Peter comes from a technical background as a consultant, founder, and individual contributor on a wide range of software projects for companies large and small, government institutions, and nonprofits. He now works at DataRobot across the organization and with customers to advocate for developers who use DataRobot’s products and services, and to ensure that DataRobot is constantly improving its offerings for technical users who are focused on delivering software.
Jen Underwood has a unique blend of product management and “hands-on” experience in data warehousing, reporting, visualization, and advanced analytics. In addition to keeping a constant pulse on industry trends, she enjoys digging into oceans of data to solve complex problems with machine learning.Over the past 20 years, Jen has held worldwide product management roles at Microsoft andserved as a technical lead for system implementation firms. She has experience launching new products and turning around failed projects. Most recently she provided advisory, strategy, educational content development, and marketing services to 100+ technology vendors through her own firm. She has been mentioned by KD Nuggets, Information Management and Forbes for her work. She also has written for InformationWeek, O’Reilly Media, and numerous other tech industry publications. Jen has a Bachelor of Business Administration – Marketing, Cum Laude from the University of Wisconsin, Milwaukee and a post-graduate certificate in Computer Science – Data Mining from the University of California, San Diego. She was also honored to be a former IBM Analytics Insider, Tableau Zen Master, and Top 10 Women Influencer.
Sophie is a software engineer at Red Hat, where she works in an emerging technology group. She has a background in Mathematics and has recently completed a PhD in Bayesian statistics, in which she developed algorithms to estimate intractable quantities quickly and accurately. Since joining Red Hat in 2017, Sophie has focused on applying her data science and statistics skills to solving business problems and informing next-generation infrastructure for intelligent application development.
Meeta is a passionate, customer-obsessed product leader with a track record of strategizing, building and launching innovative products that solve real business problems.
As VP Product at Appen she is building a machine learning data annotation platform focused on Computer Vision, Autonomous Vehicles, Conversational AI and NLP. Prior to Appen, Meeta held several product leadership roles in Cisco Systems, Tokbox/Telefonica and Computer Associates with a focus on AI, Chatbots, Voice/Video and Data Analytics. She has an MBA Degree from UC Davis and engineering degree from National Institute of Technology, India.
Building Ethics and Diversity in AI
Aaron Richter is a software developer turned data engineer and data scientist. He has pioneered the development and implementation of large-scale data science infrastructure in both business and research environments. Inevitably, he spent a lot of time finding efficient ways to clean data, run pipelines, and tune models. Aaron holds a PhD in machine learning from Florida Atlantic University.
William Benton leads a team of data scientists and engineers at Red Hat, where he has applied analytic techniques to problems ranging from understanding infrastructure logs at datacenter scale to designing better cycling workouts. His current focus is designing architectures for machine learning systems in the hybrid cloud, but he has also conducted research and development in the areas of static program analysis, managed language runtimes, logic databases, cluster configuration management, and music technology.
Sean Smith is a Director of Success at DataRobot where he supports customers development of actionable intelligence with automated machine learning. Sean has held multiple positions in advanced analytics & machine learning with some of today’s largest technology companies and is a contributing author to Towards AI, one of the top to artificial intelligence publications on Medium.
The Practitioner’s Data Prep Workshop(Workshop)
Emily Webber is a Data Scientist with DataRobot. Her background is originally in neuroscience, but changed fields to data science after completing her postdoctoral fellowship. She took an assistant directorship at a large non-profit and built custom machine learning models to raise money for biomedical research and hasn’t looked back since. Emily’s passion is to help people understand and use their data to solve problems.
Keenan Freyberg is a Product Manager at Kensho Technologies where he works with the machine learning team’s Applied R&D group. He works with the team in developing a suite of natural language processing (NLP) capabilities designed to help businesses make decisions with conviction.
Keenan joined Kensho in 2018 as a Client Operations Associate helping sell-side clients best leverage Kensho technology. Prior to Kensho, Keenan worked as a consultant at Capco helping clients design trading systems.
He holds a B.A. in International Affairs with a concentrations in Economics and Russian from George Washington University.
