Personal Information
Unternehmen/Arbeitsplatz
Greater New York City Area, IL United States
Branche
Technology / Software / Internet
Webseite
www.uchicago.edu
Info
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
Mehr anzeigen
Präsentationen
(3)Gefällt mir
(4)IBM Runtimes Performance Observations with Apache Spark
AdamRobertsIBM
•
Vor 7 Jahren
10 more lessons learned from building Machine Learning systems
Xavier Amatriain
•
Vor 8 Jahren
Accelerating Machine Learning Applications on Spark Using GPUs
IBM
•
Vor 8 Jahren
Justin Basilico, Research/ Engineering Manager at Netflix at MLconf SF - 11/13/15
MLconf
•
Vor 8 Jahren
Personal Information
Unternehmen/Arbeitsplatz
Greater New York City Area, IL United States
Branche
Technology / Software / Internet
Webseite
www.uchicago.edu
Info
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
Mehr anzeigen