Oct 31st – Nov 3rd, 2023
Natural Language Processing Track
Learn the latest models, advancements, and trends from the top practitioners and researchers behind NLP
Natural Language Processing Track
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
Steven Bird, PhD
Steven Bird has spent much of his career pursuing scalable computational methods for capturing, enriching, and analysing data from endangered languages, drawing on fieldwork in West Africa, South America, and Melanesia. Over the past 5 years he has shifted to working from the ground up with remote Aboriginal communities in Australia, supporting language learning and development in an Aboriginal ranger program, school, and arts centre. He is a co-developer of the Natural Language Toolkit (NLTK), co-founder of the Open Language Archives Community (OLAC), founder of the ACL Anthology, and director of the Aikuma Project. He has held academic appointments at the universities of Edinburgh, Pennsylvania, UC Berkeley, and Melbourne, and is now professor at Charles Darwin University, in Darwin, Australia.
Malte Pietsch
Malte Pietsch is CTO & Co-Founder at deepset. His current focus is on building deepset Cloud – a SaaS platform for developers to build, deploy and operate modern NLP pipelines. He holds a M.Sc. with honors from TU Munich and conducted research at Carnegie Mellon University. Before founding deepset he worked as a data scientist for multiple startups. He is an active open-source contributor and author of the NLP framework Haystack.
Building Modern Search Pipelines with Haystack, Large Language Models and Hybrid Retrieval(Talk)
Julien Simon
Julien is currently Chief Evangelist at Hugging Face. He’s recently spent 6 years at Amazon Web Services where he was the Global Technical Evangelist for AI & Machine Learning. Prior to joining AWS, Julien served for 10 years as CTO/VP Engineering in large-scale startups.
Hyper-productive NLP with Hugging Face Transformers(Workshop)
Benjamin Batorsky, PhD
Ben is a Senior Data Scientist at the Institute for Experiential AI at Northeastern University. 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 presented his work at conferences, published articles, taught courses in data science and NLP, and is co-organizer of the Boston chapter of PyData. He also contributes to volunteer projects applying data science tools for public good.
Bagging to BERT – A Tour of Applied NLP(Workshop)
Chandra Khatri
Chandra Khatri is the Chief Scientist and Head of AI at Got It AI, wherein, his team is transforming AI space by leveraging state-of-the-art technologies to deliver the world’s first fully autonomous Conversational AI system. Under his leadership, Got It AI is democratizing Conversational AI and related ecosystems through automation. Prior to Got-It, Chandra was leading various AI applied and research groups at Uber, Amazon Alexa and eBay.
At Uber, he was leading Conversational AI, Multi-modal AI, and Recommendation Systems. At Amazon he was the founding member of the Alexa Prize Competition and Alexa AI, 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. And at eBay he was driving NLP, Deep Learning, and Recommendation Systems related applied research projects.
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 and Multi-modal Understanding, and Introducing Common Sense within Artificial Agents.
Self-Supervised and Unsupervised Learning for Conversational AI and NLP(Workshop)
Utkarsh Contractor
Utkarsh Contractor is the VP of AI and Machine Learning at Aisera, where he leads the data science team working on machine learning and artificial intelligence applications in the fields of Natural Language Processing and Computer Vision. As a graduate student at Stanford University, his research focussed on experiments in computer vision, using Deep Neural Networks to analyze surveillance scene imagery and footages. Utkarsh has a decade of industry experience in Computer Vision, NLP and other Machine Learning domains working at companies such as Aisera, LinkedIn and AT&T Labs.
Mosharaf Chowdhury, PhD
Mosharaf Chowdhury is a Morris Wellman associate professor of CSE at the University of Michigan, Ann Arbor, where he leads the SymbioticLab. His work improves application performance and system efficiency of machine learning and big data workloads. He is also building software solutions to monitor and optimize the impact of machine learning systems on energy consumption and data privacy. His group developed Infiniswap, the first scalable software solution for memory disaggregation; Salus, the first software-only GPU sharing system for deep learning; FedScale, the largest federated learning benchmark and a scalable and extensible federated learning engine; and Zeus, the first GPU energy-vs-training performance tradeoff optimizer for DNN training. In the past, Mosharaf did seminal works on coflows and virtual network embedding, and he was a co-creator of Apache Spark. He has received many individual awards and fellowships, thanks to his stellar students and collaborators. His works have received seven paper awards from top venues, including NSDI, OSDI, and ATC, and over 22,000 citations. Mosharaf received his Ph.D. from UC Berkeley in 2015.
