Thomas Wiecki is the Chief Executive Officer at PyMC Labs (www.pymc-labs.io), the world’s first Bayesian consultancy. Prior to that Thomas was the VP of Data Science at Quantopian, where he used probabilistic programming and machine learning to help build the world’s first crowdsourced hedge fund. He is an author of the popular PyMC3 package — a probabilistic programming framework written in Python. He holds a PhD from Brown University.
Bayesian Modeling without the Math(Track Keynote)
Nisha Muktewar is a Research Engineer at Cloudera Fast Forward Labs, where she spends time researching latest ideas in machine learning, builds prototypes that showcase these capabilities when applied to real-world use cases, and advises clients in this space. Prior to joining Cloudera, she worked as a Manager in Deloitte’s Actuarial & Modeling practice leading teams in designing, building, and implementing predictive modeling solutions for pricing, consumer behavior, marketing mix, and customer segmentation use cases for insurance and retail/consumer businesses.
Guglielmo is part of MSD (Merck & Co. in North America). He is currently busy unlocking business value through Computer Vision and other ML/DL/AI applications to the biotech manufacturing space. He has an extensive background in Software Engineering and Data Science across other big organizations including IBM, Optum and FAO of the UN in diverse contexts (such as Healthcare, DevOps, Cyber Security). Guglielmo has been recognized as DataOps Champion at the Streamsets DataOps Summit 2019 and awarded as one of the Top 50 Tech Visionaries at the 2019 Dubai Intercon Conference. Since 2018 he is also an international speaker (almost 30 international conferences so far, including Big Things 2019 and 2020, Spark+AI Summit 2019, Annual Cyber Security and AI Summit 2019 and 2020), author of a tech book on distributed Deep Learning with Apache Spark and planning about a second tech book which should be probably released at the end of 2021.
Frank Hutter is a Full Professor for Machine Learning at the Computer Science Department of the University of Freiburg (Germany), as well as Chief Expert AutoML at the Bosch Center for Artificial Intelligence.
Frank holds a PhD from the University of British Columbia (UBC, 2009) and a Diplom (eq. MSc) from TU Darmstadt (2004). He received the 2010 CAIAC doctoral dissertation award for the best thesis in AI in Canada, and with his coauthors, several best paper awards and prizes in international competitions on machine learning, SAT solving, and AI planning. He is the recipient of a 2013 Emmy Noether Fellowship, a 2016 ERC Starting Grant, a 2018 Google Faculty Research Award, a 2020 ERC PoC Award, and he is a Fellow of ELLIS. Frank’s recent research focuses on automated machine learning (AutoML), where he co-organized the ICML workshop series on AutoML every year since its inception in 2014, co-authored the prominent AutoML tools Auto-WEKA, Auto-sklearn, and Auto-PyTorch, won the first two AutoML challenges with his team, co-authored the first book on AutoML, worked extensively on efficient hyperparameter optimization and neural architecture search, and gave a NeurIPS 2018 tutorial with over 3000 attendees.
Lucas is a cloud native expert who’s serving the CNCF community in lead positions for 5 years. He’s awarded Top CNCF Ambassador 2017 with Sarah Novotny. Lucas was a co-lead for SIG Cluster Lifecycle, co-created kubeadm, Cluster API & Weave Ignite and ported Kubernetes to ARM. Lucas runs 3 meetups, and has spoken at 7 KubeCons. Most recently, Lucas co-created Racklet besides his university studies.
Azin is currently an applied research scientist on Georgian’s R&D team where she works with Georgian’s portfolio companies to help adopt applied research techniques to overcome business challenges. Azin holds a Master of Science in Computer Science from University of Toronto and a Bachelor of Computer Science from University of Tehran. Prior to joining Georgian, Azin was a research assistant at the University of Toronto and University Health Network (UHN) where she was working on the intersection of Machine Learning, Transfer Learning, and Computer Vision.
Daniel is a data scientist, teacher, and author of “Deep Learning with PyTorch Step-by-Step: A Beginner’s Guide”.
He has been teaching machine learning and distributed computing technologies at Data Science Retreat, the longest-running Berlin-based bootcamp, since 2016, helping more than 150 students advance their careers. Daniel is also the main contributor of two Python packages: HandySpark and DeepReplay.
His professional background includes 20 years of experience working for companies in several industries: banking, government, fintech, retail, and mobility.
Daria Stepanova is a research scientist at Bosch Center for Artificial Intelligence. Her research interests include Knowledge Representation and Reasoning with a special focus on the automatic acquisition of rules from structured knowledge. Previously Daria was a senior researcher at Max Plank Institute for Informatics (Germany), where she was heading a group on Semantic Data. Daria got her diploma degree in Applied Computer Science from the Department of Mathematics and Mechanics of St. Petersburg State University (Russia) in 2010 and a PhD in Computational Logic from Vienna University of Technology (Austria) in 2015. Before starting her PhD she worked as a visiting researcher at the School of Computing Science at Newcastle University (UK) in an industrially-oriented project.
Franziska Kirschner is the Research and Product Lead of Car Inspection at Tractable. Her team uses machine learning to automate car damage appraisal across a range of applications. Her research interests include domain adaptation, and multitask- and multi-instance learning. In a previous life, she did a PhD in Physics at the University of Oxford. In her spare time, she enjoys cooking and making bad puns.
Mikhail is a Research Staff Member at IBM Research and MIT-IBM Watson AI Lab in Cambridge, Massachusetts. His research interests are Model fusion and federated learning; Algorithmic fairness; Applications of optimal transport in machine learning; Bayesian (nonparametric) modeling and inference. Before joining IBM, he completed Ph.D. in Statistics at the University of Michigan, where he worked with Long Nguyen. He received his bachelor’s degree in applied mathematics and physics from the Moscow Institute of Physics and Technology.
Jaime Buelta has been a professional programmer since 2002 and a full-time Python developer since 2010. He has developed software for a variety of fields, focusing, in the last 10 years, on developing web services in Python in the gaming and finance industries. He is a strong proponent of automating everything to make computers do most of the heavy lifting, so humans can focus on the important stuff. He published his first book, “Python Automation Cookbook”, in 2018 (now updated recently with an extended second edition), followed by “Hands-On Docker for Microservices with Python” the following year. He is currently working as Software Architect in Double Yard in Dublin, Ireland, and is a regular speaker at PyCon Ireland.
Basic Python for Data Processing(Half-Day Training)
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