Meet the Community

We're headed back home to host our first H2O World San Francisco!

Join the greatest minds in AI and data science for this 2-day interactive event packed with deep-dive technical sessions, talks on real-world business use cases and a hands-on training. You'll discover the strategies and insights you need to optimize and transform your business and prepare for the wave of Explainable AI.

H2O World San Francisco is a must-attend event whether you're a newbie getting your toes wet, or an H2O power user. You'll get to network with industry trailblazers and peers that are shaping the future of AI and machine learning.

Speakers

SriSatish Ambati
SriSatish Ambati Co-Founder & CEO, H2O.ai
SriSatish Ambati

Bio: SriSatish Ambati is the CEO and Co-Founder of H2O.ai – makers of H2O, the leading open source machine learning platform and Driverless AI, which speeds up data science workflows by automating feature engineering, model tuning, ensembling and model deployment.

Sri is known for envisioning killer apps in fast evolving spaces and assembling stellar teams towards productizing that vision. A regular speaker in the Big Data, NoSQL and Java circuit, Sri leaves a trail @srisatish.

Linkedin: https://www.linkedin.com/in/srisatishambati/

cancel
Tanya Berger Wolf
Tanya Berger Wolf Co-Founder and Director, Wildbook
Tanya Berger Wolf
AI and Humans Combatting Extinction Together

Abstract: Photographs, taken by field scientists, tourists, automated cameras, and incidental photographers, are the most abundant source of data on wildlife today. Wildbook, a project of tech for conservation non-profit Wild Me, is an autonomous computational system that starts from massive collections of images and, by detecting various species of animals and identifying individuals, combined with sophisticated data management, turns them into high resolution information database, enabling scientific inquiry, conservation, and citizen science. We have built Wildbooks for over 20 species of animals, including whales (flukebook.org), sharks (whaleshark.org), giraffes (giraffespotter.org), and, with H2O.ai's help, working on elephants. In January 2016, Wildbook enabled the first ever full species (the endangered Grevy's zebra) census using photographs taken by ordinary citizens in Kenya.The resulting numbers are now the official species census used by IUCN Red List and we repeated the effort in 2018, becoming the first certified census from an outside organization accepted by the Kenyan government. Wildbook is becoming the data foundation for wildlife science, conservation, and policy. Read more: Fast Company(TM) article

Bio: Berger-Wolf is a Professor of Computer Science at UIC, where she heads the Computational Population Biology Lab, and a co-founder of machine learning for wildlife conservation tech Wildbook, a project of WildMe.org, which she directs. Berger-Wolf holds a Ph.D. from the University of Illinois at Urbana-Champaign. She has received numerous awards for her research and mentoring, including the US National Science Foundation CAREER Award, Association for Women in Science Chicago Innovator Award, and the UIC Mentor of the Year Award.

Linkedin: https://www.linkedin.com/in/tanyabw/

cancel
Gary Rapsey
Dan Rubenstein Class of 1877 Professor of Zoology, EEB; Director, Program in Environmental Studies
Dan Rubenstein

Abstract: Photographs, taken by field scientists, tourists, automated cameras, and incidental photographers, are the most abundant source of data on wildlife today. Wildbook, a project of tech for conservation non-profit Wild Me, is an autonomous computational system that starts from massive collections of images and, by detecting various species of animals and identifying individuals, combined with sophisticated data management, turns them into high resolution information database, enabling scientific inquiry, conservation, and citizen science. We have built Wildbooks for over 20 species of animals, including whales (flukebook.org), sharks (whaleshark.org), giraffes (giraffespotter.org), and, with H2O.ai's help, working on elephants. In January 2016, Wildbook enabled the first ever full species (the endangered Grevy's zebra) census using photographs taken by ordinary citizens in Kenya.The resulting numbers are now the official species census used by IUCN Red List and we repeated the effort in 2018, becoming the first certified census from an outside organization accepted by the Kenyan government. Wildbook is becoming the data foundation for wildlife science, conservation, and policy. Read more: Fast Company(TM) article

Linkedin: https://www.linkedin.com/in/daniel-rubenstein-4676714/

cancel
Paul Zikopoulos
Paul Zikopoulos Vice President - Big Data Cognitive Systems, IBM
Paul Zikopoulos
Your AI Needs an IA (Your Artifical Intelligence Needs an Infrastructure Agenda)

The poet A. R. Ammons once wrote, "A word too much repeated falls out of being" and although the term AI (encompassing machine & deep learning domains) for sure feels "too much repeated',' it's not about to fall "out of being" any time soon. In this session you'll hear about how your artificial intelligence (AI) needs an infrastructure agenda (IA). As business hopes move from phases rennovaton to those of innovation, there's a massive problem: talent, trust, and time. All of these friction points to realising the true value of AI. In this sesion, which include live demos and won't be your standards set of powerpoint slideware, you'll hear how the things H20 and IBM are partnering around to empower the many on the journey to AI.

