Sri Ambati

Bio: Sri Ambati is the CEO and Founder of H2O.ai – the maker behind H2O, the leading open source machine learning platform used by 18,000 companies and hundreds of thousands of data scientists. Prior to H2O.ai, he co-founded the big data analytics company Platfora. His professional career also spans technical and executive roles at Datastax, Azul Systems, and RightOrder. His academic career involved sabbaticals in theoretical neuroscience at Stanford and Berkeley and an M.S. in math and computer science from the University of Memphis. He was recently recognized by Datanami as one of the 12 People to Watch 2019.

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

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

Arno Candel

A Look Under the Hood of H2O Driverless AI

Driverless AI is H2O.ai’s latest flagship product for automatic machine learning for the enterprise. It fully automates some of the most challenging and productive tasks in data science, such as feature engineering, model tuning, model ensembling, model interpretation, report generation, and production deployment. Across industries and verticals, Driverless AI takes datasets and creates grand-master-level machine learning pipelines with minimal human input required. It also produces standalone scoring pipelines for Java, Python, R, and C++ for low-latency inference in production without any approximations. Driverless AI is designed to avoid common mistakes such as under- or overfitting, data leakage, or improper model validation, which are some of the hardest challenges in data science.

With bring your own recipe (BYOR), domain experts and advanced data scientists can write their own recipes in Python and seamlessly extend the Driverless AI platform with their favorite tools from the rich ecosystem of open source data science and machine learning libraries. Other industry-leading capabilities include automatic data visualization, machine learning interpretability, automatic report generation, and enterprise features such as security, authentication, data connectors, and model management.

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/

Erin Ledell

Scalable Automatic Machine Learning with H2O

In this presentation, Erin LeDell (Chief Machine Learning Scientist, H2O.ai), will provide a history and overview of the field of “Automatic Machine Learning” (AutoML), followed by a detailed look inside H2O’s open source AutoML algorithm. H2O AutoML provides an easy-to-use interface which automates data pre-processing, training and tuning a large selection of candidate models (including multiple stacked ensemble models for superior model performance). The result of the AutoML run is a “leaderboard” of H2O models which can be easily exported for use in production. AutoML is available in all H2O interfaces (R, Python, Scala, web GUI) and due to the distributed nature of the H2O platform, can scale to very large datasets. The presentation will end with a demo of H2O AutoML in R and Python, including a handful of code examples to get you started using automatic machine learning on your own projects.

Bio: Dr. LeDell is the Chief Machine Learning Scientist at H2O.ai, the company that produces the open source, distributed machine learning platform, H2O. Before joining H2O.ai, she was the Principal Data Scientist at two AI startups (both acquired), the founder of DataScientific, Inc. and a software engineer at a large consulting firm. She received her Ph.D. from UC Berkeley where her research focused on machine learning and computational statistics. She also holds a B.S. and M.A. in Mathematics.

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

Jade Mandel

LinkedIn: https://www.linkedin.com/in/jade-d-mandel/

Anurag Sehgal

LinkedIn:

LinkedIn: https://www.linkedin.com/in/anurag-sehgal-772267b0/

Charles Elkan

Machine learning in finance—the promise and the peril

This talk will discuss how machine learning (ML) fits into the landscape of quantitative methods used in finance, and draw conclusions about application domains where ML is more promising versus domains where the perils are more acute. The talk will also discuss how to formulate a financial goal as an ML problem, and how to choose between solution approaches.

Bio: Leading projects to apply machine learning and artificial intelligence across the firm. Evaluating opportunities to work with other organizations and consulting with clients.

LinkedIn: https://www.linkedin.com/in/celkan/

Yogesh Mudgal

Explainable AI Panel

Bio: Yogesh Mudgal is Director and Head of the Emerging Technology Risk & Risk Analytics at Citi; the goal of the program is to enable responsible innovation. He is responsible for leading the program globally, which includes identification of risks, evangelizing risks with emerging technologies, influence building of guardrails and frameworks, and risk assessments of emerging technologies. The program designs and manage risks analytics platform used by various teams to conduct risk assessments. Recently, he has been actively evangelizing, and collaborating with various institutions on AI risks. Yogesh has experience working at various financial institutions, where he built and influenced various information security initiatives.

LinkedIn: https://www.linkedin.com/in/yogeshmudgal/

Weiyan Zhao

A Decade of Data Science. The Nationwide Journey

The Nationwide Enterprise Analytics Office (formerly Customer Insights and Analytics) has more than 10 years of experience in end-to-end data product development and system integration. The culture to attract, train and develop talent, the technical advancement to apply the new methods, the model factory to productionalize models, and responsive processes to measure business impact have all contributed to positive business outcomes as well as this team’s fast growth. In this talk, we will introduce Nationwide’s data science capabilities through case studies of a few data products they have built and deployed.

Bio: Weiyan Zhao is the Director of Data Science at Nationwide Insurance’s Enterprise Analytics Office. She currently leads a team of data scientists to provide enterprise solutions that drive business value and influence decisions through application of advanced statistical modeling and machine learning techniques. Previously, Weiyan served as an Analytics Manager at Chase, and as a Research Associate at Nationwide Children’s Hospital and at University of Texas at San Antonio. She received her PhD in Epidemiology and Statistics, and has been passionate about data and analytics throughout her career. Additionally, she is also a long term volunteer for different non-profit organizations to promote culture and diversity, and mentors young professionals.

LinkedIn: https://www.linkedin.com/in/weiyan-zhao-11535235/

Prem Tadipatri

Innovation Journey, Vision and Approach

Bio: Prem is an industry veteran with 25 years of financial services and technology experience across investment banking, private banking, risk, finance and IT playing various leadership roles as the CTO, heading up various departments & large firm-wide programs.

