Lak is the Director for Data Analytics and AI Solutions on Google Cloud. His team builds software solutions for business problems using Google Cloud’s data analytics and machine learning products. He founded Google’s Advanced Solutions Lab ML Immersion program and is the author of three O’Reilly books and several Coursera courses. Before Google, Lak was a Director of Data Science at Climate Corporation and a Research Scientist at NOAA. Follow him on Twitter at @lak_gcp, read articles by him on Medium, and see more details at www.vlakshman.com
Practical Machine Learning on Images(Half-Day Training)
Dr. Clair Sullivan is currently a graph data science advocate at Neo4j, working to expand the community of data scientists and machine learning engineers using graphs to solve challenging problems. She received her doctorate degree in nuclear engineering from the University of Michigan in 2002. After that, she began her career in nuclear emergency response at Los Alamos National Laboratory where her research involved signal processing of spectroscopic data. She spent 4 years working in the federal government on related subjects and returned to academic research in 2012 as an assistant professor in the Department of Nuclear, Plasma, and Radiological Engineering at the University of Illinois at Urbana-Champaign. While there, her research focused on using machine learning to analyze the data from large sensor networks. Deciding to focus more on machine learning, she accepted a job at GitHub as a machine learning engineer while maintaining adjunct assistant professor status at the University of Illinois. In 2021 she joined Neo4j as a Graph Data Science Advocate. Additionally, she founded a company, La Neige Analytics, whose purpose is to provide data science expertise to the ski industry. She has authored 4 book chapters, over 20 peer-reviewed papers, and more than 30 conference papers. Dr. Sullivan was the recipient of the DARPA Young Faculty Award in 2014 and the American Nuclear Society’s Mary J. Oestmann Professional Women’s Achievement Award in 2015.
Quanquan Gu is an Assistant Professor of Computer Science at UCLA and the director of the statistical machine learning lab. His research is in the area of artificial intelligence and machine learning, with a focus on developing and analyzing nonconvex optimization algorithms for machine learning to understand large-scale, dynamic, complex, and heterogeneous data and building the theoretical foundations of deep learning and reinforcement learning. He received his Ph.D. degree in Computer Science from the University of Illinois at Urbana-Champaign in 2014. He is a recipient of the Yahoo! Academic Career Enhancement Award, NSF CAREER Award, Simons Berkeley Research Fellowship among other industrial research awards. He leads a team at UCLA using machine learning to forecast the spread of COVID-19 (https://covid19.uclaml.org) and their model has been adopted by the U.S. Centers for Disease Control and Prevention and the California Department of Public Health.
Magnus Ekman is a Director of Architecture at NVIDIA, where he leads an engineering team working on CPU performance and power efficiency. As the deep learning (DL) field exploded in the past few years, fueled by NVIDIA’s GPU technology and CUDA, he found himself in the midst of a company expanding beyond computer graphics and becoming a DL powerhouse. As a part of that journey, he challenged himself to stay up to date with the most recent developments in the field. In collaboration with NVIDIA Deep Learning Institute (DLI) he recently published the book “Learning Deep Learning: Theory and Practice of Neural Networks, Computer Vision, Natural Language Processing, and Transformers Using TensorFlow.”
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.
Adrien Gaidon is the Head of Machine Learning Research at the Toyota Research Institute (TRI) in Los Altos, CA, USA. Adrien’s research focuses on scaling up ML for robot autonomy, spanning Scene and Behavior Understanding, Simulation for Deep Learning, 3D Computer Vision, and Self-Supervised Learning. He received his PhD from Microsoft Research – Inria Paris in 2012, has over 50 publications and patents in ML & Computer Vision (cf. Google Scholar), and his research is used in a variety of domains, including automated driving. You can find him at adriengaidon.com, on linkedin, and Twitter @adnothing.
Andrea Lowe, PhD is the Training and Enablement Engineer at Domino Data Labs where she develops training on topics including overviews of coding in Python, machine learning, Kubernetes, and AWS. She trained over 1000 data scientists and analysts in the last year. She has previously taught courses including Numerical Methods and Data Analytics & Visualization at the University of South Florida and UC Berkeley Extension. Her conference experience includes a deep learning tutorial at PyCon, 2 invited talks, 21 poster presentations, and 4 chair positions.