Ben Taylor has over 16 years of machine learning experience. After studying chemical engineering, Taylor joined Intel and Micron and worked in their photolithography, process control, and yield prediction groups. Pursuing his love for high-performance computing (HPC) and predictive modeling, Taylor joined an artificial intelligence hedge fund (AIQ) as their HPC/AI expert and built out models using a 600 GPU cluster to predict stock movements based on the news. Taylor then joined a young HR startup called HireVue. Taylor built out their data science group, filed 7 patents, and helped to launch HireVue’s AI insights product using video/audio from candidate interviews. That work allowed Taylor’s team of PhD physicists to help pioneer anti-bias mitigation strategies for AI. In 2017 Taylor co-founded Zeff.ai with David Gonzalez to pursue deep learning for image, audio, video, and text for the enterprise. Zeff was acquired by DataRobot
Haniyeh is a data science researcher at DataRobot’s Trusted AI team. Her research focuses on bias, privacy, stability, and ethics in AI and Machine Learning. She has a demonstrated history of implementing ML and AI in retail, finance, and IT companies with expertise in customer relation, human resources, and fraud detection. She has initiated a project to incorporate bias and fairness into DataRobot’s products and is a thought leader in the area of bias in AI and AI ethics. Haniyeh holds a PhD in Astronomy and Astrophysics from the Rheinische Friedrich-Wilhelms-Universität Bonn.
How to Stop Worrying and Tackle AI Bias (Workshop)
Edward M. Young is the director of Advanced Analytics at FCA Fiat Chrysler. Although most of his career history is with accounting and finance positions for several leading companies, over time, Ed recognized the power that advanced technology could bring to business and he began to pursue his interest in predictive analytics. Today, Ed leads a team of data scientists on important business questions and is extending the reach of AI to 25 business analysts within his organization.
Jett Oristaglio is the Data Science and Product Lead of Trusted AI at DataRobot. He has a background in Cognitive Science, with focuses in computer vision, neuro-ethics, and transcendent states of consciousness. His primary mission at DataRobot is to answer the questions: “What is everything you need in order to trust a decision-making system with your life? And what tools can we build to automated that process as comprehensively as possible?
How to Stop Worrying and Tackle AI Bias (Workshop)
Angela Baltes is a Biomedical Informatics PhD candidate with Rutgers University. She began her career in health-data environments where she provided data-driven solutions and insights to improve patient outcomes. Angela is now employed with the University of New Mexico as an Institutional Researcher specializing in machine learning, data analysis and creating effective visualizations to aid in transparency and leveraging actionable data for dissemination.
Seph Mard joins DataRobot as a recognized industry leader of enterprise model risk management, model validation, model governance and best practices. Seph has more than a decade of experience applying data science to quantitative finance and risk management. As Director of Technical Product, Seph is a leader on DataRobot Product Management team where he is focused exclusively on ML Ops product management and strategy. Seph is bringing innovation into the world of Machine Learning Operations using DataRobot’s superior machine learning automation and data science edge. He holds dual M.Sc. degrees in Applied Mathematics and Econometrics.
Shyam Ayyar is a Product Manager at DataRobot, where his primary focus is data preparation. He’s worked at Paxata for 3 years focussing on Data Preparation. He has over a decade of experience in Data Management & Analytics primarily in the Financial Services industry with companies such as JP Morgan Chase where he has helped to build data pipelines. Shyam studied Computer Science and has worked in a variety of roles in building software products.
The Practitioner’s Data Prep Workshop(Workshop)
Sarah Nooravi is a Sr. Financial Analyst at Snap Inc. with over 4 years of experience in the field of Data Science and Analytics. With a focus in marketing analytics, she has a history of delivering innovative marketing tools to help drive better business decisions in the entertainment, social media and gaming industries. She is also passionate about teaching and giving back to the community. In that spirit, she has coordinated and led monthly Machine Learning workshops, taught a Data Visualization course through USC Viterbi School of Engineering, and mentored women in Data Science and Engineering through organizations like: GLAD Mentorship, Society of Women Engineers, Women in Big Data, and She Loves Data. These activities support a core motivation for Sarah: helping set up others for success in industry.
