Gang Wu

Research Scientist 2

San Jose

Gang Wu joined Adobe Research as Research Scientist in December, 2017. His research spans different machine learning fields including statistical modeling, optimization, deep learning, and generative modeling, with data science applications such as digital marketing, user behavior modeling, recommender systems, and computer vision applications such as image compression, video understanding, etc.

Prior to joining Adobe, he worked on probabilistic methods for matrix completion during my PhD in Electrical & Computer Engineering Department at Iowa State University, advised by Prof. Ratnesh Kumar. He also interned at Adobe Research conducting comprehensive research on recommender systems and user engagement maximization (see “Meet the Intern Who Knows What Videos You Want to Watch”).

More information about him is available on his personal site.

Publications

Structured Policy Iteration for Linear Quadratic Regulator

Park, Y., Rossi, R., Wen, Z., Wu, G., Zhao, H. (Jul. 31, 2020)

International Conference on Machine Learning (ICML)

Linear Quadratic Regulator for Resource-Efficient Cloud Services

Park, Y., Mahadik, K., Rossi, R., Wu, G., Zhao, H. (Nov. 21, 2019)

ACM Symposium on Cloud Computing

Scalable Bid Landscape Forecasting in Real-time Bidding

Ghosh, A., Mitra, S., Sarkhel, S., Xie, J., Wu, G., Swaminathan, V. (Sep. 16, 2019)

ECML-PKDD 2019

Digital content recommendation system using implicit feedback data

Wu, G., Swaminathan, V., Mitra, S., Kumar, R. (Dec. 11, 2017)

IEEE BigData 2017

Context-aware video recommendation based on session progress prediction

Wu, G., Swaminathan, V., Mitra, S., Kumar, R. (Oct. 7, 2017)

IEEE ICME 2017