Prior to Credit Suisse, Prem spent time with AT&T, Morgan Stanley, Engineering Animation & TCS in various management and hands on delivery roles.

Prem has a good broad understanding of the business across IB, PB, Risk, Finance, Operations and Shared Services. He is a hands on technology, data science and big data practitioner hence straddles hybrid roles across business, statistics and technology.

He holds an M.B.A in Finance & Entrepreneurship with Honors from the University Of Chicago Booth School Of Business and a Bachelor’s in Mechanical Engineering with a gold medal from College of Engineering Pune University.

LinkedIn: https://www.linkedin.com/in/premtadipatri/

Dean Soteropoulos

Innovation Journey, Vision and Approach

Bio: Dean Soteropoulos is a Managing Director and the Innovation Lead for the Finance organization, based in New York.

Since joining Credit Suisse in 2011, Dean has held a number of positions including Head of Finance Change, Product Control COO and Head of the Line Control organization within Product Control.

Prior to joining Credit Suisse, Dean was at Barclays Capital, where he was the Head of Product Control Change Management. During his nearly four years at Barclays Capital, Dean managed the Finance division’s strategic change programs, and played an integral role in advising on the firm’s front-to-back strategic architecture programs. Before Barclays Capital, Dean spent eight and a half years at Merrill Lynch where he held a number of senior line and change management positions within the Finance and Operations divisions. Previously, Dean spent two years at Lehman Brothers in Global Operations and Technology. He also has five years of experience outside of the financial services industry.

Dean holds a Bachelors of Science in Electrical Engineering from the State University of New York at Stony Brook.

Vinay Pai

Democratizing Data Science

Although AI makes headlines, AI adoption by financial professionals is still quite low. There are many business problems that can be solved through data science, but most of the required is trapped behind disparate systems. Sound familiar? At Bill.com, we have integrated H2O with our on-prem and AWS cloud infrastructure to “democratize data science.” We have enabled product managers, business strategists and operations professionals to solve their business problems with a rich data science platform. I will share our approach and architecture and provide recommendations.

Bio: Vinay Pai is an experienced technology executive with a track record of leading high-performing international organizations and driving technology transformation at scale and business growth globally. As the SVP of Engineering at Bill.com, Vinay leads the technology teams that develop and deliver the Bill.com product portfolio. In 2019 he was invited to join the Advisory Board for the Rice School of Engineering. He also will serve as Executive in Residence for the Computer Science Department at Rice. In 2018 he joined the Forbes Technology Council.

Prior to joining Bill.com, Vinay was SVP of Engineering at First Data, where he led Engineering for the Clover point of sale product line. Earlier, Vinay held several leadership roles in the small business group during his eight years at Intuit, which he joined as part of the PayCycle acquisition.

Most recently, as VP for Intuit Developer Platform, Vinay led the business segment responsible for the QuickBooks ecosystem of third-party applications and developers. Vinay has also held engineering leadership roles at Cassatt, Sun Microsystems and Schlumberger, and founded a startup on the Apple platform that delivered three products. Vinay has MS Electrical Engineering, BS Electrical Engineering and BA Computer Science degrees from Rice University.

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

Shar Rubio

Diversity and Inclusion in Tech Panel

Bio: Shar Rubio has 20+ years of experience in the financial services, pharmaceuticals and professional services businesses. She is currently an Executive Director at Rabobank, the leading global bank in the Food and Agriculture sectors. In her current role, Shar is focused on the governance and assurance of the North America projects portfolio on behalf of C-suite and senior executives and provides portfolio analytics and insights to drive C-suite decisions and portfolio strategy alignments.

LinkedIn: https://www.linkedin.com/in/shar-rubio/

Mike Minelli

LinkedIn: https://www.linkedin.com/in/michaelminelli/

Vijay Raghavan

How We Use H2O for Pharma Commercial Analytics

The talk focusses on how H2O machine learning algorithms can be utilized to identify ideal patient type of a prescription drug. Using these insights we can find other patients who have similar characteristics and target the physicians who have those patients through a wide variety of marketing programs. The insights can also be used to customize messages and creatives to activate different physicians

Bio: Vijay Raghavan is head of marketing analytics for Allergan the maker of Botox.  His group focusses on measuring ROI of various marketing campaigns and optimizing the spend. The other main focus is to utilize machine learning techniques on large claims and other datasets to identify patient characteristics and use that for consumer and hcp targeting. Previously Vijay worked at Goldman Sachs and ZS Associates a management consulting firm. Vijay has an MS in engineering from Cornell University and MBA from Columbia Business School.

LinkedIn: https://www.linkedin.com/in/vijay-raghavan-3236a1/

Leland Wilkinson

The Grammar of Graphics and the Future of Big Data Visualization

This year marks the twentieth anniversary of the first edition of The Grammar of Graphics (GG). The book laid the foundation for a major visualization component at the statistical software company SPSS. Since that edition, a commercial company, Tableau, evolved from a Stanford seminar and dissertation devoted to the book. Not long after, the widely used open-source visualization package, ggplot2, arose from a dissertation at Iowa State University. GG not only provided for the first time a formal mathematical foundation for generating statistical charts, but also introduced a wider range of graphics than seen in previous graphical systems. This talk will briefly review that history and then outline recent efforts at H2O to apply GG to very large datasets where classical rendering and analytic methods are infeasible.