Shoili Pal
Shoili Pal is a Data Scientist at The Home Depot where she currently works on Recommendations and Personalization. She has also worked in product data science teams, a finance team and two early stage startups. She holds a Masters in Analytics from Georgia Tech and a Masters in Operations Research from the London School of Economics. In her spare time she reads fantasy and science fiction, builds Lego sets and goes on bike rides.
Ajay Thampi, PhD
Ajay Thampi is a machine learning engineer at Meta where he works on large recommender systems, responsible AI and fairness. He holds a PhD and his research was focused on signal processing and machine learning. He has published papers at leading conferences and journals on reinforcement learning, convex optimization, and classical machine learning techniques applied to 5G cellular networks.
Interpretable AI or How I Learned to Stop Worrying and Trust AI(Talk)
Balaji Lakshminarayanan, PhD
Balaji is currently a Staff Research Scientist at Google Brain working on Machine Learning and its applications. Previously, he was a research scientist at DeepMind for 4.5+ years. Before that, he received a PhD in machine learning from Gatsby Unit, UCL supervised by Yee Whye Teh. His research interests are in scalable, probabilistic machine learning. More recently, he has focused on: – Uncertainty and out-of-distribution robustness in deep learning – Deep generative models including generative adversarial networks (GANs), normalizing flows and variational auto-encoders (VAEs) – Applying probabilistic deep learning ideas to solve challenging real-world problems.
Practical Tutorial on Uncertainty and Out-of-distribution Robustness in Deep Learning(Tutorial)
Jacob Schreiber
Jacob Schreiber is a post-doctoral researcher at the Stanford School of Medicine. As a researcher, he has developed machine learning approaches to integrate thousands of genomics data sets, to design biological sequences with desired characteristics, and has described how statistical pitfalls can be encountered and accounted for in genomics data sets. As an engineer, he has contributed to the community as a core contributor to scikit-learn and as the developer of several machine learning toolkits, including pomegranate for probabilistic modeling and apricot for submodular optimization.
Navigating the Pitfalls of Applying Machine Learning in Practice(Talk)
Daniel Lenton, PhD
Daniel Lenton is the creator of Ivy, which is an open-source framework with an ambitious mission to unify all other ML frameworks. Prior to starting Ivy, Daniel was a PhD student at Imperial College London, where he published research in the areas of machine learning, robotics and computer vision.
Unifying ML With One Line of Code(Tutorial)
Dr. Anju Kambadur
Dr. Prabhanjan (Anju) Kambadur heads the AI Engineering group at Bloomberg. Anju leads a group of 100+ researchers and engineers who build solutions for Bloomberg clients in the areas of machine learning, natural language processing (NLP) and natural language understanding, information extraction, knowledge graphs, question answering, and table understanding. Previously, Anju was a research staff member in the Business Analytics and Mathematical Sciences Department at IBM Research’s Thomas J. Watson Research Center, where he worked on problems in machine learning, such as matrix sketching, genome-wide association studies, temporal causal modeling, and high-performance computing. He received his PhD from Indiana University. Anju has published peer-reviewed articles in the fields of high-performance computing, machine learning, and natural language processing.
Guy Van den Broeck, PhD
Guy Van den Broeck is an Associate Professor and Samueli Fellow at UCLA, in the Computer Science Department, where he directs the Statistical and Relational Artificial Intelligence (StarAI) lab. His research interests are in Machine Learning, Knowledge Representation and Reasoning, and Artificial Intelligence in general. His work has been recognized with best paper awards from key artificial intelligence venues such as UAI, ILP, KR, and AAAI (honorable mention). He also serves as Associate Editor for the Journal of Artificial Intelligence Research (JAIR). Guy is the recipient of an NSF CAREER award, a Sloan Fellowship, and the IJCAI-19 Computers and Thought Award.
Artificial Intelligence Can Learn from Data. But Can It Learn to Reason?(Talk)
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 WEST 2023 - Oct 31st – Nov 3rd
RegisterYou 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
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