Bio: Paul C. Zikopoulos, is the VP of Cognitive BigData Systems at IBM. He’s an award winning writer and speaker who has been consulted on the topic of BigData by the popular TV show “60 Minutes,” advises various universities on their graduate analytics programs, and named to over a dozen “Experts to Follow” lists in social media. You’ll also find Paul taking a very active role around Women in Technology (including a seated board member for Women 2.0 - a global brand for women in tech and entrepreneurship that works to close the gender gaps of tech companies). Paul has written 19 books and over 360 articles on data. He doesn’t think NoSQL is something you put on a resume if you don’t have SQL skills and he knows JSON isn’t a person in his department. Ultimately, Paul is trying to figure out the world according to Chloë—his daughter, whom he notes didn’t come with a handbook and is more complex than the topic of BigData itself, but more fun too. The rest of the bio? It would be BLAH BLAH, BLAH, so find him on Twitter @BigData_paulz

Linkedin: https://www.linkedin.com/in/paul-zikopoulos-4323607/

cancel
Dean Stoecker
Dean Stoecker Chairman and Chief Executive Officer, Alteryx
Dean Stoecker

Bio:Dean Stoecker is Chairman and Chief Executive Officer, and a founding partner of Alteryx, revolutionizing business through data science and analytics. Dean's leadership and motivational skills, along with his ability to create, communicate and realize a vision, are a driving force behind bringing back the thrill of solving to analysts and data scientists across the globe.

Linkedin: https://www.linkedin.com/in/dean-stoecker-298010a/

cancel
Naren Gupta
Naren Gupta Co-Founder and Managing Director, Nexus Venture Partners
Erin Ledell
Erin Ledell Chief Machine Learning Scientist, H2O.ai
Erin Ledell

Bio: .Erin is the Chief Machine Learning Scientist at H2O.ai. Erin has a Ph.D. in Biostatistics with a Designated Emphasis in Computational Science and Engineering from University of California, Berkeley. Her research focuses on automatic machine learning, ensemble machine learning and statistical computing. She also holds a B.S. and M.A. in Mathematics.

Before joining H2O.ai, she was the Principal Data Scientist at Wise.io (acquired by GE Digital in 2016) and Marvin Mobile Security (acquired by Veracode in 2012), and the founder of DataScientific, Inc.

Linkedin: https://www.linkedin.com/in/erin-ledell/

cancel
Arno Candel
Arno Candel CTO, H2O.ai
Arno Candel

Bio: Arno Candel is the Chief Technology Officer at H2O.ai. He is the main committer of H2O-3 and Driverless AI and has been designing and implementing high-performance machine-learning algorithms since 2012. Previously, he spent a decade in supercomputing at ETH and SLAC and collaborated with CERN on next-generation particle accelerators.

Arno holds a PhD and Masters summa cum laude in Physics from ETH Zurich, Switzerland. He was named “2014 Big Data All-Star” by Fortune Magazine and featured by ETH GLOBE in 2015. Follow him on Twitter: @ArnoCandel.

Linkedin: https://www.linkedin.com/in/candel/

cancel
Marios Michalidis
Marios Michalidis Kaggle Grandmaster, Competitive Data Scientist, H2O.ai
Marios Michalidis

Bio: Marios Michailidis is a Competitive Data Scientist at H2O.ai. He holds a Bsc in accounting Finance from the University of Macedonia in Greece, an Msc in Risk Management from the University of Southampton and a PhD in machine learning at from UCL . He has worked in both marketing and credit sectors in the UK Market and has led many analytics’ projects with various themes including: acquisition, retention, recommenders, fraud detection, portfolio optimization and more. He is the creator of KazAnova, a freeware GUI for credit scoring and data mining 100% made in Java as well as is the creator of StackNet Meta-Modelling Framework. In his spare time he loves competing on data science challenges and was ranked 1st out of 500,000 members in the popular Kaggle.com data competition platform. He currently ranks 3rd.

Linkedin: https://www.linkedin.com/in/mariosmichailidis/

cancel
Branden Mmurray
Branden Murray Kaggle Grandmaster, Customer Data Scientist, H2O.ai
Branden Murray

Bio: Kaggle Grandmaster Branden is a customer data scientist at H2O.ai and holds a B.S. in Finance from the San Diego State University. Among his favorite hobbies is participating in predictive analytics competitions primarily on Kaggle.com. Currently, he is ranked 58th among Grandmasters globally and has stood in the top 10% 8 times among all the competitions he participated on Kaggle. Branden is on the team of data scientists from H2O.ai behind PwC’s Audit Innovation of the Year title. They have collectively developed PwC’s Audit.ai - a revolutionary bot that does what humans can’t. Its AI analyses billions of different data points in seconds and applies judgement to detect anomalies in general ledger transactions.

Linkedin: https://www.linkedin.com/in/bmurr26/

cancel
Sudalai Rajkumar
Sudalai Rajkumar Kaggle Grandmaster, Data Scientist, H2O.ai
Sudalai Rajkumar

Bio: Sudalai Rajkumar (aka SRK) is a Senior Data Scientist at H2O.ai Inc, building Driverless AI, an automated machine learning platform. Prior to this, he was with Freshworks, Tiger Analytics and Global Analytics. He has more than 8 years of experience in the DS / ML field and solved a lot of interesting data science problems for various customers across the globe. Apart from his day job, he takes part in various data science competitions to enhance his knowledge and has won several of them. He is a Kaggle Grandmaster in Competitions & Kernels section. He is ranked #1 on Analytics Vidhya platform as well.

