Gang Wu

Research Scientist 2

San Jose

Gang Wu joined Adobe Research (BEL-SEL)  in San Jose  as a Research Scientist after being a research intern in STL for a few years in the Video Systems group conducting comprehensive research on recommender systems and user engagement maximization. His internship work in the Video Systems group has led to multiple academic papers, patents, a technology transfer to Adobe Target and Primetime, and even a media exposure (see “Meet the Intern Who Knows What Videos You Want to Watch”).  Prior to joining Adobe, Gang was working on his PhD in Electrical Engineering at Iowa State University, where his research focus on probabilistic methods for matrix completion.

His research interests span various domains such as machine learning, statistical signal processing, and applied statistics.

 

Recent Publications:

  • Linear Quadratic Regulator for Resource-Efficient Cloud Services. Youngsuk Park, Kanak Mahadik, Ryan A. Rossi, Gang Wu, Handong Zhao. In Proceedings of the ACM Symposium on Cloud Computing 2019 Posters. (SoCC 2019).
  • Higher-Order Ranking and Link Prediction: From Closing Triangles to Closing Higher-Order Motifs. Ryan A. Rossi, Anup Rao, Sungchul Kim, Eunyee Koh, Nesreen K. Ahmed, Gang Wu. arXiv:1906.05059, 2019.
  • Generative Networks for Synthesizing Human Videos in Text-Defined Outfits. Akshay Malhotra, Viswanathan Swaminathan, Gang Wu and Ioannis D. Schizas. In Proceedings of IEEE 21st International Workshop on Multimedia Signal Processing (MMSP 2019).
  • Improved Bid Landscape Forecasting in Real-Time Bidding. Aritra Ghosh, Saayan Mitra, Somdeb Sarkhel, Jason Xie, Gang Wu and Viswanathan Swaminathan. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (ECML 2019).
  • Learning a Joint Low-rank and Gaussian Model in Matrix Completion with Spectral Regularization and Expectation Maximization Algorithm. Gang Wu, Ratnesh Kumar. 2018. In Proceedings of 2018 IEEE International Congress on Big Data.
  • Digital content recommendation system using implicit feedback data. Gang Wu, Viswanathan Swaminathan, Saayan Mitra, Ratnesh Kumar. In Proceedings of 2017 IEEE International Conference on Big Data (ICBD 2017).
  • Context-aware video recommendation based on session progress prediction. Gang Wu, Viswanathan Swaminathan, Saayan Mitra and Ratnesh Kumar. In Proceedings of 2017 IEEE International Conference on Multimedia & Expo (ICME 2017).
  • Matrix Completion under Gaussian Models Using MAP and EM Algorithms. Gang Wu, Viswanathan Swaminathan, and Ratnesh Kumar. Journal of Communications, vol. 12, no. 3, pp. 180-186, 2017 (JCM 2017) .

My Publications

Published November 21, 2019

Linear Quadratic Regulator for Resource-Efficient Cloud Services

ACM Symposium on Cloud Computing

Youngsuk Park, Kanak Mahadik, Ryan Rossi, Gang Wu, Handong Zhao
  • AI & Machine Learning
  • Systems & Languages

Published September 16, 2019

Scalable Bid Landscape Forecasting in Real-time Bidding

ECML-PKDD 2019

Aritra Ghosh, Saayan Mitra, Somdeb Sarkhel, Jason Xie, Gang Wu, Vishy Swaminathan
  • AI & Machine Learning
  • Data Intelligence

Published December 11, 2017

Digital content recommendation system using implicit feedback data

IEEE BigData 2017

Gang Wu, Vishy Swaminathan, Saayan Mitra, Ratnesh Kumar
  • AI & Machine Learning

Published October 7, 2017

Context-aware video recommendation based on session progress prediction

IEEE ICME 2017

Gang Wu, Vishy Swaminathan, Saayan Mitra, Ratnesh Kumar
  • AI & Machine Learning
  • Computer Vision, Imaging & Video
  • Data Intelligence