Professor Pieter Abbeel is Director of the Berkeley Robot Learning Lab and Co-Director of the Berkeley Artificial Intelligence (BAIR) Lab. Abbeel’s research strives to build ever more intelligent systems, which has his lab push the frontiers of deep reinforcement learning, deep unsupervised learning, especially as it pertains to robotics. Abbeel’s Intro to AI class has been taken by over 100K students through edX, and his Deep Unsupervised Learning materials are standard references for AI researchers. Abbeel has founded several companies, including Gradescope (AI to help instructors with grading homework, projects and exams) and Covariant (AI for robotic automation of warehouses and factories). He advises many AI and robotics start-ups, and is a frequently sought after speaker worldwide for C-suite sessions on AI future and strategy. Abbeel has received many awards and honors, including ACM Prize, IEEE Fellow, PECASE, NSF-CAREER, ONR-YIP, AFOSR-YIP, Darpa-YFA, TR35, and 10+ best paper awards/finalists. His work is frequently featured in the press, including the New York Times, Wall Street Journal, BBC, Rolling Stone, Wired, and Tech Review.
Steve is the author of the book Exploring GPT-3 from Packt Publishing and the managing director of Dabble Lab, a technology education and services company that helps businesses accelerate learning and adoption of artificial intelligence, blockchain, and other emerging technologies. Steve has been designing and building automation solutions for over 20 years and has consulted on AI and automation projects for companies including Amazon, Google, and Twilio. He also publishes technical tutorials on Dabble Lab’s YouTube channel— one of the most popular educational resources for conversational AI developers—and manages several open-source projects, including the Autopilot CLI, Twilio’s recommended tool for building Autopilot bots.
Stefanie Molin is a data scientist and software engineer at Bloomberg in New York City, where she tackles tough problems in information security, particularly those revolving around anomaly detection, building tools for gathering data, and knowledge sharing. She is also the author of “Hands-On Data Analysis with Pandas,” which is currently in its second edition. She holds a bachelor’s of science degree in operations research from Columbia University’s Fu Foundation School of Engineering and Applied Science. She is currently pursuing a master’s degree in computer science, with a specialization in machine learning, from Georgia Tech. In her free time, she enjoys traveling the world, inventing new recipes, and learning new languages spoken among both people and computers.
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.
Matt Harrison has been using Python since 2000. He runs MetaSnake, a Python and Data Science consultancy and corporate training shop. In the past, he has worked across the domains of search, build management and testing, business intelligence, and
He has presented and taught tutorials at conferences such as Strata, SciPy, SCALE, PyCON, and OSCON as well as local user conferences.
Machine Learning with XGBoost(Workshop)
Frank Zickert is Quantum machine learning engineer and the author of Hands-On Quantum Machine Learning With Python. He teaches quantum machine learning in an accessible way to help those without a degree in math or physics to get started in the field.
In his research, Frank strives to use quantum machine learning to advance the field of knowledge graph-based natural language processing. He is also the Chief Technology Officer of Ihr MPE B+C where he supports medical physicists to provide radiation protection services for clinical customers. Previously he worked at Aperto-An IBM Company and Deutsche Bank.
Frank earned his Ph.D. in Information Systems Development from Goethe University Frankfurt am Main, Germany.
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(Workshop)
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 at conferences 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. Jennifer enjoys working with clients and helping them achieve their goals.
Serg Masís has been at the confluence of the internet, application development, and analytics for the last two decades. Currently, he’s a Climate and Agronomic Data Scientist at Syngenta, a leading agribusiness company with a mission to improve global food security. Before that role, he co-founded a search engine startup, incubated by Harvard Innovation Labs, that combined the power of cloud computing and machine learning with principles in decision-making science to expose users to new places and events efficiently. Whether it pertains to leisure activities, plant diseases, or customer lifetime value, Serg is passionate about providing the often-missing link between data and decision-making. He wrote the bestselling book “Interpretable Machine Learning with Python” and is currently working on a new book titled “DIY AI” for Addison-Wesley for a broader audience of curious developers, makers, and hackers.