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/

Dr. David Eisenbud

Reducing AI Bias Using Truncated Statistics

An emergent threat to the practical use of machine learning is the presence of bias in the data used to train models. Biased training data can result in models which make incorrect or disproportionately correct decisions, or that reinforce the injustices reflected in their training data. For example, recent works have shown that semantics derived automatically from text corpora contain human biases, and found that the accuracy of face and gender recognition systems are systematically lower for people of color and women. While the root causes of AI bias are difficult to pin down, a common cause of bias is the violation of the pervasive assumption that the data used to train models are unbiased samples of an underlying “test distribution,” which represents the conditions that the trained model will encounter in the future.  Overcoming the bias introduced by the discrepancy between train and test distributions has been the focus of a long line of research in truncated Statistics. We provide computationally and statistically efficient algorithms for truncated density estimation and truncated linear, logistic and probit regression in high dimensions, through a general, practical framework based on Stochastic Gradient Descent.  We illustrate the efficacy of our framework through several experiments.

Bio: David Eisenbud served as Director of MSRI from 1997 to 2007, and began a new term in 2013. He received his PhD in mathematics in 1970 at the University of Chicago under Saunders MacLane and Chris Robson, and was on the faculty at Brandeis University before coming to Berkeley, where he became Professor of Mathematics in 1997. He served from 2009 to 2011 as Director for Mathematics and the Physical Sciences at the Simons Foundation, and is currently on the Board of Directors of the Foundation. He has been a visiting professor at Harvard, Bonn, and Paris. Eisenbud’s mathematical interests range widely over commutative and non-commutative algebra, algebraic geometry, topology, and computer methods.

Eisenbud is Chair of the Editorial Board of the Algebra and Number Theory journal, which he helped found in 2006, and serves on the Board of the Journal of Software for Algebra and Geometry, as well as Springer-Verlag’s book series Algorithms and Computation in Mathematics.

Eisenbud was President of the American Mathematical Society from 2003 to 2005. He is a Director of Math for America, a foundation devoted to improving mathematics teaching. He has been a member of the Board of Mathematical Sciences and their Applications of the National Research Council, and is a member of the U.S. National Committee of the International Mathematical Union. In 2006, Eisenbud was elected a Fellow of the American Academy of Arts and Sciences.

Eisenbud’s interests outside of mathematics include theater, music and juggling. He is the co-author of a paper on the mathematics of juggling. He plays the flute and sings Bach, Brahms, Schubert, Schumann….

http://www.msri.org/people/161

Constantinos Daskalakis

Reducing AI Bias Using Truncated Statistics

An emergent threat to the practical use of machine learning is the presence of bias in the data used to train models. Biased training data can result in models which make incorrect or disproportionately correct decisions, or that reinforce the injustices reflected in their training data. For example, recent works have shown that semantics derived automatically from text corpora contain human biases, and found that the accuracy of face and gender recognition systems are systematically lower for people of color and women. While the root causes of AI bias are difficult to pin down, a common cause of bias is the violation of the pervasive assumption that the data used to train models are unbiased samples of an underlying “test distribution,” which represents the conditions that the trained model will encounter in the future.  Overcoming the bias introduced by the discrepancy between train and test distributions has been the focus of a long line of research in truncated Statistics. We provide computationally and statistically efficient algorithms for truncated density estimation and truncated linear, logistic and probit regression in high dimensions, through a general, practical framework based on Stochastic Gradient Descent.  We illustrate the efficacy of our framework through several experiments.

Bio: Constantinos Daskalakis is a Professor of Computer Science and Electrical Engineering at MIT. He holds a Diploma in Electrical and Computer Engineering from the National Technical University of Athens, and a Ph.D. in Electrical Engineering and Computer Sciences from UC-Berkeley. His research interests lie in Theoretical Computer Science and its interface with Economics, Probability Theory, Machine Learning and Statistics. He has been honored with the 2007 Microsoft Graduate Research Fellowship, the 2008 ACM Doctoral Dissertation Award, the Game Theory and Computer Science (Kalai) Prize from the Game Theory Society, the 2010 Sloan Fellowship in Computer Science, the 2011 SIAM Outstanding Paper Prize, the 2011 Ruth and Joel Spira Award for Distinguished Teaching, the 2012 Microsoft Research Faculty Fellowship, the 2015 Research and Development Award by the Giuseppe Sciacca Foundation, the 2017 Google Faculty Research Award, the 2018 Simons Investigator Award, the 2018 Rolf Nevanlinna Prize from the International Mathematical Union, the 2018 ACM Grace Murray Hopper Award, and the 2019 Bodossaki Foundation Distinguished Young Scientists Award. He is also a recipient of Best Paper awards at the ACM Conference on Economics and Computation in 2006 and in 2013.

Sushma Manjunath

Distributed ML on Kubernetes with H2O

AIR9 is an end-to-end approach to building and deploying data science models. AIR9 accelerates the timeline to data access and replaces the need for managing multiple environment accounts. AIR9 provides capability to discover data, prepare a dataset, and provision a SAS, H2O, Jupyter, or R Studio modeling environment. In this session, we will discuss how we abstracted a kubernetes platform to offer model training platform to our Data scientists. We will demonstrate how we spin-up containerized distributed H2O as well as Sparklingwater environments to execute ML algortihms at scale, in a multi-tenant environment. Sign up to learn how we operationalize and empower our scientists to self-provision distributed H2O as well as Sparkling Water environments to execute their ML pipelines.

Bio: – Accomplished leader with an excellent track record in leading the plan, design, and delivery of technology services and data strategy roadmaps.
– Extensive experience in leading Bigdata and AWS cloud teams, providing IaC and PaaS design and architecture solutions for large enterprises with multi-petabyte data.
– Successful implementation of Enterprise data analytics and machine learning platforms with microservices and container-based analytics solutions.
– Extensive experience in presenting to senior leaders, VPs and CIOs.
– Proven ability to hire, mentor and lead geographically dispersed teams.
– Problem-solving experience in data, application and infrastructure issues on Hadoop as well as relational platforms like Snowflake.
– Drive efforts to research, deliver, and standardize new technologies in support of enterprise Data Strategy and Architecture roadmaps.
– Skilled in partnering with business, technology teams, and vendors to analyze business needs and deliver secure IT services.
– Proven ability to establish and articulate a vision, set goals, develop and execute strategies and track and measure results.

