin-based language technology lab. In addition to English he speaks fluent Hungarian and passable German, along with bits and pieces of other languages.

How Can We Make Machine Translation Responsive and Responsible?(Business Talk)

Senior Data ScientistDatabricks

Chengyin Eng

Chengyin Eng is a Senior Data Science Consultant on the Machine Learning Practice team at Databricks. She is experienced in developing end-to-end scalable machine learning solutions for cross-functional clients. She also teaches deep learning and ML in production courses and regularly gives talks at universities and conferences. Prior to Databricks, she worked in the life insurance industry, where she contributed to risk modeling and marketing pipelines. She holds an MS in Computer Science from the University of Massachusetts, Amherst. Her Bachelor’s degrees were in Statistics and Environmental Studies from Mount Holyoke College.

Evolution of NLP and its Underpinnings(Tutorial)

Lead Machine Learning ScientistY Meadows

Serdar Cellat, PhD

Serdar Cellat is currently working as a Lead Machine Learning Engineer at Y Meadows, an early stage startup helping customer service teams with NLP solutions. Serdar’s work includes a variety of NLP tasks such as Intent Classification, Named Entity Recognition, Emotion Detection, Topic Modeling, Question and Answering Systems, and Semantic Textual Similarity. Prior to that, Serdar was a Senior Data Scientist at Liberty Mutual Insurance Legal Department where he built NLP systems to detect excessive charges in legal invoices. Serdar has also served as an instructor and mentor for post graduate certificate programs in Machine Learning and Data Science offered by MIT and UT Austin. Serdar holds a PhD degree in Mathematics from Florida State University, and his research focus was on Machine Learning and Optimization.

Emotion Detection with Natural Language Inference(Talk)

VP, AI InnovationWelocalize, Inc.

Olga Beregovaya

A seasoned professional with over 20 years of leadership experience in language technology, NLP, ML and AI data generation and annotation, Olga is the VP, AI Innovation at Welocalize. She is passionate about growing business through driving change and innovation, and an expert in building things from scratch and bringing them to measurable success. Olga has experience on both the buyer and the supplier side, giving her unique perspective around establishing strategic buyer/supplier alliances and designing cost-effective Global Content Lifecycle Programs. She has built and managed global production, engineering and development teams of up to 300 members specializing in NLP and broader ML and AI.

Creating Data to Enable Multilingual AI: What Can Go Wrong and Ways to Mitigate It (Business Talk)

 

EVP AI, Engineering, Technology and Digital Health ResearchCompass Pathways

Gregory Ryslik, PhD

Greg Ryslik is a data science and machine learning executive experienced in leading AI and engineering initiatives ranging across the biotech, healthtech and fintech domains. As senior vice president at COMPASS Pathways his work focuses on developing technology to improve the therapeutic journey for patients with treatment resistant depression. Concurrently, Greg is an assistant professor of practice in the Department of Computer Science & Engineering at Ohio State University as well as an Instructor at Stanford Continuing Studies where he lectures on statistics for artificial intelligence.

Greg started his career as an actuary at PricewaterhouseCoopers in New York City. After his PhD he spent several years in Silicon Valley at companies such as Tesla Motors, Mindstrong Health and Genentech. He is a fellow of the Casualty Actuarial Society, as well as a member of the American Academy of Actuaries. In September 2020, he was named one of Boston Business Journal’s “40 under 40”.

Greg holds a PhD from Yale University in biostatistics, a master’s degree in statistics from Columbia University and an undergraduate degree in mathematics, computer science and finance from Rutgers University.

Utilizing NLP in the Context of COMP360 Psilocybin Therapy for Treatment Resistant Depression(Talk)


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 2023 | May 9th-11th

Register your interest for East 2023
Open Data Science

 

 

 

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