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/

Agus Sudjianto

Interpretable Machine Learning

Abstract: Machine Learning has gained a very rapid adoption in the financial industry. Machine learning models are being applied in areas that have customarily been the domain of traditional statistical methods (e.g., credit risk and financial crimes) as well as other areas that normally do not employ models (e.g., compliance or conduct risks). Managing machine learning model risk is of the utmost importance in heavily regulated industries such as finance; in particular, to manage potential risks due to bias/fairness, conceptual soundness, implementation, and model change control. In this talk, I will discuss the wide ranging applications of machine learning in banking and how we can manage their risks with a special focus on model interpretability.

Bio: Agus Sudjianto is an executive vice president and head of Corporate Model Risk for Wells Fargo, where he is responsible for enterprise model risk management.

Linkedin: https://www.linkedin.com/in/agus-sudjianto-76519619/

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/

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/

Ingrid Burton

Bio: Ingrid Burton is CMO at H2O.ai, the open source leader in AI. She has several decades of experience leading global marketing teams to build brands, create demand, and engage and grow communities. She has advised several startups as a brand and demand expert including DriveScale, Paxata and MapR. Prior to her advisor roles, she was CMO at Hortonworks, where she drove a brand and marketing transformation, and created ecosystem programs that positioned the company for growth. At SAP she co-created the Cloud strategy, led SAP HANA and Analytics marketing, and drove developer outreach.

She also served as CMO at Silver Spring Networks and Plantronics after spending almost 20 years at Sun Microsystems, where she was head of Sun marketing, led Java marketing to build out a thriving Java developer community, championed and led open source initiatives, and drove various product and strategic initiatives. A developer early in her career, Ingrid holds a BA in Math with a concentration in Computer Science from San Jose State University.

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

Paul Zikopoulos

Into the Mysterious World of a Thinking Business

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/

Sumit Gupta

Bio: Sumit Gupta is VP, AI, Machine Learning, and HPC in the IBM Cognitive Systems business. Sumit leads the business strategy and software and hardware products for machine learning, deep learning, & HPC, including PowerAI and Spectrum Compute. Prior to IBM, Sumit was the general manager of the AI & GPU accelerated data center business at NVIDIA and was central in building that business from the ground-up to what is now a multi-billion dollar business for NVIDIA. Sumit has a Ph.D. in CS from UC, Irvine, and a BS in EE from IIT Delhi.

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

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/

Naren Gupta

Linkedin: https://www.linkedin.com/in/naren-gupta-6364483/

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/

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/

Leland Wilkinson

Bio: Leland Wilkinson is Chief Scientist at H2O and Adjunct Professor of Computer Science at the University of Illinois Chicago. He received an A.B. degree from Harvard in 1966, an S.T.B. degree from Harvard Divinity School in 1969, and a Ph.D. from Yale in 1975. Wilkinson wrote the SYSTAT statistical package and founded SYSTAT Inc. in 1984. After the company grew to 50 employees, he sold SYSTAT to SPSS in 1994 and worked there for ten years on research and development of visualization systems. Wilkinson subsequently worked at Skytree and Tableau before joining H2O.

Wilkinson is a Fellow of the American Statistical Association, an elected member of the International Statistical Institute, and a Fellow of the American Association for the Advancement of Science. He has won best speaker award at the National Computer Graphics Association and the Youden prize for best expository paper in the statistics journal Technometrics. He has served on the Committee on Applied and Theoretical Statistics of the National Research Council and is a member of the Boards of the National Institute of Statistical Sciences (NISS) and the Institute for Pure and Applied Mathematics (IPAM). In addition to authoring journal articles, the original SYSTAT computer program and manuals, and patents in visualization and distributed analytic computing, Wilkinson is the author (with Grant Blank and Chris Gruber) of Desktop Data Analysis with SYSTAT. He is also the author of The Grammar of Graphics, the foundation for several commercial and open­source visualization systems (IBM­RAVE, Tableau, R­ggplot2, and Python­Bokeh).

