Kaggle Grandmasters of NVIDIA

Meet the Kaggle Grandmasters of NVIDIA (KGMoN), and learn how they use NVIDIA accelerated data science to build winning recommender systems, predict degradation rates in RNA molecules, identify melanoma in medical imaging, and more.

Meet the KGMoN Team

Bo Liu

Bo Liu

Senior Deep Learning Data Scientist, NVIDIA

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Chris Deotte

Chris Deotte

Senior Data Scientist, NVIDIA

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Christof Henkel

Christof Henkel

Senior Deep Learning Data Scientist, NVIDIA

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Dave Austin

David Austin

Principal Systems Software Engineer, NVIDIA

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Gilberto Titericz

Gilberto Titericz

Senior Data Scientist, NVIDIA

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Jean-Francois Puget

Jean-Francois Puget

Distinguished Engineer, NVIDIA

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Jiwei Liu

Jiwei Liu

Senior System Software Engineer, NVIDIA

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Kazuki Onodera

Kazuki Onodera

Senior Data Scientist, NVIDIA

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Théo Viel

Théo Viel

Senior Deep Learning Data Scientist, NVIDIA

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Explore the KGMoN team’s recent competitions.

The Rise of DeBERTa for NLP Downstream Tasks

March and May 2022

The Rise of DeBERTa for NLP Downstream Tasks


In two different competitions, the team used natural language processing to analyze argumentative writing elements from students and identified key phrases in patient notes from medical licensing exams.

  Watch Submission Video: The Rise of DeBERTa for NLP Downstream Tasks

(58:24 Minutes)

The Recommender Systems Challenge

June 2021

The RecSys Challenge


The NVIDIA Merlin and KGMON team earned 1st place in the RecSys Challenge 2021 by effectively predicting the probability of user engagement within a dynamic environment and providing fair recommendations on a multi-million point dataset.

  Watch Submission Video: Boosted Trees and Deep Neural Networks for Better Recommender Systems

(1:10:47 Minutes)

Booking.com Destination Recommendation Challenge

March 2021

Booking.com Web Search and Data Mining (WSDM) WebTour 2021 Challenge


In this recommendation system challenge, the goal was to use a dataset based on millions of real anonymized accommodation reservations to come up with a strategy for making the best recommendation for their next destination, all in real-time.

  Watch Submission Video: How to Build a World-Class ML Model for Melanoma Detection

(49:11 Minutes)

Building World-Class NLP Models with Transformers and Hugging Face

March 2021

Building World-Class NLP Models with Transformers and Hugging Face


Watch this video to get a short history lesson and the current state of natural language processing and the best practices for using Hugging Face transformers in four different competitions.

  Watch Video: Building World-Class NLP Models with Transformers and Hugging Face

(1:06:02 Minutes)

COVID-19 mRNA Vaccine Degradation Prediction Competition

October 2020

OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction


In this competition, teams were charged with developing machine learning models and designing rules for RNA degradation. The models needed to predict likely degradation rates at each base of an RNA molecule, trained on a subset of an Eterna dataset comprising over 3000 RNA molecules (which span a panoply of sequences and structures) and their degradation rates at each position.

  Watch Submission Video: How to Predict Which Candidate COVID-19 mRNA Vaccines Are Stable with AI

(56:56 Minutes)

Google Landmark Recognition 2020

September 2020

Google Landmark Recognition 2020


In this landmark recognition challenge, the team had to build models that recognize the correct landmark (if any) in a dataset of complicated test images. This is easier said than done, given landmark recognition contains a much larger number of classes. For example, there were more than 81,000 classes in this competition.

  Watch Submission Video: How to Perform Large-Scale Image Classification

(49:11 Minutes)

SIIM-ISIC Melanoma Classification

August 2020

SIIM-ISIC Melanoma Classificatione


In this competition, the team had to create ML models to identify skin lesions from patients’ images and determine which images are most likely to represent a melanoma. The winning ML model was able to identify melanoma earlier and more accurately than the average dermatologist.

  Watch Submission Video: How to Build a Winning Deep Learning Recommender System, E5

(49:11 Minutes)

Explore the data science Grandmaster Series.

The Grandmaster Series is a monthly educational video series for data scientists. In each episode, some of the world's leading experts in data science share their insights, best practices, and key learnings from a recent competition. Tune in and learn how  to apply their learnings to your own data science challenges.

Explore the Series

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