LinkedIn: https://www.linkedin.com/in/sushma-manjunath/

Tom Oscherwitz

Explainable AI and Innovation: A CFPB Perspective

The talk will give a regulatory perspective on the use of Explainable AI in credit decisioning and on opportunities for innovation.

Bio: Tom Oscherwitz is a Senior Advisor and Counsel in the Markets office at the Consumer Financial Protection Bureau. His work focuses on artificial intelligence and its deployment in consumer financial markets. Mr. Oscherwitz joined the Bureau in 2011, and has held senior positions in both the Markets and Supervision offices. In Supervision, he served as an Assistant Deputy, overseeing the Bureau’s supervision of the consumer reporting industry.

Prior to his government experience, Mr. Oscherwitz was a senior executive at ID Analytics, a financial analytics company, holding the title of Vice President of Government Affairs, Chief Privacy Officer, and head of regulatory compliance. He also served as counsel to Senator Dianne Feinstein (D-Cal.) for five years on the Senate Judiciary Subcommittee on Terrorism, Technology, and Homeland Security.

Mr. Oscherwitz is a certified privacy professional. He received his law degree from Berkeley, and graduated with honors from Stanford University.

Patrick Hall

The Case for Model Debugging

Prediction by machine learning models is fundamentally the execution of computer code. Like all good code, machine learning models should be debugged for logical or runtime errors or for security vulnerabilities. Recent, high-profile failures have made it clear that machine learning models must also be debugged for disparate impact across demographic segments and other types of sociological bias. Model debugging enhances trust in machine learning directly by increasing accuracy in new or holdout data, by decreasing or identifying hackable attack surfaces, or by decreasing sociological bias. As a side-effect, model debugging should also increase understanding and explainability of model mechanisms and predictions. This presentation outlines several standard and newer model debugging techniques and proposes several potential remediation methods for any discovered bugs. Discussed debugging techniques include adversarial examples, benchmark models, partial dependence and individual conditional expectation, random attacks, Shapley explanations of predictions and residuals, and models of residuals. Proposed remediation approaches include alternate models, editing of deployable model artifacts, missing value injection, prediction assertions, and regularization methods.

Bio: Patrick Hall is the Senior Director of Product 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/

Tom Prendergast

Explainable AI Panel

LinkedIn: https://www.linkedin.com/in/tom-prendergast-69082611/

Ingrid Burton

The Business of AI Panel

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 also serves as an independent director on the Aerohive board. Prior to H2O.ai 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/

Dimitris Tsementzis

Some Problems with Machine Learning in Finance

Bio: Dimitris Tsementzis is a machine learning scientist for the Central Machine Learning team at Goldman Sachs, which has a broad mandate to apply ML and AI across the firm. More specifically, he is driving efforts to apply machine learning techniques to automated trading as well as the generation of financial insights. Before joining Goldman Sachs, he was a postdoctoral researcher in statistics at Rutgers University, where he investigated interactions between statistics, machine learning, and geometry. Earlier, he completed his PhD in mathematical logic at Princeton University.

LinkedIn: https://www.linkedin.com/in/dimitris-tsementzis-59b066173/

Ben Lyons

LinkedIn: https://www.linkedin.com/in/benjamin-lyons-451b57b4/

Meg Mude

Diversity and Inclusion in Tech Panel

Bio: Meg brings more than 15 years of data science, data engineering and solutions architecture at a global scale for Fortune 100 companies.  She is presently a Machine Learning/AI Solutions Architecture Team Lead 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/

Sameer Singh

Explaining and Debugging NLP Models

Bio: Dr. Sameer Singh is an Assistant Professor of Computer Science at the University of California, Irvine. He is working on large-scale and interpretable machine learning applied to information extraction and natural language processing. Before UCI, Sameer was a Postdoctoral Research Associate at the University of Washington, working primarily with Carlos Guestrin. He received his PhD from the University of Massachusetts, Amherst in 2014 under the supervision of Andrew McCallum, during which he also interned at Microsoft Research, Google Research, and Yahoo! Labs. He was awarded the Adobe Research Data Science Faculty Award, was selected as a DARPA Riser, won the grand prize in the Yelp dataset challenge, and received the Yahoo! Key Scientific Challenge (umass story, yahoo story).

LinkedIn: https://www.linkedin.com/in/sameersingh/

Scott Lundberg

Explainable Machine Learning with Shapley Values

Shapley values are popular approach for explaining predictions made by complex machine learning models. In this talk I will discuss what problems Shapley values solve, an intuitive presentation of what they mean, and examples of how they can be used through the ‘shap’ python package.

Bio: I am a senior researcher at Microsoft Research. Before joining Microsoft, I did my Ph.D. studies at the Paul G. Allen School of Computer Science & Engineering of the University of Washington working with Su-In Lee. My work focuses on explainable artificial intelligence and its application to problems in medicine and healthcare. This has led to the development of broadly applicable methods and tools for interpreting complex machine learning models that are now used in banking, logistics, sports, manufacturing, cloud services, economics, and many other areas.

David Ferber

Using AI to Help People Live their Financial Best

Bio: David has over 20 years of experience in the credit industry focusing on the technical development and growth of Equifax’s data platforms. This includes Equifax’s state of the art big data analytics platform that provides valuable cost effective insights and analytics, on its wide range of differentiated data sources. At Equifax, David has held multiple positions; Vice President of Technology, Decision 360 Technology Leader, Enterprise Data Leader and most recently Solutions Delivery Leader within Equifax’s Data & Analytics organization. David holds a degree in Computer Science from North Georgia College.