Linkedin: https://www.linkedin.com/in/sudalairajkumar/

cancel
Mathias Mueller
Mathias Mueller Kaggle Grandmaster, Data Scientist, H2O.ai
Mathias Mueller

Bio: A Kaggle Grandmaster and a Data Scientist at H2O.ai, Mathias Müller holds an AI and ML focused diploma (eq. M.Sc.) in computer science from Humboldt University in Berlin. During his studies, he keenly worked on computer vision in the context of bio-inspired visual navigation of autonomous flying quadrocopters. Prior to H2O.ai, he as a machine learning engineer for FSD Fahrzeugsystemdaten GmbH in the automotive sector. His stint with Kaggle was a chance encounter as he stumbled upon the data competition platform while looking for a more ML-focused platform as compared to TopCoder. This is where he entered his first predictive modeling competition and climbed up the ladder to be a Grandmaster. He is an active contributor to XGBoost and is working on Driverless AI with H2O.ai.

Linkedin: https://www.linkedin.com/in/muellermat/

cancel
Dmitry Larko
Dmitry Larko Kaggle Grandmaster, Senior Data Scientist, H2O.ai
Dmitry Larko

Bio: Senior Data Scientist at H2O.ai, Dmitry Larko also is a former #25 Kaggle Grandmaster and loves to use his machine learning and data science skills in Kaggle Competitions and predictive analytics software development. He has more than 15 years of experience in information technology. Post his masters in computer information systems from Krasnoyarsk State Technical University (KSTU), he started his career in data warehousing and business intelligence and gradually moved to big data and data science. He holds a lot of experience in predictive analytics in a wide array of domains and tasks. Prior to H2O.ai, Dmitry held the position of SAP BW Developer at Chevron, Data Scientist at EPAM, and that of Lead Software Engineer with the Russian Federation.

Linkedin: https://www.linkedin.com/in/dlarko/

cancel
Martin Stein
Martin Stein Chief Analytics Officer, G5
Martin Stein
Driving Marketing Performance with H2O Driverless AI

Linkedin: https://www.linkedin.com/in/steinmartin/

cancel
Tess Posner
Tess Gilman Posner CEO, AI4ALL
Tess Gilman Posner
Diverse and Inclusive AI

Artificial intelligence could contribute an additional 1.2% to annual gross domestic product growth over the next decade, according to a recent McKinsey report. The report also predicts that about 70% of companies will adopt at least one form of AI by 2030. As AI goes full steam ahead, it's critical to ask the right questions while still in early stages: who is building and shaping this important technology? Research shows that the AI field doesn’t adequately reflect the broader population, which suggests that globally, we’re missing out on the value that diverse teams bring to AI development, implementation, and research. For example, in the US only 13% of AI CEOs are women, and only 2.6% of tenure-track engineering faculty identify as African American and only 3.6% identify as Hispanic. When diverse voices are left out of AI, the reliability and fairness of AI systems come into question.

Bio: Tess Posner is the CEO of AI4ALL, where she works to make artificial intelligence more diverse and inclusive and to ensure that AI is developed responsibly. Previously, she was Managing Director of TechHire, a national initiative launched out of the White House to increase diversity in the tech economy. Tess’s work has been featured by the Wall Street Journal, the Atlantic, Business Insider, TechCrunch, and Fast Company and funded by top national foundations and influencers including Melinda Gates, Jensen Huang, Google.org, JPMorgan Chase Foundation, Autodesk and the Robin Hood Foundation.

Linkedin: https://www.linkedin.com/in/tessposner/

cancel
Patrick Hall
Patrick Hall Senior Director - Products, H2O.ai
Patrick Hall
Human-Friendly Machine Learning

This presentation illustrates how to combine innovations from several sub-disciplines of machine learning research to train understandable, fair, trustable, and accurate predictive modeling systems. Techniques from research into fair models, directly interpretable Bayesian or constrained machine learning models, and post-hoc explanations can be used to train transparent, fair, and accurate models and make nearly every aspect of their behavior understandable and accountable to human users. Additional techniques from fairness research can be used to check for sociological bias in model predictions and to preprocess data and post-process predictions to ensure the fairness of predictive models. Finally, applying new testing and debugging techniques, often inspired by best practices in software engineering, can increase the trustworthiness of model predictions on unseen data. Together these techniques create a new and truly human-friendly type of machine learning suitable for use in business- and life-critical decision support.

Patrick Hall is senior director for data science products at H2O.ai where he focuses mainly on model interpretability. Patrick is also currently an adjunct professor in the Department of Decision Sciences at George Washington University, where he teaches graduate classes in data mining and machine learning. Prior to joining H2O.ai, Patrick held global customer facing roles and research and development roles at SAS Institute.

Linkedin: https://www.linkedin.com/in/jpatrickhall/

cancel
Vivant Shen
Vivant Shen Lead Data Scientist, Lending Works
Vivant Shen

Vivant has studied economics and has obtained two masters in finance and risk sectors. She has spent several years in the financial sector and peer-to-peer lending focusing on credit risk modelling, fraud detection and building scorecards. She is also a Kaggle Master since 2016.

