The Google LA PhD Summit 2014 was held on February 14th at Google LA site. Jian Li, Hao Xu, Jia He and Xin Zhang from MCL attended the event. They were invited to several events from Google including a keynote talk “Music Understanding”, presentations about “Large-Scale Machine Learning”, “Language Understanding”, “Chrome Security” and “Vision + Quantum”. Besides, they had a good chance to talk with leading Google PhDs, and got the opportunities to meet with Google engineers and project managers.

 

During the information session, engineers from Google mainly introduced researching differences between academia and Google. Two key differences are about available resources and the motivation of the research. Firstly, Google has the most powerful computer center, which offers almost infinite computation resources. They can train very complicated models, adopt more training samples, and obtain results almost instantly with the help from Google. This is crucial for current computer vision research. Secondly, as of motivation, researchers have no pressure on the quantity of publication. Instead, the quality of the publication plays more important role when they publish papers. Additionally, Google has no limit on sharing the work to research community, but they prefer to share the practical work instead of theories only. Several research scientists pointed out that they fundamentally traded the community work to coding when they move from academia to Google.

 

Besides, they have learned more about the ongoing research work in Google. Hartwig Adam, who is the technical lead manager of the Visual Search team in LA office, shared his work there. His team is focusing on developing computer vision algorithms and a scalable computer vision application such as image and video searching, data mining. He also works for Google Goggles and Glass. They talked about Ads implanting for web pages, source code management and product releasing and version control. Also, it was a surprise for them to see Navneet Dalal, who is well known for the publication of Histogram of Oriented Gradients (HOG) feature.