LinkedIn: https://www.linkedin.com/in/david-ferber-88559b1/

Pinaki Ghosh

Using AI to Help People Live their Financial Best

Bio: Pinaki has over 20 years of industry experience of which 17 years at Equifax building data management and real-time decisoning platforms with strong background in software development, IT and data management. This includes building cutting edge Big Data Platforms which provides high speed access to differentiated data for building rich and actionable insights for Equifax and its clients. Pinaki is passionate and expert in the disciplines of Data Quality, Data Management, Entity.

LinkedIn: https://www.linkedin.com/in/pinakighosh/

Ankit Sinha

Ascend Analytical Sandbox

How businesses can recession proof themselves by using the power of the Ascend Analytical Sandbox; and how Experian is leveraging its vast data to make sure every borrower is presented in the best light in front of the lenders.

Bio: Ankit is the Product & Innovation Expert at Experian leading the overall roadmap for the Ascend Analytical Sandbox; a one-stop shop for insights, model development, and results measurement

LinkedIn: https://www.linkedin.com/in/ankitsinha1/

Marc Rind

The Business of AI Panel

Bio: I create people focused business software and solutions. My focus has been in collecting, curating and leveraging massive data sets about people and the world of work; turning them into insights and intelligence about how companies best manage their talent. I have not only built out successful award winning Analytics solutions for ADP’s clients, but have also architected the data and data science ecosystem to enable innovative development to occur across the organization.

I enjoy using my start-up experience and innovative mindset in creating, evangelizing, and rolling-out new solutions while collaborating and partnering across very large organizations.

LinkedIn: https://www.linkedin.com/in/marcrind/

Ashrith Barthur

Building Recipes for Anti-Money Laundering Usecases

How do you solve Anti-Money Laundering using Driverless AI? In this presentation we will see how to reduce false positive alerts, which is a big problem for financial institutions. Using this approach you can quickly and easily design models that will reduce false positive alerts significantly, while keeping the false negative number low.

Bio: Ashrith is the security scientist designing anomalous detection algorithms at H2O.ai. He recently graduated from the Center of Education and Research in Information Assurance and Security (CERIAS) at Purdue University with a PhD in Information security. He is specialized in anomaly detection on networks under the guidance of Dr. William S. Cleveland. He tries to break into anything that has an operating system, sometimes into things that don’t. He has been christened as “The Only Human Network Packet Sniffer” by his advisors. When he is not working he swims and bikes long distances.

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

Kim Montgomery

Kaggle Grandmaster Panel

Bio: Kim Montgomery has a Ph.D. in applied mathematics, with a background in both predictive modeling and differential equations. She has significant experience applying mathematical modeling to problems in the energy industry and in the biosciences.

She is a Kaggle Grandmaster and has been ranked as high as 15th in the overall Kaggle rankings. She’s excited to be applying her skills at H2O.ai.

Linkedin: https://www.linkedin.com/in/kim-montgomery-70b8189/

Kang Yuan Wong

The Business of AI Panel

Bio: Mr. Wong is an actuary working as The Head of Analytics & Strategy in Tokio Marine Life Malaysia, an insurance company based in Malaysia. He pioneered the Company’s move into its AI journey since the beginning of this year. Prior to this, he was working as the Appointed Actuary of the Company.

LinkedIn: https://www.linkedin.com/in/kang-yuan-wong-0485b85/

Niraj Swami

Conservation & AI

Bio: Niraj Swami is an avid technologist & innovator with a passion for building solutions at the intersection of Artificial Intelligence, human-centered design and behavioral economics. Niraj has led AI and innovation strategy for enterprises, non-profits and startups in the human capital, healthcare and conservation industries. He enjoys exploring the AI-Human relationship with ventures that weigh on purpose, fairness and balance. Niraj is the founder of SCAD AI, a boutique purpose-tech venture firm, and a Senior Advisor for Applied AI & Innovation Ventures at The Nature Conservancy, a global non-profit solving the planet’s greatest conservation problems. Niraj holds an Honors MBA from the University of Chicago Booth School of Business and a Summa Cum Laude Software Engineering degree from Marquette University. When not tinkering with ideas, he enjoys writing music and traveling in search of orcas in the wild.

LinkedIn: https://www.linkedin.com/in/nirajswami/

Nick Schmidt

Responsible Data Science: Identifying and Fixing Biased AI

Numerous stories in the press have shown that machine learning has the potential to be unfair and even discriminatory. As a result, the public, regulators, and legislators are taking a hard look at AI; if your models are used for high-stakes decision making, then you will need to be able to convince these groups that your models are not discriminatory. To do this, you need to know how to assess models for evidence of discrimination and then be able to fix any problems you may find. In this talk, Nick will outline what is required for a model to be fair, discuss how different types of discrimination might make their way into a model, and then explain the algorithms and techniques that can be used to make AI fairer.

Bio: Nicholas Schmidt is a partner at BLDS, LLC, and heads the Artificial Intelligence Practice. In these roles, Nick specializes in the application of statistics and economics to questions of law, regulatory compliance, and best practices in model governance.

As head of the A.I. practice, Nick develops and assists in the deployment of methods that allow his clients to make their A.I. models fairer and more inclusive. In this work, he has created A.I.-based techniques that enable clients to minimize disparate impact in credit, insurance, and marketing models. He has additionally helped his clients understand and implement methods that open “black-box” A.I. models, enabling a clearer understanding A.I.’s decision-making process. His clients use this work to inform their customers on potential denials of credit (“adverse action notices”). These methods are used in a number of the top-10 U.S. retail banks and FinTechs.