Linkedin: https://www.linkedin.com/in/leland-wilkinson-07a0b25/

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/

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/

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/

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/

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/

Balaji Gopalakrishnan

Building a World-Class Data Science Team

Bio: Balaji Gopalakrishnan has over 15 years experience in the Machine Learning and Data Science space. Balaji has led cross functional data science and engineering teams for developing cutting-edge machine learning and cognitive computing capabilities for insurance fraud and underwriting, telematics, multi-asset class risk, scheduling under uncertainty, and others. He is passionate about driving AI adoption in organizations and strongly believes in the power of cross functional collaboration for this purpose.

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

Krishnan Swamy

Building a World-Class Data Science Team

Bio: Krish Swamy is an experienced professional with deep skills in applying analytics and BigData capabilities to challenging business problems and driving customer insights. Krish's analytic experience includes marketing and pricing, credit risk, digital analytics and most recently, big data analytics and data transformation. His key experiences lie in banking and financial services, the digital customer experience domain, with a background in management consulting. Other key skills include influencing organizational change towards a data and analytics driven culture, and building teams of analytics, statisticians and data scientists.

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/

Meg Mude

Data Engineering Lifecycle Optimized on Intel

Developing meaningful AI applications requires complete data lifecycle management. Sourcing, harvesting, labelling and ensuring the conduit to consume data structures and repositories is critical for model accuracy....but, one of the least talked about subjects. Intel’s optimized technologies enable efficient delivery of complete data samples to develop (and deploy) meaningful outcomes. During this session, we’ll review the considerations and criticality of data lifecycle management for the AI production pipeline.

Bio: Meg brings more than 17 years of global product, engineering and solutions experience. She is presently a Solutions Architect with Intel Corporation specializing in Visual Compute and AAI (Analytics and AI) Architecture. She is passionate about the potential for technology to improve the quality of peoples’ lives and humanity on the whole.

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

Martin Stein

Driving Marketing Performance with H2O Driverless AI

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

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/

Sundar Ranganathan

Driverless AI integration with NetApp’s Hybrid-Cloud Data Fabric

In this presentation, we will demonstrate the integration of H2O Driverless.ai with NetApp Cloud Volumes Service. In addition, we’ll describe key considerations for the development of Deep Learning environments and the solutions that enable seamless data management across edge environments, on-premises data centers, and the cloud. This presentation is targeted for data scientists, data engineers, and line of business leaders.

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

Navrina Singh

Responsible AI – a collective effort

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/

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/

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/

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/

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/

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/

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/

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".

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/

Melanie Rubino

Fraud Detection at Wells Fargo with H2O

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

Bharath Sudharsan

NLP in Aid of Critical Health Decisions

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

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/

Vinod Iyengar

Driverless AI integration with NetApp’s Hybrid-Cloud Data Fabric

In this presentation, we will demonstrate the integration of H2O Driverless.ai with NetApp Cloud Volumes Service. In addition, we’ll describe key considerations for the development of Deep Learning environments and the solutions that enable seamless data management across edge environments, on-premises data centers, and the cloud. This presentation is targeted for data scientists, data engineers, and line of business leaders.

Bio: Vinod comes with over 7 years of Marketing & Data Science experience in multiple startups. He was the founding employee for his previous startup, Activehours, where he helped build the product and bootstrap the user acquisition with growth hacking. He has seen the user base for his companies grow from scratch to millions of customers. He’s built models to score leads, reduce churn, increase conversion, prevent fraud and many more use cases. He brings a strong analytical side and an metrics driven approach to marketing. He is responsible for all of our demand generation and growth efforts. When he is not busy hacking, Vinod loves painting and reading. He is a huge foodie and will eat anything that doesn’t crawl, swim or move.

Linkedin: https://www.linkedin.com/in/vinod-iyengar-1386126/

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/

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/

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/

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/

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/

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/

Robert Coop

Optimizing Manufacturing with Driverless AI

This talk will walk through a use case for Driverless AI within the manufacturing sector. We will discuss the motivation and tool selection process, then cover the solution development in detail. The solution development coverage will detail how Driverless AI was applied to the problem and how the resulting models are delivered to the customer.

Bio: Robert Coop leads the Artificial Intelligence and Machine Learning team within the Digital Accelerator at Stanley Black & Decker. He has been working with machine learning techniques for the past 10 years and has spent the majority of this time practicing data science and leading teams within an enterprise environment. Robert also currently teaches the Georgia Tech Data Science and Analytics Boot Camp as part of the Georgia Tech Professional Education Program.