Zan does Developer Relations at DataRobot in London. His passions are educating and enabling developers. He’s currently nerding out about machine learning 🤖, serverless ⚡ , and the new age of APIs 🌅. If allowed to speak unchecked, he may start dispersing trivia on ✈ and 🍺. If allowed into your docs, he might add excessive amounts of emoji and just the right amount of oxford comma. Which is all of them.
David “Gonzo” Gonzalez is a “reformed” Data Science professional with well over a decade of experience putting AI and ML into production in mission-critical systems. At DataRobot he is working to empower traditional software development teams comprised of product visionaries and engineers with tools and solutions that will allow them to more easily incorporate AI into what they build. Prior to DataRobot he worked on multiple auto-ml platforms and pioneered transactionally authored training and inference multi-modal datasets at Zeff.ai where he was CEO and co-founder. Gonzo is the primary author of The Manifesto for Applied Artificial Intelligence Development and has multiple ML patents. In his free time, he enjoys playing outdoors in his adoptive home of Utah with his wife and children.
Keenan is a published scientist, inventor, entrepreneur and data analytics business lead for a fortune 50 company. His current job involves applying the latest Machine Learning techniques to improving and augmenting Freddie Mac’s business operations for Single Family. In 2019, he co-founded The Python Academy where he organizes and teaches classes in data science to anyone looking for a career switch or looking to learn the skills from scratch. Keenan also publishes in the popular Towards Data Science blog, is an avid learner and a big proponent of autoML, being an early adopter of the Data Robot platform.
Jingqing Zhang is a 3rd-year PhD (HiPEDS) at Department of Computing, Imperial College London under the supervision of Prof. Yi-Ke Guo. His research interest includes Natural Language Processing, Text Mining, Data Mining and Deep Learning. He received his BEng degree in Computer Science and Technology from Tsinghua University, 2016, and MRes degree with distinction in Computing from Imperial College London, 2017.
Angel Aponte has focused on extracting key insights and generating prescriptive analytics for over 25 years. After extensive Researching in Academia and Geomodeling in Oil and Gas industry careers, Angel shifted his attention to Advanced Visualization, Cloud Technology/Computing, Quantum and Statistical Mechanics, ML+AI, Augmented Reality, and other cutting-edge technologies. But all the while his passion and goal remains to get the most value out of data using Analytics and Advanced Visualization techniques in order to address real-life issues that can have the biggest impact.
Felix Huthmacher is responsible for the AI Engineering team at DataRobot. He is a ML enthusiast, and hobby app developer, who has worked well over a decade in IT consulting across various industries. As AI Engineer, he helps clients realize business value by successfully implementing ML solutions powered by the DataRobot platform supporting business processes in marketing, sales and service.
Yamini Rao is a Developer Advocate. She compiles developer scenarios, workshops and training material based on IBM Cloud technologies to demonstrate value. She is active in the local developer communities by collaborating with them to present and organise Workshops, Meetups and Conferences. She has a background in computer science and has worked extensively as an Implementation Engineer for various IBM Analytical tools.
Josh is a sales engineer turned product manager with a background in business operations and data preparation. Josh is focused on building the product strategy, vision, and impactful features for DataRobot’s AI Catalog and strategic technology alliances
Experienced consultant, developer, and sales engineer with a demonstrated history of success across many roles and many industry clients. Specializing in Data Warehousing, Data Analysis, Machine Learning, and the Data Engineering space.
Beau Walker runs a full-stack IP (intellectual property) development lab, Method Data Science (www.methoddatascience.com). They invent, build, and protect creative data science solutions that bring their clients long-term value. Your company’s unique intellectual property is a crucial asset, that when developed and protected strategically can transform your success as a business. He has a JD in Intellectual Property Law and have years of experience inventing, drafting patents, and developing and executing IP strategy. Over the past few years he has invented hundreds of patentable inventions for his clients, some of which have been granted patents. As a data scientist, he is a generalist: his experience includes custom intellectual property development (he’s invented a number of custom proprietary machine learning algorithms), AI / machine learning, business analytics, data collection, data engineering and architecture, software development, study design, data preparation, experimental design, algorithm and model development, reporting, and data visualization. He has worked with dozens of startups and small businesses in a variety of industries, including biotech and pharma, legal and legal services, insurance, financial services, sports and entertainment, IOT, and marketing and sales