Linkedin: https://www.linkedin.com/in/vivant-shen-3b86813b/

cancel
Bojan Tunguz
Bojan Tunguz Kaggle Grandmaster, Data Scientist, H2O.ai
Bojan Tunguz

Bojan was born in Sarajevo, Bosnia & Herzegovina, which my family fled for Croatia during the war. He came to the US as a high school exchange student, and managed to realize his dream of studying Physics. He has worked in academia for a few years, but for various personal and professional reasons decided to leave it. A few years ago he stumbled upon the wonderful world of Data Science and Machine Learning, and felt like he discovered his second vocation in life. Some of you may know him through Kaggle, where he's currently ranked in top 20 for competition, and in top 10 for kernels and discussions. He has a wonderful wife and three amazing little boys that keep him constantly busy and amused. He is a voracious reader, passionate about tinkering with all sorts of tools and gadgets, love digital photography, and really enjoys hiking in the woods.

Linkedin: https://www.linkedin.com/in/tunguz/

cancel
Navrina Singh
Navrina Singh Principal Product Lead, Microsoft AI
Navrina Singh

Navrina Singh is Principal Product Lead in Microsoft Cloud & AI, where she is focused on building conversational AI products for Business Application Group. Prior to this, Navrina was the Director Business Development for Artificial Intelligence responsible for business development, strategy and partnerships to forge new businesses for Microsoft leveraging Artificial Intelligence technologies. Before joining Microsoft in 2016, Navrina spent 12 years at Qualcomm Incorporated, where she held multiple roles across engineering, product management and strategy. In her last role at Qualcomm, Navrina was the head of Qualcomm Innovation responsible for the vision and execution of the technology incubator (Qualcomm ImpaQt) focused on building emerging technologies and delivering strategic partnerships in Artificial Intelligence, Internet of Things and Mobile.

Navrina is a Young Global Leader with World Economic Forum (WEF), for her work in disruptive technologies, catalyzing startup ecosystems and a keen focus on cognitive diversity and Inclusion. Navrina was also a member of the WEF Global Future Council on AI and Robotics, exploring how developments in these fields could impact industry, governments and society in the future. Navrina currently serves on the industry advisory board of the University of Wisconsin-Madison College of Electrical Engineering. Navrina holds a MS in Electrical Engineering from the University of Wisconsin-Madison, an MBA from the University of Southern California and a BS in Electronics & Telecommunications from College of Engineering, Pune India.

Linkedin: https://www.linkedin.com/in/navrina/

cancel
Megan Kurka
Megan Kurka Customer Data Scientist, H2O.ai
Megan Kurka
Auto-Doc with H2O Driverless AI

H2O Driverless AI is H2O.ai's flagship platform for automatic machine learning. It fully automates the data science workflow including some of the most challenging tasks in applied data science such as feature engineering, model tuning, model optimization, and model deployment. Driverless AI turns Kaggle Grandmaster recipes into a full functioning platform that delivers "an expert data scientist in a box" from training to deployment. Driverless AI with Auto Doc is the next logical step of the data science workflow by taking the final step of automatically documenting and explaining the processes used by the platform. Auto Doc frees up the user from the time consuming task of documenting and summarizing their workflow while building machine learning models. The resulting documentation provides users with insight into machine learning workflow created by Driverless AI including details about the data used, the validation schema selected, model and feature tuning, and the final model created. With this capability in Driverless AI, users can focus on model insights and results.

Megan is a Customer Data Scientist at H2O. Prior to working at H2O, she worked as a Data Scientist building products driven by machine learning for B2B customers. She has experience working with customers across multiple industries, identifying common problems, and designing robust and automated solutions.

Linkedin: https://www.linkedin.com/in/megan-kurka-36336569/

cancel
Jakub Hava
Jakub Hava Senior Software Engineer, H2O.ai
Jakub Hava

Bio: Jakub (or “Kuba” as we call him) completed his Bachelor’s Degree in Computer Science and Master’s Degree in Software Systems at Charles University in Prague. As a bachelor’s thesis, Kuba wrote a small platform for distributed computing of any types of tasks. During his master’s degree studies, he developed a cluster monitoring tool for JVM based languages which makes debugging and reasoning the performance of distributed systems easier using a concept called distributed stack traces. Kuba enjoys dealing with problems and learning new programming languages. At H2O.ai, Kuba works on Sparkling Water. Aside from programming, Kuba enjoys exploring new cultures and bouldering. He’s also a big fan of tea preparation and the associated ceremony.

Linkedin: https://www.linkedin.com/in/havaj/

cancel
Eva Prakash
Eva Prakash Alumni Chapter Lead, AI4ALL
Eva Prakash

Bio: Eva Prakash is a Stanford AI4ALL 2016 alum and Alumni Chapter Lead. AI is so fascinating to her—it doesn’t just power voice assistants or recommend Netflix shows, it is the most transformative technology of our time and is revolutionizing every major industry! Since AI is making key decisions about our lives, Eva wants to help ensure it is developed by a diverse community of female technologists, so that its assessments are truly all-inclusive and not simply male-dominated. Eva is the author of the young adult fiction book Alan Purring, which tells the story of a young Latina who crafts an AI-powered catbot named Alan Purring. She also delivered a TEDx talk titled "Why Diversity Matters for the Future of AI".

cancel
Vladimir Iglovikov
Vladimir Iglovikov Kaggle Grandmaster
Vladimir Iglovikov

Vladimir Iglovikov is a Computer Vision Engineer at Lyft where he applies Deep Learning techniques to the problems of self-driving. He holds Ph.D. in theoretical physics from the University of California, Davis. Among his hobbies is participating in predictive analytics competitions primarily on Kaggle.com. Currently, he is ranked 65.

