Huazhong Ning's Email Addresses & Phone Numbers

Executive Director of Perception @ WeRide.ai At WeRide, we are revolutionizing mobility with autonomous driving technologies powered by AI and Robotics. We are creating an innovative mobility ecosystem to transform every single trip to be safe, efficient, cost-effective and entertaining. Perception of WeRide enables the cars to see the world and to know what they see. San...

Executive Director of Perception @ WeRide.ai At WeRide, we are revolutionizing mobility with autonomous driving technologies powered by AI and Robotics. We are creating an innovative mobility ecosystem to transform every single trip to be safe, efficient, cost-effective and entertaining. Perception of WeRide enables the cars to see the world and to know what they see. San Francisco Bay AreaResearch Scientist @ Facebook Working in Applied Machine Learning Team- Large scale deep learning platform for major Facebook ranking systems- Multi-task learning- Large scale topic modeling (unsupervised, sparse, hierarchical generative model)- Multi-modality learning- Deep learning for user modeling From July 2016 to March 2019 (2 years 9 months) San Francisco Bay AreaStaff Software Engineer @ Google Worked in Google Play- Continue to lead App&user understanding for Play Store (TL) until 01/2016.- Search filters: recommend narrow down topics/tags for topic search queries. The recommended tags are displayed under the search bar. Available for Play App Search. From March 2015 to July 2016 (1 year 5 months) San Francisco Bay AreaSenior Software Engineer @ Google Worked in Google Play- App&user understanding for Play Store (TL). Initiated the project from scratch and drove 10+ launches to improve Play search and recommendations.- Play Store App recommendations (TL). Led the project and doubled the app acquisition rate.- Play Store machine learning (TL). Led a virtual team to build the machine learning common infra for Play which enabled 15+ launches on Play search and recommendations From March 2013 to March 2015 (2 years 1 month) San Francisco Bay AreaSoftware Engineer @ Google Worked in Google Research- Youtube Mix (TL of quality). Designed and implemented the core Mix algorithms.- Youtube channel recommendation. One of the 3 core engineers. Doubled the channel subscription rate.- Large Scale Machine Learning System. One of the 3 core engineers. Designed and implemented a large scale machine learning system from scratch. From September 2010 to March 2013 (2 years 7 months) San Francisco Bay AreaSenior Research Scientist @ AKIIRA Media Systems Inc. Designed and implemented a video search engine that is focusing on professional long videos. Mainly involved in these projects.1. Fast nearest neighbor search. Learn random forest to fast generate hash code and leverage on Lucene to index and search. Used for searching similar video shots and face recognition.2. Face recognition. Train and test on LFW database (about 13K images with 6K people) and achieved the state-of-the-art performance.3. Leveraging on Hadoop, designed and implemented the video cloud computing platform (C++): representation/serialization/deserialization of objects, multithreading pipeline of video processing, unified map/reduce for the pipeline, Hadoop simulator for C pipes...4. Large scale distributed eig-decomposition. Compute top 1000 eigens of a 1M by 0.5M full matrix in about 2 days using 11 nodes.5. Video annotation. We segment videos into shots and classify each shot using SVM or other methods. Our major contribution is the visual feature. From December 2009 to July 2010 (8 months) Applied Researcher @ Microsoft Corporation Conduct research and development on multimedia advertisement, ad relevance, monitoring and diagnostics of ad systems:1. Content ads for videos. It is a new delivery channel to sell AdCenter ads through video content. Solve the optimization problem: find the most relevant ad and overlay it on the video at the least intrusive spatial-temporal location. This algorithm was delivered to MSN.video for pilot.2. Automatic monitoring and diagnostics of the ad system. Ad system is extremely complex and any change of an internal or external factor may affect the revenue. We designed and implemented an automatic tool to locate the possible changes/errors and recommend solutions, using Bayesian net. This tool is a widely used internal tool in Microsoft AdCenters. From December 2008 to December 2009 (1 year 1 month) Intern @ NEC Laboratories America, Inc. Research intern for 4 summers during this period, I proposed several novel algorithms on multimedia computing and machine learning and also built several software systems:1. Action detection in Shopping Mall Project. Learn discriminative visual words for 3D human pose estimation through an integrated optimization framework. 2. Tracking evolutionary virtual communities in web-blogs. Proposed an incremental spectral clustering algorithm.3. A live demo system for driving safety. Developed the core module – temporal difference learning to predict danger level. The system was highly praised by the Vice-President of Toyota.4. Developed an online speaker diarization system (“who spoke when”). My main contributions include the working system and the cross-EM refinement algorithm. From 2005 to 2008 (3 years) Technical Project Manager and 3G Software Engineer @ Alcatel 1. Technical project manager of the project 3G-UAN (Uniform Access Number). The project was completed in time with high quality.2. Developed the GPRS part of 3G projects: 3G-VPN (Virtual Private Network) and 3G-PPS (Pre-Paid Service). From September 2003 to July 2004 (11 months)