Meet speakers for San Francisco 2020 edition

Paige Bailey

Paige Bailey is a product manager for TensorFlow at Google Brain, and a long-time Kaggle enthuasiast.

Prior to her role as a PM, Paige was developer advocate for TensorFlow; a senior software engineer and machine learning engineer in the office of the Microsoft Azure CTO; and a data scientist at Chevron. Her academic research was focused on lunar ultraviolet, at the Laboratory for Atmospheric and Space Physics (LASP) in Boulder, CO, as well as Southwest Research Institute (SwRI) in San Antonio, TX.

Xuan Cao

Kaggle Grandmaster

Xuan Cao received his Ph.D. in Materials Science and Engineering from the University of Pennsylvania in 2019 and currently works as a Data Scientist at Walmart Labs. He started his kaggle journey from “Avito Demand Prediction Challenge” in April 2018 and was deeply attracted by this amazing platform. He has taken part in many Kaggle competitions covering topics of Tubular Data, Image Classification/Segmentation and Natural Language Processing. He became a Competition Grandmaster in November 2019 after winning 5 gold medals.

Heads or Tails

Kaggle Grandmaster

Former Research Astronomer turned Data Scientist. 1st Kaggle Kernels Grandmaster and former #1 in Kernels ranking. Prize winner in 3 Kernels competitions. Curious at heart.

Rob Mulla

Kaggle Grandmaster

Rob is a research scientist currently working in the hospitality industry focusing on demand forecasting and revenue management. He holds degrees from UC Berkeley, Kansas State and Virginia Tech. He is a Kaggle Kernels Grandmaster (his highest rank so far is 8th overall). Additionally, he is both a Competitions and Discussions Master. His passions are equally split between exploring data for actionable insights and building models with strong predictive power. He loves that Kaggle is great place to practice both of these skills.

Sanyam Bhutani

Kaggle Expert

Sanyam Bhutani is a ML Engineer and AI Content Creator at He is also an inc42, Economic Times recognized ML Practitioner.

Sanyam is an active Kaggler where he is a Triple Tier Expert, ranked in Top 1% in all categories as well as an active AI blogger with over 1 Million+ Views overall.

Sanyam is also the host of Chai Time Data Science Podcast, which has now been streamed across 110+ countries, where he interviews top practitioners, researchers, and Kagglers.

Polong Lin

Polong Lin is a Developer Advocate at Google focusing on machine learning and data engineering in the cloud. He is the creator of several online courses in data science, including on Coursera.

Polong has a background in Psychology and enjoys hearing about projects at the intersection of machine learning and human behaviour. Share your use case with him on Twitter @PolongLin

Vladimir Iglovikov

Kaggle Grandmaster

Vladimir got his Ph.D. in Theoretical Condensed Matter Physics at UC Davis. After graduation he was developing Energy Disaggregation algorithms that were a combination of the signal processing and machine learning techniques, working as a data scientist at Bidgely. After this, he moved to San Francisco to work in TrueAccord where he was mainly focussed on building recommender systems. Currently, Vladimir is applying Deep Learning techniques to the computer vision problems at the Lyft’s Level5 Engineering Center that is focused on the development of the self-driving cars.

Ben Hamner

Ben is Kaggle’s co-founder and CTO. He leads the product, engineering, and design teams. He’s directly run over 40 competitions, including automated essay scoring, the GE flight quests, and the Neurips 2017 adversarial learning challenges. Ben created the product vision for Kaggle notebooks and datasets, leading them from initial development to large-scale adoption.

Prior to Kaggle, Ben applied machine learning to improve EEG-based brain-computer interface applications. This includes a virtual keyboard and telepresence robot.

Ben holds degrees in math, biomedical engineering, and electrical & computer engineering from Duke University.

Anna Montoya

Anna is the head of marketing at Kaggle. She’s passionate about helping Kagglers around the world achieve their data science and ML education and career goals. Talk to her about inclusivity, community, and/or circus arts.

Paweł Jankiewicz

Kaggle Grandmaster

Kaggle Grandmaster, finance and banking graduate with over 10 years of experience, both as a specialist and an AI Team Leader. Passionate and self taught Data Scientist – he has taken part in 20+ international data mining / machine learning competitions achieving prize and won 6 of them.

Chris Deotte

Kaggle Grandmaster

Earned a BA in mathematics then worked as a graphic artist, photographer, carpenter, and teacher. Earned a PhD in computational science and mathematics with a thesis on optimizing parallel processing. Now work as a data scientist and researcher with Nvidia. Double Kaggle Grandmaster.

Philipp Singer

Kaggle Grandmaster

Philipp is a data scientist working and living in Vienna, Austria. He obtained a PhD at the Technical University of Graz and has published several papers in the areas of statistics, machine learning, and data science including a best paper award at the renowned World Wide Web conference. Philipp started to compete on Kaggle a bit more than a year ago when he also co-founded “The Zoo” team. Since then, he has steadily climbed the competition leaderboard, being currently ranked 6th. The Zoo has won 3 competitions and acquired 7 gold medals overall.

Meredith Lee

Meredith is the Founding Executive Director of the West Big Data Innovation Hub, a venture launched with support from the National Science Foundation to build and strengthen partnerships across industry, academia, nonprofits, and government. Based at UC Berkeley, she focuses on translational data science, including initiatives in Smart and Connected Communities, Water, Disaster Recovery, Health, and Education. Meredith is a member of the Women in Data Science (WiDS) global conference committee and leads the WiDS Datathon initiative in collaboration with Kaggle. She was previously a AAAS Science & Technology Policy Fellow at the Homeland Security Advanced Research Projects Agency, guiding strategic programs in graph analytics, risk assessment, machine learning, data visualization, and distributed computing. Under the Obama Administration, Meredith led the White House Innovation for Disaster Response & Recovery Initiative and contributed to several science, technology, and open data / open government initiatives.

Meredith completed her B.S., M.S., and Ph.D. in Electrical Engineering at Stanford University. She serves on the Advisory Committee for NASA DIRECT-STEM, the Blockchain Working Group for the State of California, and the National Leadership Council for the Society for Science and the Public. Her work has been featured by, ArsTechnica, The Washington Post, Forbes, WIRED, Bloomberg, and Nature.