Linkedin: https://www.linkedin.com/in/iglovikov/

cancel
Melanie Rubino
Melanie Rubino Senior Data Scientist, Wells Fargo
Melanie Rubino
Fraud Detection at Wells Fargo with H2O

Linkedin: https://www.linkedin.com/in/melanie-rubino/

cancel
Bharath Sudharsan
Bharath Sudharsan Director of Data Science and Innovation, ArmadaHealth
Bharath Sudharsan
NLP in Aid of Critical Health Decisions

Linkedin: https://www.linkedin.com/in/bharath-sudharsan-7219b9a/

cancel
Megan Risdal
Megan Risdal Product Lead, Kaggle
Megan Risdal

Megan Risdal is a Product Lead at Kaggle. She holds Master's degrees in linguistics from UCLA and NC State University. She leads product development for Kaggle's Datasets platform. Outside of product, she's also a passionate advocate for Kaggle's community.

Linkedin: https://www.linkedin.com/in/megan-risdal-4617812a/

cancel
Ruben Diaz
Ruben Diaz Data Scientist, Vision Banco
Ruben Diaz
AI journey at Vision Banco - Paraguay

We will talk about the AI transformation journey at Vision Banco - Paraguay, from the early initiatives to futures use cases, and how we adopted open source H2O.ai and Driverless AI in our organization.

My name is Ruben Diaz, from Asunción, Paraguay. I am married and father of 3 children. I work as Data Scientist at Vision Banco

Linkedin: https://www.linkedin.com/in/rubenjosediaz/

cancel
Carmelo Iaria
Carmelo Iaria Founder & CEO, AI Academy
Carmelo Iaria
How The AI Academy is accelerating NLP projects with Driverless AI

The 2018 Brazilian Presidential Elections represented a tangible demonstration of radical change in the way candidates conduct their campaigns, as the shift from traditional media to social media hit the shore of the largest country in the southern hemisphere.
Analyzing the political agenda, the broadcast TV-based debates and exchange on social media networks was an NLP feast that The AI Academy reckoned was too good to pass. In this panel, we present the work we conducted , and will show how Driverless AI helped us accelerate our NLP experiments thanks to the recent introduction of advanced text analytics recipes.



Bio: Maker/Dreamer/Iconoclast/Chaordic Leader with over 20 years of experience across a number of high-tech industries around the world. Curiosity towards new technologies and the ability to adapt to different cultural and social environments has taken him from a research lab in Italy to a start up in Denmark, to a multinational technology company in Silicon Valley, and ultimately to a leading broadband and video service provider in Brazil. Time and again his career journey has demonstrated his ability to recognize at a very early stage high-potential disruptive ideas and the determination to transform an idea into a real product / service.
Over the past seven years, Carmelo cultivated his passion for innovation by leading major technology incubations at a large Telecom operator, supporting the Brazilian startup ecosystem as a Mentor at a startup accelerator and continuously extending his business and technology knowledge through a blend of formal learning & hands-on projects implementations. His focus over the past few years has been on Data Science and Artificial Intelligence, carrying out in-depth technology investigations, product incubations and solutions development.
By establishing The AI Academy, Carmelo intends to create and foster a rich environment for the study, research and application of Machine/Deep Learning techniques to real-life use cases, bridging the AI gap between talent and Enterprises - and furthermore elevating Brazil's "AIQ", inserting São Paulo on the world's AI Map.

Linkedin: https://www.linkedin.com/in/carmeloiaria/

cancel
Anthony Goldbloom
Anthony Goldbloom Co-Founder & CEO, Kaggle
Anthony Goldbloom

Anthony Goldbloom is CEO of Kaggle (now a Google company), the world's largest data science and machine learning community. Forbes has twice named Anthony one of the 30 under 30 in technology, the MIT Technology Review has named him as one of the 35 Innovators Under 35 and the University of Melbourne has given Anthony an Alumni of Distinction Award.

Linkedin: https://www.linkedin.com/in/anthonygoldbloom/

cancel
Mark Seiss
Mark Seiss Director - Advanced Analytics Services, Dun & Bradstreet
Mark Seiss

Mark Seiss is a director in the Advanced Analytic Services (AAS) group at Dun and Bradstreet. As a resource stationed in the Government Solutions office in Reston, VA, Mark often works with the Government Sales team to show the value of D&B’s data and analytics to U.S. government agencies and contractors. More recently, he has also devoted his time to supporting research applicable all verticals at Dun and Bradstreet, including initiatives focusing on Cyber Security Risk and Machine Learning Strategy. Prior to joining Dun and Bradstreet in 2013, Mark spent 9 years working as mathematical statistician at the US Census Bureau, conducting estimation for the Decennial Census, Census Coverage Measurement, and American Community Survey operations. Mark holds a B.S. and M.S. in Mathematics from Virginia Tech, M.S. in Statistics from George Washington University, and Ph.D. in Statistics from Virginia Tech. As part of his doctorate program, Mark worked in the Laboratory for Interdisciplinary Statistical Analysis, where he collaborated with clients on over 100 statistical projects.

