Personal Information
Organization / Workplace
Greater New York City Area, IL United States
Industry
Technology / Software / Internet
Website
www.uchicago.edu
About
I currently work on big data and distributed systems. Specifically, to accelerate machine learning algorithms using scale-up (e.g., GPU) and scale-out (e.g., Spark) systems. As an example, I built cuMF (https://github.com/wei-tan/CuMF/), a scalable matrix factorization library on GPU. As far as I know, cuMF is the fastest and can tackle the largest MF problem ever reported. CuMF can be used in recommender systems, embedding layer in deep learning, and topic model.
I also worked on NoSQL (e.g., HBase) and services computing.
My work and code have been incorporated into IBM patent portfolio and products such as BigInsights and Cognos. I am also a very hands-on researcher (see my GitHub p...
Tags
machine learning
gpu
big data
matrix factorization
recommender systems
cloud computing
services
api
web
collaborative filtering
recommendation
apache spark
See more
- Presentations
- Documents
- Infographics
IBM Runtimes Performance Observations with Apache Spark
AdamRobertsIBM
•
7 years ago
10 more lessons learned from building Machine Learning systems
Xavier Amatriain
•
8 years ago
Accelerating Machine Learning Applications on Spark Using GPUs
IBM
•
8 years ago
Justin Basilico, Research/ Engineering Manager at Netflix at MLconf SF - 11/13/15
MLconf
•
8 years ago