Machine Learning & Deep Learning
Some Current Speakers
Some Previous Speakers
2021 Schedule coming soon
Daniel Voigt Godoy has20+ years experience in developing solutions, programs and models using analytical skills across different industries: software development, government, fintech, retail and mobility. 7+ years experience with data processing, data analysis, machine learning and statistical tools: Python (numpy, scipy, pandas, scikit-learn), Spark, R Studio, MatLab and Statistica. Experience in stochastic simulation and agent-based modeling. Experienced programmer in SQL, Python, Java, R, PowerBuilder, PHP. Strong programming skills and eagerness to learn different languages, frameworks and tools. Solid background in statistics, economics, capital markets, debt management and financial instruments.
Dr. Jiahang Zhong is the leader of the data science team at Zopa, one of the UK’s earliest fintech company. He has broad experience of data science projects in credit risk, operational optimization and marketing, with keen interests in machine learning, optimization algorithms and big data technologies. Prior to Zopa, he worked as a PhD and Postdoctoral researcher on the Large Hadron Collider Project at CERN, with a focus on data analysis, statistics and distributed computing.
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.
Anna Veronika Dorogush graduated from the Faculty of Computational Mathematics and Cybernetics of Lomonosov Moscow State University and from Yandex School of Data Analysis. She used to work at ABBYY, Microsoft, Bing and Google, and has been working at Yandex since 2015, where she currently holds the position of the head of Machine Learning Systems group and is leading the efforts in development of the CatBoost library.
Gabriel is the founder of Scalar Research, a full-service artificial intelligence & data science consulting firm. Scalar helps companies tackle complex business challenges with data-driven solutions leveraging cutting-edge machine learning and advanced analytics. Previously, Gabriel was a B.S. & M.S. student in computer science at Stanford, where he conducted research on computer vision, deep learning, and quantum computing. He’s also spent time at Google, Facebook, startups, and investment firms.
Azin Asgarian is currently an applied research scientist on Georgian’s R&D team where she works with 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 part of the Computer Vision Group where she was working on the intersection of Machine Learning, Transfer Learning, and Computer Vision. Due to her interest in HealthCare, she has worked on various healthcare projects as a research assistant at University Health Network (UHN).
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.
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.
Joaquin Vanschoren is Assistant Professor in Machine Learning at the Eindhoven University of Technology. His research focuses on machine learning, meta-learning, and understanding and automating learning. He founded and leads OpenML.org, an open science platform for machine learning. He received several demo and open data awards, has been tutorial speaker at NeurIPS and ECMLPKDD, and invited speaker at ECDA, StatComp, AutoML@ICML, CiML@NIPS, DEEM@SIGMOD, AutoML@PRICAI, MLOSS@NIPS, and many other occasions. He was general chair at LION 2016, program chair of Discovery Science 2018, demo chair at ECMLPKDD 2013, and he co-organizes the AutoML and meta-learning workshop series at NIPS and ICML. He is also co-editor of the book ’Automatic Machine Learning: Methods, Systems, Challenges’.
Tutorial on Automated Machine Learning(Workshop)
Pieter Gijsbers is a PhD student at Eindhoven University of Technology. His areas of interest are automated machine learning and meta-learning. He is the main author of GAMA, a research-focused open source AutoML tool. While GAMA is easy to use for end-users, it also allows researchers to try different search techniques and visualize the optimization process. He is a co-author of the Open Source AutoML Benchmark. He is also part of the team working on the openml-python package, providing an easy to use Python interface to OpenML.
Automated Machine Learning(Workshop)
Ian is a Chief Data Scientist and has worked in AI and Data Science building teams and high-value IP since 1999. He’s published the 2nd edition of his High-Performance Python book with O’Reilly, speaks and gives keynote talks internationally and co-founded the 11,000 member PyDataLondon community which has delivered 7 years of volunteer-run meetups and conferences to the community
Michael Mitzenmacher is a Professor of Computer Science in the School of Engineering and Applied Sciences at Harvard University. Michael
has authored or co-authored over 200 conference and journal publications on a variety of topics, including algorithms for the Internet, efficient hash-based data structures, erasure and error-correcting codes, power laws, and compression. He is interested in both algorithms for AI applications, and how predictors from AI systems can yield better algorithms with rigorous performance bounds. He is an ACM Fellow and an IEEE Fellow. He has a widely used textbook on randomized algorithms and probabilistic techniques in computer science published by Cambridge University Press.
Michael Mitzenmacher graduated summa cum laude with a B.A. in mathematics and computer science from Harvard in 1991. After studying
mathematics for a year in Cambridge, England, on the Churchill Scholarship, he obtained his Ph. D. in computer science at U.C. Berkeley in 1996. He then worked at Digital Systems Research Center until joining the Harvard faculty in 1999. He served as the chair for computer science from 2010 to 2013 and co-chair in the 2018-2019 academic year.
Algorithms with Predictions(Tutorial)
Stephanie is a Senior Data Scientist at Saturn Cloud, a company making large scale Python easy and accessible to the data community using Dask. Throughout her career, she’s used varied tools to make effective data visualizations, including as a DS Tech Lead at a travel data startup, and as a Senior Data Scientist at Uptake, an industrial data science company. She holds Master’s degrees in sociology and education, and was formerly an adjunct faculty member at DePaul University in Chicago.
With over 10 years of experience in the tech and data science space, I am the Lead Data Scientist at Bumble. I am also the founder of the DataScienceGuidance.com platform which provides advice for people in the field from industry experts. I am a published author in the field of Artificial Intelligence and have hands-on and leadership experience in the delivery of projects using natural language processing, computer vision, and recommender systems, from initial conception through to production.
Professor Michael Huth (Ph.D.) is Co-Founder and CTO of the technology company XAIN and teaches at Imperial College London. His research focuses on Cybersecurity, Cryptography, Mathematical Modeling, as well as security and privacy in Machine Learning. He served as the technical lead of the Harnessing Economic Value theme at PETRAS IoT Cybersecurity Research Hub in the UK. In 2017, he founded XAIN AG together with Leif-Nissen Lundbæk and Felix Hahmann. The Berlin-based company aims to solve the challenge of combining AI with privacy with an emphasis on Federated Learning. XAIN won the first Porsche Innovation Contest and has already worked successfully with Porsche AG, Daimler AG, Deutsche Bahn, and Siemens.
Professor Huth studied Mathematics at TU Darmstadt and obtained his Ph.D. at Tulane University, New Orleans. He worked at TU Darmstadt, Kansas State University and spent a research sabbatical at The University of Oxford. Huth has authored several scientific publications and is an experienced speaker on international stages.
Immerse yourself in talks, tutorials, and workshops on Machine Learning and Deep Learning tools, topics, models, and advanced trends
Expand your network and connect with like- minded attendees to discover how Machine Learning and Deep Learning knowledge can transform not only your data models but also your business and career
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
2021 Schedule coming soon
Top speakers and practitioners in Machine Learning and Deep Learning
Data Scientists and Data Analysts
Software Developers focused on Machine Learning and Deep Learning
Data Science Innovators
CEOs, CTOs, CIOs
Core contributors in the fields of Machine Learning and Deep Learning
Data Science Enthusiasts