Linkedin: https://www.linkedin.com/in/mark-seiss-44a44011/

cancel
Oleksii Barash
Oleksii Barash IVF Laboratory Research Director - Reproductive Science Center of the San Francisco Bay Area
Oleksii Barash
Machine Learning in Reproductive Science: Human Embryo Selection and Beyond

In this talk, Oleksii Barash PhD, IVF Laboratory Research Director at the Reproductive Science Center of the San Francisco Bay Area, will discuss his team’s approach to applying machine learning for decision making during infertility treatment. Oleksii will also give a quick overview of how he uses Driverless AI to build models for predicting IVF outcomes.

Bio: Oleksii believes that evidence-based clinical decisions will greatly improve the efficiency and safety of the medicine. He received his Master degree in Clinical Embryology from University of Leeds (UK) and PhD in Cell Biology. The ultimate goal of his findings is to essentially transform medical records into medical knowledge.

Linkedin: https://www.linkedin.com/in/oleksii-barash-65132185/

cancel
Deepak Agarwal
Deepak Agarwal Vice President of Engineering, Linkedin
Deepak Agarwal
AI that creates professional opportunities at scale

Professional opportunities can manifest itself in several ways like finding a new job, enhancing or learning a new skill through an online course, connecting with someone who can help with new professional opportunities in the future, finding insights about a lead to close a deal, sourcing the best candidate for a job opening, consuming the best professional news to stay informed, and many others. LinkedIn is the largest online professional social network that connects talent with opportunity at scale by leveraging and developing novel AI methods. In this talk, I will provide an overview of how AI is used across LinkedIn and the challenges thereof. The talk would mostly emphasize the principles required to bridge the gap between theory and practice of AI, with copious illustrations from the real world.

Deepak Agarwal is a vice president of engineering at LinkedIn where he is responsible for all AI efforts across the company. He is well known for his work on recommender systems and has published a book on the topic. He has published extensively in top-tier computer science conferences and has coauthored several patents. He is a Fellow of the American Statistical Association and has served on the Executive Committee of Knowledge Discovery and Data Mining (KDD). Deepak regularly serves on program committees of various conferences in the field of AI and computer science. He is also an associate editor of two flagship statistics journals

Linkedin: https://www.linkedin.com/in/dipu1025/

cancel
Marc Stein
Marc Stein Founder and CEO, Underwrite.ai
Marc Stein
Driverless AI Use Cases in Finance and Cancer Genomics

Marc Stein is the founder and CEO of Underwrite.ai. Underwrite.ai applies advances in artificial intelligence derived from genomics and particle physics to provide lenders with non-linear, dynamic models of credit risk which radically outperform traditional approaches. Marc’s career has always revolved around deep interests in artificial intelligence, quantum physics, genomics, sugar cream pie, and all ice cream flavors found at Berthillon and the challenge of how to combine all these in practical applications.

Linkedin: https://www.linkedin.com/in/marc-stein-underwrite/

cancel
Mara Averick
Mara Averick Tidyverse Dev Advocate, RStudio
Mara Averick
Sustainers of the tidyverse

If a piece of open-source software is to survive its own success, it must have a healthy community of active contributors. The tidyverse, a collection of R packages for data science with a shared underlying design philosophy, has become popular in no small part because of its usability among those who do not consider themselves to be “programmers.” However, this active base of non-developer users has been a fruitful source of contributions as we’ve sought to highlight aspects of contributing to open source beyond committing lines of code. This talk will cover the technical tools, virtual spaces, and social norms that have enabled and empowered community contributions.

Mara Averick is the tidyverse developer advocate at RStudio. She got into R by way of a long-time love for the NBA and (fantasy) basketball. When not catering to the every whim of her dogs, she can be found: perusing weird words; indulging her bibliomania; and/or watching, quantifying, and visualizing Archer.

Linkedin: https://www.linkedin.com/in/maraaverick/

cancel
Robert Coop
Robert Coop Senior Manager, Artificial Intelligence and Machine Learning, Stanley Black & Decker
Robert Coop

Linkedin: https://www.linkedin.com/in/rcoop/

cancel
Navdeep Gill
Navdeep Gill Senior Machine Learning Engineer, H2O.ai
Mark Landry
Mark Landry Competitive Data Scientist, H2O.ai
Mateusz Dymczyk
Mateusz Dymczyk Senior Machine Learning Engineer, H2O.ai
Pramit Choudhary
Pramit Choudhary Lead Data Scientist, H2O.ai
Pramit Choudhary

Linkedin: https://www.linkedin.com/in/pramitc/

cancel
Mike Gualtieri
Mike Gualtieri VP & Principal Analyst, Forrester Research
Mike Gualtieri
Making AI Happen Without Getting Fired

AI is real. Enterprises use it to automate decisions, hyper-personalize customer experiences, streamline operational processes, and much more. However, for most enterprise technology leaders, AI technologies and use cases are still far too mysterious. The field is moving fast. Enterprise leaders must forge a coherent, pragmatic AI strategy that is tied to business outcomes. In this session, guest speaker Forrester Research Vice President & Principal Analyst Mike Gualtieri will demystify enterprise AI, identify use cases most likely to succeed, and, most importantly, provide key advice to enterprise leaders that are charged with moving AI forward in their organization.