Robert holds a Ph.D. in Machine Learning (Computer Engineering), where he focused on neural network architectures, training algorithms, and ensemble techniques.

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

Navdeep Gill

Linkedin: https://www.linkedin.com/in/navdeep-gill-b1729456/

Mark Landry

Linkedin: https://www.linkedin.com/in/mark-landry-78b863a/

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/

Mateusz Dymczyk

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

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/

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/

Rafael Coss

AI journey at Vision Banco

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

Luis Armenta

AI journey at Vision Banco

Luis holds a BSc in Electrical Engineering from the National University of Mexico and a MSc in Electrical Engineering/Computer Science from the University of Waterloo in Canada. He is also currently completing an Executive MBA at McCombs School of Business at the University of Texas in Austin. Luis has over ~14 years of experience, having started his career as a Research Scientist at Intel Labs before being promoted to 2nd Line Engineering Manager, leading the high-speed interconnect hardware design of Intel’s server portfolio. Luis also has held roles as Product Manager of EM simulators at Ansys, Inc. and as a Systems Engineer of 4K and 8K UHDTVs at Macom.

Linkedin: https://www.linkedin.com/in/luis-armenta/

Tom Aliff

Configurable Modeling for Maximizing Business Value

In the current world of data science there many available data sources, big data platforms, and advanced Machine Learning and AI based technologies available. It has become easier and easier to derive predictive value in an efficient and streamlined way and lose sight of objectives especially in the business world. This session will focus on not losing the business context and objective as the navigator for these powerful tools we have at our disposal. Through this discussion, I will review a path towards how to use the tools like explainable and driverless AI to your advantage versus letting the tools set the direction.

Bio: At Equifax, Tom leads the Data and Analytics consulting practice. Previously, Tom was the US Consumer and Commercial Data Sciences Leader. Tom joined Equifax in July of 2009. He brings several years of analytical experience in leading teams on statistical modeling engagements, analysis and consultation across several verticals including telecommunications, lending, mortgage, automotive, and marketing. Prior to Equifax, Tom was a data science manager at Experian and a Risk Modeler/Manager at American General Finance (now OneMain Financial). Tom holds a Master of Science in Applied Statistics from Purdue University, and a Bachelor of Science degree in Mathematics with a concentration in Statistics, also from Purdue University.

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

Lior Amar

The Production Loop - Deploying and Managing H2O Models

Deploying your H2O model is only the beginning. How do you know if your model is behaving as you expected in production? What if your live data drifts away from your training data? How do you get your model back on track? In this session, you will learn how to ParallelM has integrated with H2O to allow you to quickly deploy both batch and real-time predictions; monitor models for data deviation and performance; and then seamlessly update models in production without missing a beat.

Bio: Lior Amar is the Principal Engineer at ParallelM where he is responsible for MCenter platform. He is an expert with 20 years’ experience in distributed systems development, low-level system programming and HPC cluster management / Linux systems. Before joining ParallelM, Lior was a government researcher working on high-performance computing (HPC). Before that, he was the Founder and CTO of Cluster Logic, a distributed systems consulting company. He has a Ph.D., and Master’s degree in Computer Science focused on distributed systems.

Linkedin: https://www.linkedin.com/in/lior-amar-bb560b3/

Rahul Bhuman

Truck roll prediction using Driverless AI

Linkedin: https://www.linkedin.com/in/rahul-bhuman-9740172/

Belle Reader

Belle is an enterprising 7th grader who is interested in using technology to expand the reach of future entrepreneurial endeavors beyond the borders her home town of Oakland. With dueling interests in education and fashion, she envisions creating e-commerce platforms with global reach that can facilitate exposure of local professionals (like educators and textile designers) to broader markets. Thanks to her partnership with BLACK GIRLS CODE, Belle has been actively engaged with coders, mentors and influencers who have been redefining the Bay Area's tech landscape. Over the past few years, BGC projects at Salesforce, Google, Apple, and UC Berkeley School of Engineering have been fun, while helping her understand how she can harness tech and science to shape a positive future that can benefit others. Belle was grateful for a recent opportunity to represent BGC while at a cryptocurrency conference in Prague, where she learned about bitcoin and blockchain. She is excited to explore ways to incorporate what she was exposed to into ambitious longterm business goals.

