Nadja Herger is a Data Scientist at Thomson Reuters Labs, based in Switzerland. She is primarily focusing on Deep Learning PoCs within the Labs, where she is working on applied NLP projects in the legal and news domains, applying her skills to text classification, metadata extraction, and summarization tasks. Before joining Thomson Reuters, she obtained her Ph.D. in Climate Science from the University of New South Wales, Australia. She has successfully made the transition from working with Spatio-temporal data to working with text-based data on the job. Nadja is passionate about education, which is reflected in her ongoing mentorship of students within Thomson Reuters, as well as South African students from previously disadvantaged groups who are aspiring to get into Data Science.
Viktoriia Samatova is a Head of Applied Innovation team of Data Scientists within Reuters Technology division focused on discovering and applying new technologies to enhance Reuters products and improving efficiency of news content production and discoverability. Prior to joining Reuters, Viktoriia spent over 5 years at State Street Bank’s Global Exchange division where she was managing product development of academic and financial industry content ingestion platform, which was the first company-wide product application utilizing emerging technologies of AI and machine learning.
Nina Hristozova is a Data Scientist at Thomson Reuters (TR) Labs. She has a BSc in Computer Science from the University of Glasgow, Scotland. As part of her role at TR she has worked on a wide range of projects applying ML and DL to a variety of NLP problems. Her current focus is on applied summarization of legal text. She is actively engaged with local technology Meetups to spread the love and knowledge for NLP through tech talks and workshops. In her free time she plays volleyball for the local team in Zug, enjoys going to the mountains and SUP in the lakes.
Daniel Whitenack (aka Data Dan) is a PhD trained data scientist who has been developing artificial intelligence applications in the real world for over 10 years. He knows how to see beyond the hype of AI and machine learning to build systems that create business value, and he has taught these skills to 1000’s of developers, data scientists, and engineers all around the world. Now with the AI Classroom event, Data Dan is bringing this knowledge to an live, online learning environment so that you can level up your career from anywhere!
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.
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)
Phoebe Liu is a senior data scientist with expertise in robotics and conversational AI. Previously, she was a robotics researcher in Japan, working in Hiroshi Ishiguro Laboratory at Advanced Telecommunications Research Institute International (ATR), where she also earned her PhD at Osaka University at the same time. She has 6+ years of research experience and 3 years of industry experience. Her research focus includes embodied agent, dialogue system, multimodal data annotation and behavior generation, topic modeling, learning from demonstration, style transfer, explainable AI, proactiveness, personalization, and artificial curiosity and has published many first-author papers in robotics conferences and journals.
Training Conversational Agents on Noisy Data(Demo Talk)
Frank is a Senior Director and a key member of S&P Global Market Intelligence’s Quantamental Research group. His primary focus is to conduct systematic alpha research on global equities with publications on natural language processing, newly discovered stock selection anomalies, event-driven strategies, and industry-specific signals. Frank has master’s degrees in Financial Engineering from UCLA Anderson and in Finance from Boston College Carroll and has undergraduate degrees in Computer Science and Economics from the University of California, Davis.
Tutorial: State-of-the-Art Natural Language Processing with Spark NLP
Workshop: Deep Learning-Driven Text Summarization & Explainability
Talk: Transfer Learning in NLP
Training: Advanced NLP with TensorFlow and PyTorch: LSTMs, Self-attention and Transformers
Workshop: State-of-the-Art Natural Language Processing with Spark NLP
Talk: Topic-Adjusted Visibility Metric for Scientific Articles
Workshop: Deep Learning-Driven Text Summarization & Explainability
Talk: Natural Language Processing: Feature Engineering in the Context of Stock Investing
Tutorial: Building a ML Serving Platform at Scale for Natural Language Processing
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