Bio: Mike's research focuses on software technologies, platforms, and practices that enable technology professionals to deliver digital transformations that lead to prescient digital experiences and breakthrough operational efficiency. His key technology coverage areas are AI, machine learning, deep learning, AI chips and systems, digital decisions, streaming analytics, prescriptive analytics, big data analytical platforms and tools (Hadoop/Spark/Flink; translytical databases), optimization, and emerging technologies that make software faster and smarter. Mike is also a leading expert on the intersection of business strategy, artificial intelligence, and innovation. Mike provides technology vendors with actionable, fine-tuned advisory sessions on strategy, messaging, competitive analysis, buyer-persona analysis, market trends, and product road maps for the areas he directly covers and adjacent areas that wish to launch into new markets or use new technologies. Mike is a recipient of the Forrester Courage Award for making bold calls that inspire leaders and guide great business and technology decisions.

Linkedin: https://www.linkedin.com/in/mgualtieri/

cancel
Alvaro Viloria
Alvaro Viloria Search & Recommendations Team - Software Development Engineer, Groupon
Alvaro Viloria
Productionalizing ML at-scale with MLFlow and H2O Sparkling Water

The conceptual workflow of applying machine learning (ML) to any specific use case is simple: at the training phase, the learning component takes a dataset as input and builds a learned model; at the serving phase, the model takes features as input and yields predictions. However, the actual workflow becomes more complex when ML models need to be set up in a production environment. This will require a careful orchestration of several components to reliably produce, deploy and evaluate such models. At Groupon, the ranking recommendation system is based on supervised ML models. Once a model is promoted from candidate to released, and start serving real-time traffic, it opens the following questions: How can we assure that the model is not losing its prediction power? How can we reliably keep track of all released models life cycle? To answer these questions, each new model is built on an ML pipeline that guarantees its standardization, transparency, reproducibility and reliable evaluation. For this purpose, at Groupon we built a custom made ML-pipeline, using a simple but powerful integration between MLFow and H2O sparkling water. Every model during its training step publishes all its information, such as output values, hyperparameters, evaluation metrics, features, queries, etcetera, into MLflow as the main Model Registry. As a final step of the ML Pipeline, every released model is evaluated with fresh data, by applying a sequence of orchestrated steps. Each released model retrieves its metadata from MLflow and is evaluated by using the same constraints over the data, so as to assure a reliable evaluation. Finally, the variations on the predictive power of the model are visualized using Kibana, to constantly monitor any sign of decay.

Linkedin: https://www.linkedin.com/in/alvaro-viloria-97b4b725/

cancel
Tate Campbell
Tate Campbell Data Scientist, Change Healthcare
Tate Campbell

Tate Campbell is a Data Scientist at Change Healthcare specializing in Big Data and Cloud Technologies. Tate has experience as a practicing Data Scientist in the healthcare, legal, and marketing industries. Tate studied biochemistry and mathematics at California State University, Chico and holds a Master’s degree in Analytics from the University of San Francisco.

Linkedin: https://www.linkedin.com/in/lolskee/

cancel
Nanda Vijaydev
Nanda Vijaydev Senior Director of Solutions and Lead Data Scientist, BlueData
Nanda Vijaydev
Deploying H2O in Large-Scale Distributed Environments using Containers

This session will discuss how to get up and running quickly with containerized H2O environments (H2O Flow, Sparkling Water, and Driverless AI) at scale, in a multi-tenant architecture with a shared pool of resources using CPUs and/or GPUs. See how how you can spin up (and tear down) your H2O environments on-demand, with just a few mouse clicks. Find out how to enable quota management of GPU resources for greater efficiency, and easily connect your compute to your datasets for large-scale distributed machine learning. Learn how to operationalize your machine learning pipelines and deliver faster time-to-value for your AI initiative — while ensuring enterprise-grade security and high performance.

Bio: Nanda Vijaydev is senior director of solutions at BlueData (now HPE) - where she leverages technologies like Hadoop, Spark, and TensorFlow to build solutions for enterprise analytics and machine learning use cases. Nanda has 10 years of experience in data management and data science. Previously, she worked on data science and big data projects in multiple industries, including healthcare and media; was a principal solutions architect at Silicon Valley Data Science; and served as director of solutions engineering at Karmasphere. Nanda has an in-depth understanding of the data analytics and data management space, particularly in the areas of data integration, ETL, warehousing, reporting, and machine learning.