In his litigation practice, Nick testifies and consults on matters relating to employment discrimination litigation, wage and hour law, and other matters requiring the utilization of statistics to address questions of liability or damages.

Nick holds an MBA in economics and econometrics from the University of Chicago Booth School of Business.

LinkedIn: https://www.linkedin.com/in/nickpschmidt/

Pavel Pleskov

Kaggle Grandmaster Panel

Bio: Pavel graduated from Lomonosov Moscow State University (2010) and New Economic School (2012). For a year, he worked as a financial consultant at Oliver Wyman. Then he began to build algorithms in the high-frequency trading industry (HFT). For two years, he was a co-founder and CBDO of ThunderBid.

After that, he found his calling in Data Science. In a year and a half, Pavel became the 1st in Russia and the 3rd in the world competition ranking at Kaggle. Since then, he was professionally participating and helping to organize online competitions and hackathons in DS / ML. Pavel is a huge fan of traveling, motorcycles, and kitesurfing.

Josie Williams

Diversity and Inclusion in Tech Panel

Bio: Josie Williams is currently a research assistant at NYU Medical Center working with a team to implement machine learning into healthcare with the goal to create technology that can predict chronic kidney disease more than two years in advance. Her particular role deals with algorithmic fairness and ensuring universal accuracy for all demographic subgroups. Josie also previously worked as the Engineering Lead at Draft.Fish, where she managed a team of developers and assisted in programming a time management application called Robin, which is now in its beta phase.

Josie is also founder and president of an organization called Students of Color in Computer Science, which is based at NYU. One of the things she finds of utmost importance is bringing computer education and general exposure to software development to underrepresented demographics. There, the club serves to promote the growth and development of a community of people of color passionate about computer science. Our overall goal is to increase the number of people of color in the computer science field by holding workshops to teach basic programming concepts, volunteering at organizations geared towards minorities, and providing opportunities for internships outside of an academic setting.

LinkedIn: https://www.linkedin.com/in/josie-williams-429625111/

Mark Landry

Kaggle Grandmaster Panel

Bio: Mark Landry is a competition data scientist and product manager at H2O.ai and Kaggle Grandmaster, ranked as high as 33rd. Mark joined H2O.ai in 2015 and has provided data science support on several H2O.ai products as well as led the data science behind the award-winning applications in collaboration with PwC. Mark’s prior experience includes data science, business intelligence, and data warehousing roles within health care, hospitality, and manufacturing companies.

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

Scott Pete

The Business of AI Panel

Bio: Scott Pete is Director and Head of Insights & Analytics for the America’s at Aimia, Inc. He is responsible for leading the application and innovation of analytics to help understand customer engagement and identify opportunities to create business impact. Over the past couple of years he has evangelized the application of emerging technologies such as machine learning and AI, through the development of client predictive solutions, Customer Journey Analytics and the creation of a global Analytics Workbench. Scott also teaches a Data Visualization and Analytics course at the University of Minnesota.

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

Sudalai Rajkumar (SRK)

Kaggle Grandmaster Panel

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. In the latest version of our Driverless AI platform, we have included Natural Language Processing (NLP) recipes for text classification and regression problems. With this new capability, Driverless AI can now address a whole new set of problems in the text space like automatic document classification, sentiment analysis, emotion detection and so on using the textual data. Stay tuned to the webinar to know more.

Bio: Sudalai Rajkumar (aka SRK) is a Senior Data Scientist at H2O.ai, 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/

Megan Kurka

AutoDoc with H2O Driverless AI

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.

Bio: 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/

Matt Dowle

data.table for R and Python

data.table provides a high-performance version of R’s data.frame with syntax and feature enhancements for ease of use, convenience and programming speed. A particular focus is ordered queries which are inconvenient or slow in SQL. H2O has ported datatable to Python. Matt will summarize recent work and benchmarks: https://h2oai.github.io/db-benchmark/.

Bio: Matt Dowle is the main author of the data.table package in R. He has worked for some of the world’s largest financial organizations: Lehman Brothers, Salomon Brothers, Citigroup, Concordia Advisors and Winton Capital. He is particularly pleased that data.table is also used outside Finance, for example Genomics where large and ordered datasets are also researched. Matt has been programming in S/R for 15 years, knows C pretty well and holds a first class BSc in Applied Maths and Computing from Warwick University, U.K.

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

Nick Anderson

Distributed ML on Kubernetes with H2O

AIR9 is an end-to-end approach to building and deploying data science models. AIR9 accelerates the timeline to data access and replaces the need for managing multiple environment accounts. AIR9 provides capability to discover data, prepare a dataset, and provision a SAS, H2O, Jupyter, or R Studio modeling environment. In this session, we will discuss how we abstracted a kubernetes platform to offer model training platform to our Data scientists. We will demonstrate how we spin-up containerized distributed H2O as well as Sparklingwater environments to execute ML algortihms at scale, in a multi-tenant environment. Sign up to learn how we operationalize and  empower our scientists to self-provision distributed H2O as well as Sparklingwater environments to execute their ML pipelines.

Bio: I am a current Cloud Data Engineer at Discover while working on my Master’s in Computer Science with a concentration in Machine Learning & AI. Through my schoolwork at Georgia Tech, I have gained experience in data analytics, mathematical models, and process automation. I have built multiple machine learning, optimization, and regression models while engineering data pipelines. For these projects, I have utilized open-sourced technologies such as Python, Hadoop, and Bash. I’m currently exploring the big data ecosystem as well as more advanced topics of computer vision and deep learning.

