Senior Deep Learning Data Scientist, NVIDIA
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
Senior Data Scientist, NVIDIA
Senior Deep Learning Data Scientist, NVIDIA
Principal Systems Software Engineer, NVIDIA
Senior Data Scientist, NVIDIA
Distinguished Engineer, NVIDIA
Senior System Software Engineer, NVIDIA
Senior Data Scientist, NVIDIA
Explore the KGMoN team’s recent competitions.
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.
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.
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.
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.
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.
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.
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.
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.