Linkedin: https://www.linkedin.com/in/nanda-vijaydev-3638693/

cancel
Mark Sykes
Mark Sykes COO at Kx; Group CTO at First Derivatives (Kx parent company); and Director at Brainpool AI
Mark Sykes

Bio: Mark is a technologist with experience leading enterprise technology innovation at global companies for many years. At Kx he introduced initiatives including: extending the core database, kdb+, to include unstructured as well as structured data; expansion to the public cloud, and integration of kdb+ with more technologies and platforms, such as Python and Machine Learning. As CTO of Kx parent First Derivatives, he oversees the strategic direction of all Kx products and solutions.

Linkedin: https://www.linkedin.com/in/marksykes/

cancel
Eric Jin
Eric Jin Senior Manager, Discover Financial Services

Agenda

February 4th, 2019
8:00am

Coffee & Registration

8:00am - 9:00am
9:00am

Welcome Keynote by SriSatish Ambati

9:00am - 9:15am
9:15am

Roadmaps and Vision by Michal Malohlava

9:15am - 9:30am
9:30am

A Look Under the Hood of H2O Driverless AI by Arno Candel

9:30am - 9:50am
9:50am

Machine Learning Interpretability by Patrick Hall

9:50am - 10:10am
10:10am

Automatic Visualization by Leland Wilkinson

10:10am - 10:30am
10:30am

Get Behind the Wheel with H2O Driverless AI - Hands-on Lab

10:30am - 12:00pm
12:00pm

Lunch

12:00pm - 1:00pm
1:00pm

Deep Dive Into H2O + AutoML by Tom Kraljevic, Erin LeDell, Michal Kurka

1:00pm - 2:15pm
2:15pm

Deep Dive into Sparkling Water by Jakub Háva

2:15pm - 3:30pm
3:30pm

Closing Remarks

3:30pm - 4:00pm
4:00pm

Bharatanatyam Performance - Life of Pi

4:00pm - 5:00pm
5:00pm

Reception

5:00pm - 6:00pm
6:00pm

An Evening of Explainable AI - Meetup

6:00pm - 8:00pm
February 5th, 2019
8:00am

Coffee & Registration

8:00am - 9:00am
9:00am

Keynotes

9:00am - 10:00am
10:00am

Break

10:00am - 10:15am
10:15am

Keynotes

10:15am - 12:00pm
12:00pm

Lunch

12:00pm - 1:00pm

Business Track

Technical Track

H2O Track

1:00pm

Sessions

1:00pm - 3:00pm
3:00pm

Break

3:00pm - 3:15pm
3:15pm

Afternoon Sessions

3:15pm - 4:00pm
4:00pm

Women and Inclusion in Tech Panel

4:00pm - 5:00pm
4:00pm

H2O Driverless AI Panel

4:00pm - 5:00pm
5:00pm

How To Be An Expert Data Scientist Panel

5:00pm - 5:45pm
5:45pm

Closing Remarks

5:45pm - 6:00pm
6:00pm

Reception

6:00pm - 7:00pm
Training

Training

This will be a hands-on training of our groundbreaking products, H2O Driverless AI, H2O-3 and Sparkling Water. Join your fellow data scientists, developers and engineers in this technical deep-dive of H2O.

Don't forget to bring your laptop and power cord!

Want to get a head start and get behind the wheel of H2O Driverless AI? Request a free trial here.

Hilton

Venue

Hilton San Francisco Union Square is in the heart of San Francisco, walking distance from Westfield San Francisco Centre and Union Square. This hotel is close to Lombard Street and Pier 39.

FAQ

Who attends this event?

Designed for data scientists, data engineers and business leaders, H2O World San Francisco offers something for everyone no matter your skill set or background.

I’d like to speak at H2O World San Francisco, do you accept speakers?

While we don’t have an official call for papers, we are open to abstract submissions.

Please send your abstract title, abstract and bio to events@h2o.ai.

Are there ID or minimum age requirements to enter the event?

Please ensure you have a government-issued ID to enter the event. We will have a reception on Day 2 which will require those only 21 years old and above to attend as alcohol will be served.

What are my transportation and parking options for getting to and from the event?

From SFO Airport, take Highway 101 north and exit at 7th street. Turn right on Folsom Street, then left on 5th street. Cross Market Street and turn left on Ellis Street to Mason Street. Our garage entrance is on Ellis Street between Mason and Taylor Streets.

The hotel location is within walking distance to transportation hubs, cable cars, shopping and popular city attractions including Union Square and Moscone Convention Center. A few other location highlights:

•One block from Powell Street Station for BART and MUNI transit lines
•Only 13 miles from San Francisco International Airport (SFO) and 13 miles from Metropolitan Oakland International Airport (OAK)
•Walking distance to the Cable Cars

Please follow the H2O World San Francisco signage inside the hotel which will guide you to our registration area.

What must I bring to the event?

Please bring your laptop and charger. We'll have power stations throughout the venue.

Do I have to bring my printed ticket to the event?

No, please bring your government issued ID - that's how we'll verify your ticket.

Is my registration/ticket transferrable?

We understand things change. So, if you have to transfer your ticket to a friend or colleague, we can do that for you. No change-fee. No hassle. No sweat. You can contact us at events@h2o.ai.

Still haven't found what you are looking for?

Contact us at events@h2o.ai with any questions related to the event. Please share your ideas about topics or speakers that make you excited.

Our Code of Conduct

Learn More
Yellow pattern

Register Now

Register

#H2OWORLD