LinkedIn: https://www.linkedin.com/in/nanders/

Tintu Pathrose

Distributed ML on Kubernetes with H2O

AIR9 is an end-to-end approach to building and deploying data science models. AIR9 accelerates the timeline to data access and replaces the need for managing multiple environment accounts. AIR9 provides capability to discover data, prepare a dataset, and provision a SAS, H2O, Jupyter, or R Studio modeling environment. In this session, we will discuss how we abstracted a kubernetes platform to offer model training platform to our Data scientists. We will demonstrate how we spin-up containerized distributed H2O as well as Sparklingwater environments to execute ML algortihms at scale, in a multi-tenant environment. Sign up to learn how we operationalize and  empower our scientists to self-provision distributed H2O as well as Sparklingwater environments to execute their ML pipelines.

Bio: I am Tintu Pathrose, Product Owner and Manager of Discover’s self-service data science workbench, AIR9. I lead a group of highly talented engineers. We are focused on building an analytics platform which enables our data scientists in Discover to adapt and adopt changes in the ML space to produce better and efficient models in less time.

Linkedin: https://www.linkedin.com/in/tintu-pathrose-29658a2/

Niki Athanasiadou

Diversity and Inclusion in Tech Panel

Bio: Niki is a Customer Data Scientist at H2O AI with a passion for data-driven knowledge. Coming from a PhD on the microscopic universe of biomolecules, Niki is bringing scientific thinking to real-world big data. Niki has experience in healthcare among other sectors and loves to work in interdisciplinary teams. Her proudest moments are winning the Young Biochemist of the Year award by the British Biochemical Society and the Open Data data-science project award from the Office of the Mayor of New York city.

Niki is a fan of Sir Arthur Conan Doyle and Agatha Christie and will never turn down an offer to explore a new art exhibit.

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

Gautam Borgohain

Machine Learning Apps at PropertyGuru

PropertyGuru is the largest prop tech company in South-east Asia. We enable our customers to find their dream homes and add value to the agents who trust our platform to match them to the right property seekers. In this session, I will talk about how we are using machine learning to build products and experiences that help people make confident property decisions. I will cover how we guide property seekers and agents with innovative ways to search listings and personalised recommendations, and how we build models to maintain the quality of the listings that they interact with.

Bio: Gautam Borgohain is a Data Scientist and Software engineer with over 7 years of experience building and leading data science products in various industries and projects like recommendation systems, image-classification and object detection services, NLP, property valuation and credit risk evaluation among others. He obtained his Master Degree in Analytics from Nanyang Technological university in Singapore. Before joining PropertyGuru, Gautam gained cross-industry experience with previous stints in a fintech start-up , an university and a software company. He loves spending hours analysing data and developing smarter applications with machine learning.

LinkedIn: https://www.linkedin.com/in/gautamborgohain90/

Bryce Stephens

Explainable AI Panel

Bio:  Bryce Stephens provides economic research, econometric analysis, and compliance advisory services, with a specific focus on issues related to consumer financial protection, such as the Equal Credit Opportunity Act (ECOA), and emerging analytical methods. Prior to joining BLDS, Dr. Stephens spent over seven years as an economist and Section Chief in the Office of Research at the Consumer Financial Protection Bureau. At the Bureau, he led a team of economists and analysts that conducted analysis and supported policy development on fair lending-related supervisory exams, enforcement matters, rulemakings, and other policy initiatives.

LinkedIn: https://www.linkedin.com/in/brycestephens/

Julien Alexandre

When two worlds collide: Machine Learning in the Corporate Bond Market

Pricing in the corporate bond market is challenging because of its massive and opaque nature. At MarketAxess, we use Machine Learning along with our market microstructure expertise to come up with the most accurate and consistent price.

Bio: Julien Alexandre is a Senior Research Analyst at MarketAxess after joining the firm in 2015. Mr. Alexandre is responsible for leading the development of Composite+, MarketAxess’ award-winning A.I.-powered pricing engine. Composite+ accurately predicts two-way prices for more than 24,000 fixed income securities by leveraging data from a variety of global sources including TRACE, Trax and the MarketAxess trading platform. Composite+ is integrated throughout the MarketAxess trading platform, including as a reference price for auto-execution capabilities, Open Trading pricing provision and transaction cost analysis reporting. Prior to joining MarketAxess, Mr. Alexandre was on the Algorithmic Trading Research team at ITG as a Quantitative Analyst, where he worked on the company’s equity algorithmic dark pool aggregator. Mr. Alexandre holds a master’s degree in financial engineering from Columbia University and a Masters in Engineering from the Ecole Centrale, Paris.

LinkedIn: https://www.linkedin.com/in/julien-alexandre-83891235/

Andy Lynch

Right Customer, Right Offer, Right Time

Democratizing data science at Dish to identify and intervene with the highest value customers to save money, prevent churn, and drive additional revenue.

Bio: Energetic finance professional with five years’ success transforming data analysis and market intelligence into sustainable growth. Broad experience decomposing tasks and driving progress across finance, public accounting, marketing, and strategic planning functions. Collaborative partner excels in presentations and public speaking.

LinkedIn: https://www.linkedin.com/in/andrewlynchnd/

Dibjot Singh

Right Customer, Right Offer, Right Time

Democratizing data science at Dish to identify and intervene with the highest value customers to save money, prevent churn, and drive additional revenue.

Shivam Bansal

Kaggle Grandmaster Panel

Bio: Shivam Bansal is a Data Scientist at H2O.ai and Kaggle Grandmaster in Kernels Section. He is the three times winner of Kaggle’s Data Science for Good Competition and winner of multiple other offline AI and Data Science competitions.

Shivam has extensive cross-industry and hands-on experience in building data science products. He has helped clients in the Insurance, Healthcare, Banking, and Retail domains to solve unstructured data science problems by building end to end pipelines and solutions. Shivam really likes to work on all aspects of a data science project which includes both technical aspects as well as business aspects.
Shivam obtained his masters degree in Business Analytics from National University of Singapore in 2019 and his bachelors was in Computer Science.