Jess Garcia is the Founder of the global Cybersecurity/DFIR firm One eSecurity and a Senior Instructor with the SANS Institute.
During his 25 years in the field, Jess has led a myriad of complex multinational investigations for Fortune 500 companies and global organizations. As a SANS Instructor, Jess stands as one of the most prolific and veteran ones, having taught 10+ different highly technical Cybersecurity/DFIR courses in hundreds of conferences world-wide over the last 19 years.
Jess is also an active Cybersecurity/DFIR Researcher. With the mission of bringing Data Science/AI to the DFIR field, Jess launched in 2020 the DS4N6 initiative (www.ds4n6.io), under which he is leading the development of multiple open source tools, standards and analysis platforms for DS/AI+DFIR interoperability.
Leonardo De Marchi holds a Master in Artificial intelligence and has worked as a Data Scientist in the sports world, with clients such as the New York Knicks and Manchester United, and with large social networks, like Justgiving. His previous experience includes Head of Data Science and Analytics in Bumble, the largest dating site with over 500 million users, heading the team through an acquisition and an IPO. He is also the lead instructor at ideai.io, a company specialized in Reinforcement Learning, Deep Learning and Machine Learning training. He is also a contractor for several companies and for the European Commission, as an expert in AI and Machine Learning. As an author he wrote “Hands On Deep Learning” and he authored an online training course for O’Reilly, Introduction to Reinforcement Learning. In the academic world, he also helped set up the PhD center on Interactive Artificial Intelligence and will take part in the Inner Assessment Board to assign funding to Irish research in AI.
Josh Tobin is the founder and CEO of Gantry. Previously, Josh worked as a deep learning & robotics researcher at OpenAI and as a management consultant at McKinsey. He is also the creator of Full Stack Deep Learning (fullstackdeeplearning.com), the first course focused on the emerging engineering discipline of production machine learning. Josh did his PhD in Computer Science at UC Berkeley advised by Pieter Abbeel.
Martin is a Senior Clinical Programmer at BioMarin, where he builds dashboards and tools for making data-informed decisions. Previously, Martin built statistical tools and dashboards for the Diabetes Technology Society, a contributing author for Data Journalism in R on the Northeastern University School of Journalism blog/website, and other volunteer and non-profit organizations. He’s a data journalism instructor for California State University, Chico. Martin holds a graduate degree in Clinical Research and is passionate about data literacy and open source technologies.
Data Visualization with ggplot2(Workshop)
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.
Hugo Bowne-Anderson is a data scientist, writer, educator & podcaster. His interests include promoting data & AI literacy/fluency, helping to spread data skills through organizations and society and doing amateur stand up comedy in NYC. He does many of these at DataCamp, a data science training company educating over 3 million learners worldwide through interactive courses on the use of Python, R, SQL, Git, Bash and Spreadsheets in a data science context. He has spearheaded the development of over 25 courses in DataCamp’s Python curriculum, impacting over 170,000 learners worldwide through my own courses. He hosts and produce the data science podcast DataFramed, in which he uses long-format interviews with working data scientists to delve into what actually happens in the space and what impact it can and does have. He earned PhD in Mathematics from the University of New South Wales, Australia and has conducted biomedical research at the Max Planck Institute in Germany and Yale University, New Haven.
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.
Amita Kapoor, is the author of best-selling books in the field of Artificial Intelligence and Deep Learning. She mentors students at different online platforms such as Udacity and Coursera and is a research and tech advisor to organizations like DeepSight AI Labs and MarkTechPost. She started her academic career in the Department of Electronics, SRCASW, the University of Delhi, where she was an Associate Professor. She has over 20 years of experience in actively researching and teaching neural networks and artificial intelligence at the university level. A DAAD fellow, she has won many accolades with the most recent being Intel AI Spotlight award 2019, Europe. An active researcher, she has more than 50 publications in international journals and conferences. Extremely passionate about using AI for the betterment of society and humanity in general, she is ready to embark on her second innings as a digital nomad.