Cadence Simone Patrick

Bio: Cadence Patrick is a 10th-grade Digital Media student from Oakland who is passionate about exploring the intersection of technology and the arts, and about utilizing technology to improve lives. Cadence has been active with Black Girls Code since 2015, when her team won first place in the summer Hackathon. She credits Black Girls Code with expanding her interest in computer science and technology and broadening the way she looks at her future.

Taposh Roy

Bio: Taposh Roy leads the innovation team of decision support group at Kaiser Permanente. His work focuses on journey analytics, deep learning, data science architecture and strategy. Prior to KP, Taposh was Head of AD products at a couple of start-ups Inpowered and Netshelter(Sold to Ziff Davis). Prior to start-ups, he worked as Sr. Associate Consultant in an MIT based consulting company Sapient. He was the co-founder a biotech company Bio-Integrated solutions developing DNA sequencers and liquid handling devices for proteomics. He has a unique combination of product, technology, and strategy consulting, data science and start-up experience. He is a consumer-focused, machine learning and data science geek.

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

Anthony Goldbloom

Ask-Me-Anything with Kaggle's CEO Anthony and Product Manager Megan

Bio: 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/

Megan Risdal

Ask-Me-Anything with Kaggle's CEO Anthony and Product Manager Megan

Bio: 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/

Eric Jin

Consumer Lending Model Development and Execution using H2O

Abstract: Overview of machine learning application in consumer lending. Use Marketing model example to illustrate typical H2O model development and execution process.

Linkedin: https://www.linkedin.com/in/weiqing-eric-jin-0622691/

Oleksiy Kononenko

Bio: Oleksiy is a maker scientist and hacker at H2O.ai, focusing on highly optimized algorithms for machine learning and data analysis. He holds M.S., summa cum laude, and Ph.D. degrees in applied mathematics from National University of Kharkiv, Ukraine. In 2009, Oleksiy was selected as a research fellow by CERN and contributed to R&D for Large Hadron Collider and next generation of high energy particle accelerators. In 2013 he joined SLAC and Stanford University to develop high performance simulation suite for 3D multi-physics modeling. Oleksiy authored more than 60 scientific papers, was an invited speaker at major international conferences, prominent institutions and companies worldwide. In his free time he enjoys snowboarding, playing soccer and basketball, guitar and drums, What? Where? When? and Jeopardy!

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

Rajesh Iyer

Bio: Rajesh is VP & Head of the AI CoE at Capgemini. He focuses on North America Financial Services clients, as a part of Capgemini’s Insights & Data Global Service Line. Rajesh has a particular interest in growing an AI Practice delivering Automated, Accelerated and Explainable AI solutions and programs for large F500 clients. Rajesh has 20+ years of Data Sciences/AI experience, working mostly in financial services institutions across banking and insurance.

Prior to joining Capgemini, Rajesh served as SVP & Head – Data Sciences in Xceedance, where he established a multi-national AI Practice from scratch. He has also served in senior positions, driving analytics agenda, at EXL Service/Inductis, USAA, Mu Sigma and Hartford. Rajesh began his career after completing a BS in Mathematics and Computer Science from the University of Illinois at Urbana-Champaign and a JD from the University of Nebraska at Lincoln – Law College. Apart from his passion for AI, Rajesh also likes to read historical biographies, travel to exotic destinations and catch up to the rest of the students in his Yoga class.

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

Pasha Stetsenko

Bio: Pasha is a Hacker Scientist at H2O.ai. He holds an MS in Applied Physics and Mathematics from Moscow Institute of Physics and Technology, an MA in Economics from New Economic School (Moscow), and a PhD in Economics (econometrics) from Stanford University. During his education he obtained knowledge in Computer Science, Machine Learning, Statistics and Econometrics. Prior to coming to H2O.ai, Pasha was working at a stealth-level machine learning startup Machinify.com as a data scientist / frontend engineer; before that as an engineer at Facebook; and before as a senior quantitative analyst at a business consulting company Keystone Strategy, working on big data analysis.

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