Linkedin: https://www.linkedin.com/in/shivambansal1/

Marek Novotný

Enhancing Spark Pipeline API with Sparkling Water

Spark pipelines represent a powerful concept to support productionizing machine learning workflows. Their API allows to combine data processing with machine learning algorithms and opens opportunities for integration with various machine learning libraries. However, to benefit from the power of pipelines, their users need to have a freedom to choose and experiment with any machine learning algorithm or library. Therefore, we developed Sparkling Water that embeds H2O machine learning library of advanced algorithms into the Spark ecosystem and exposes them via pipeline API. Furthermore, the algorithms benefit from H2O MOJOs – Model Object Optimized – a powerful concept shared across entire H2O platform to store and exchange models. The MOJOs are designed for effective model deployment with focus on scoring speed, traceability, exchangeability, and backward compatibility. In this talk we will explain the architecture of Sparkling Water with focus on integration into the Spark pipelines and MOJOs. We’ll demonstrate creation of pipelines integrating H2O machine learning models and their deployments using Scala or Python. Furthermore, we will show how to utilize pre-trained model MOJOs with Spark pipelines.

Bio: Marek is a senior software engineer focusing on the development of the Sparkling Water project. He obtained a master degree in computer science at Charles University in Prague. Before Marek joined the team, he spent several years in financial industry developing scalable and fault-tolerant software systems. He is excited about learning new things and open-source software.

Linkedin: https://www.linkedin.com/in/marek-novotn

Prithvi Prabhu

Innovations in Machine Learning

Bio: Prithvi is Chief of Technology, Applications at H2O.ai. Prithvi leads the design and development of “Q”, H2O.ai’s high scale exploratory data analysis and analytical application development platform.

Prithvi has been with H2O.ai since its early days and has been responsible for several products including Driverless AI (our flagship automatic machine learning platform), Steam (distributed cluster management, model management and deployment for H2O), H2O.js (Javascript transpiler for H2O’s distributed runtime), Play (on-demand cloud provisioning system for H2O), Flow (a hybrid GUI/REPL/Notebook for H2O) and Lightning (statistical graphics for H2O).

In the past, Prithvi was an early engineer at Platfora, where he was responsible for the high-performance interactive data visualization engine. Before that, he founded Plot.io, a browser-based visual analytics environment (acquired by Platfora).

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

Pramit Choudhary

Innovations in Machine Learning

Bio: Pramit Choudhary, is an Applied Machine Learning Research Scientist/Engineer and currently Lead Data Scientist @h2o.ai.
His area of interest is building scalable Statistical/Machine Learning models(Bayesian and Frequentist Modeling techniques) and adversarial learning
to help businesses realize their data-driven goals.

He has been exploring better ways to understand and explain model’s learned decision policies to reduce the chaos in building effective models to close the gap between a prototype and operationalized model(prescriptive Machine Learning). Previously, he had authored an open source library called Skater on the idea of Model Interpretation and adversarial learning which was well received.
In his past life he has worked on ML problems related to contextual bandits, NLP (e.g. topic modeling), improving operation efficiency in Oil and Gas Industry (e.g. time series/ anomaly detection), social media analysis, personalized recommendation engines, match-making and fraud detection to name a few.

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

Branden Murray

Kaggle Grandmaster Panel

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/

Rohan Rao

Kaggle Grandmaster Panel

Bio: Rohan Rao is a Machine Learning Engineer and Kaggle Grandmaster with over 5 years of experience building data science products in various industries and projects like digital payments, e-commerce retail, credit risk, fraud prevention, growth, logistics and more. He enjoys working on competitions, hackathons and collaborating with folks around the globe on building solutions.

He completed my post-graduation in Applied Statistics from IIT-Bombay in 2013.

Solving sudokus and puzzles has been his big hobby for over a decade. Having won the national championship multiple times, he has represented India and been in the top-10 in the World, as well as finished twice on the podium at the Asian Championships.

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

Yauhen Babakhin

Kaggle Grandmaster Panel

Bio: Yauhen holds a Master’s Degree in Applied Data Analysis and has over 4 years of working experience in Data Science. He worked in Banking, Gaming and eCommerce domains. He’s also the first Kaggle competitions Grandmaster in Belarus having gold medals in both classic Machine Learning and Deep Learning competitions.

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

Dmitry Larko

Time Series in H2O Driverless AI

Time series is a unique field in predictive modelling where standard feature engineering techniques and models are employed to get the most accurate results. In this session we will examine some of the most important features of Driverless AI’s newest recipe regarding Time Series. It will cover validation strategies, feature engineering, feature selection and modelling. The capabilities will be showcased through several cases.

Bio: Dmitry has more than 10 years of experience in IT. Starting with data warehousing and BI, now in big data and data science.He has a lot of experience in predictive analytics software development for different domains and tasks.

He is also a Kaggle Grandmaster who loves to use his machine learning and data science skills on Kaggle competitions.

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

Olivier Grellier

Kaggle Grandmaster Panel

Bio: Olivier graduated from Supelec, France and holds a PhD in Signal Processing. He worked in the Airline IT business at Amadeus as a C/C++ developer then joined the London branch as a team leader. In Capgemini, he worked with clients in the public sector as a senior project manager.
Olivier then moved to trading the commodity markets, building and  backtesting trading systems, and progressively started using more  machine learning tools. His data science journey really began on Kaggle  where he could practice and improve his skills on various competitions and datasets.
In the last year, he worked for caring.com using machine learning and data mining to help families find communities for their parents.
Olivier loves spending his spare time in the yard where he grows organic apples, peers, grapes and strawberries. He also produces his own special jam.

Linkedin: https://www.linkedin.com/in/olivier-grellier/