Peter is a hands-on data science leader with a business focused approach to building data science solutions and telling stories with data. Experienced in translating business problems into data products using advanced statistical techniques and ML to support decision making in a variety of rapid growth environments. Scaled data science solutions for user acquisition, retention, channel optimization, revenue and fraud at Lyft, Alibaba and Citrix. Currently leading Marketing Science for Growth at Nextdoor.
Data Visualization with ggplot2(Workshop)
Clinton Brownley, Ph.D., is a data scientist at Meta (formerly Facebook), where he’s responsible for a variety of analytics projects designed to empower employees to do their best work. Prior to this role, he was a data scientist at WhatsApp, working to improve messaging and VoIP calling performance and reliability. Before WhatsApp, he worked on large-scale infrastructure analytics projects to inform hardware acquisition, maintenance, and data center operations decisions at Facebook.
As an avid student and teacher of modern data analysis and visualization techniques, Clinton teaches a graduate course in interactive data visualization for UC Berkeley’s MIDS program, taught a short-term graduate course in regression analysis and machine learning workshop for NYU’s A3SR program, leads an annual machine learning in Python workshop, and is the author of two books, “Foundations for Analytics with Python” and “Multi-objective Decision Analysis”.
Clinton is a past-president of the San Francisco Bay Area Chapter of the American Statistical Association and is a council member for the Section on Practice of the Institute for Operations Research and the Management Sciences. Clinton received degrees from Carnegie Mellon University and American University.
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.
Jeffrey currently teaches advanced statistics at Master of Information and Data Science at UC Berkeley. His prior roles include VP of Data Science and Engineering at WalmartLabs, the Chief Data Scientist at AllianceBernstein, a global asset management firm that managed over $550 billion as of 2019, Vice President and Head of Data Science at Silicon Valley Data Science, and senior leadership position at Charles Schwab Corporation and KPMG. He has also taught econometrics, statistics, and machine learning at UC Berkeley, Cornell, NYU, University of Pennsylvania, and Virginia Tech. Jeffrey is active in the data science community and often speaks at data science conferences and local events. He has many years of experience in applying a wide range of econometric and machine learning techniques to create analytic solutions for financial institutions, businesses, and policy institutions. Jeffrey holds a Ph.D. and an M.A. in Economics from the University of Pennsylvania and a B.S. in Mathematics and Economics from UCLA.
Julia Lintern currently works as an instructor for the Metis Data Science Flex Program. Previously, she worked as a Data Scientist for the New York Times. Julia began her career as a structures engineer designing repairs for damaged aircraft. Julia holds an MA in applied math from Hunter College, where she focused on visualizations of various numerical methods and discovered a deep appreciation for the combination of mathematics and visualizations. During certain seasons of her career, she has also worked on creative side projects such as Lia Lintern, her own fashion label.
Introduction to Machine Learning (Bootcamp)
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.
A Teaching Associate Professor in the Institute for Advanced Analytics, Dr. Aric LaBarr is passionate about helping people solve challenges using their data. There he helps design the innovative program to prepare a modern work force to wisely communicate and handle a data-driven future at the nation’s first Master of Science in analytics degree program. He teaches courses in predictive modeling, forecasting, simulation, financial analytics, and risk management. Previously, he was Director and Senior Scientist at Elder Research, where he mentored and led a team of data scientists and software engineers. As director of the Raleigh, NC office he worked closely with clients and partners to solve problems in the fields of banking, consumer product goods, healthcare, and government. Dr. LaBarr holds a B.S. in economics, as well as a B.S., M.S., and Ph.D. in statistics — all from NC State University.
Anomaly Detection with Python and R(Training)
Opportunities to form working relationships with some of the world’s top data scientists.
Access to 40+ training sessions and 70+ workshops.
Hands-on experience with the latest frameworks and breakthroughs in data science.
Affordable training–equivalent training at other conferences costs much more.
Professionally prepared learning materials, custom- tailored to each course.
Opportunities to connect with other ambitious, like-minded data scientists.