Karl Weinmeister is a Developer Relations Engineering Manager at Google, based out of Austin, Texas. Karl leads a global team of data science and ML engineering experts in the Developer Advocacy organization, who build technical assets and consult with enterprise customers on Artificial Intelligence and Machine Learning. Karl was a contributor to Proverb, an AI-based crossword puzzle solver, which competed at the American Crossword Puzzle Tournament.
Jennifer Davis, Ph.D. is a Staff Field Data Scientist at Domino Data Labs, where she empowers clients on complex data science projects. She has completed two postdocs in computational and systems biology, trained at a supercomputing center at the University of Texas, Austin, and worked on hundreds of consulting projects with companies ranging from start-ups to the Fortune 100. Jennifer has previously presented topics for Association for Computing Machinery on LSTMs and Natural Language Generation and at conferences across the US and in Italy. Jennifer was part of a panel discussion for an IEEE conference on artificial intelligence in biology and medicine. She has practical experience teaching both corporate classes and at the college level.
Ryan Kasichainula is a data science instructor at Galvanize, Inc, an industry leader in technology education, with data science and software engineering immersive bootcamps. They are also an independent data consultant with experience in the technology, agriculture, energy, and pharmaceutical industries. Ryan enjoys applying data science techniques to a wide variety of domains, and they always have at least one side project in the works, usually in the realm of natural language generation.
Ron Li is a data science instructor and senior data scientist at Galvanize, Inc. Before that, He worked on machine learning and knowledge graphs at the Information Sciences Institute. Ron has published a 4.5-star rating book Essential Statistics for Non-STEM Data Analysts. He has also authored/co-authored several academic papers, taught data science to non-STEM professionals as pro bono service, and gave talks at conferences like PyData.
Adriana Romero Soriano is a research scientist at Facebook AI Research and an adjunct professor at McGill University. Her research focuses on developing models and algorithms that are able to learn from multi-modal data, reason about conceptual relations, and leverage active acquisition strategies to mitigate their uncertainties. The playground of her research has been defined by problems that require inferring full observations from limited sensory data. She completed her postdoctoral studies at Mila, where she was advised by Prof. Yoshua Bengio. Her postdoctoral research revolved around deep learning techniques to tackle biomedical challenges, such as the ones posed by multi-modal data, high dimensional data, and graph-structured data. She received her Ph.D. from the University of Barcelona in 2015 with a thesis on assisting the training of deep neural networks, advised by Dr. Carlo Gatta.
Oliver is a software developer from Hamburg Germany and has been a practitioner for more than 3 decades. He specializes in frontend development and machine learning. He is the author of many video courses and textbooks.
Brian Kent is the founder of The Crosstab Kite, a publication for professional data scientists solving real-world challenges. He writes about survival analysis, data-driven decision-making, data science tools, and big picture trends in statistical modeling. Prior to The Crosstab Kite, Brian worked in the FinTech space as Director of Data Science & Machine Learning at Credit Sesame. Before that, he was a machine learning engineer at Apple, where he worked on autonomous systems, personalized health, and silicon engineering.
Thomas J. Fan is a Senior Software Engineer at Quansight Labs, working to sustain and evolve the PyData open-source ecosystem. He is a maintainer for scikit-learn, an open-source machine learning library written for Python. Previously, he worked at Columbia University, improving the interoperability between scikit-learn and AutoML systems. Thomas holds a Masters in Physics from Stony Brook University and a Masters in Mathematics from New York University.
Introduction to Scikit-learn: Machine Learning in Python(Half-Day Training)
Top speakers and practitioners in Machine Learning and Deep Learning
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Core contributors in the fields of Machine Learning and Deep Learning
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Immerse yourself in talks, tutorials, and workshops on Machine Learning and Deep Learning tools, topics, models and advanced trends
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Meet and connect with the core contributors and top practitioners in the expanding and exciting fields of Machine Learning and Deep Learning
Learn how the rapid rise of intelligent machines is revolutionizing how we make sense of data in the real world and impacting the domains of business, society, healthcare, finance, manufacturing, and more