About Han Sun
Han Sun is currently a machine learning engineer at Pinterest. Before he was a research engineer at Yahoo Research. His industry experience focuses on end-to-end large-scale Machine Learning and Computer Vision system development.
He graduated with a Ph.D. degree in Earthquake and Structural Engineering and an M.S. degree in Statistics from UCLA. His Ph.D. thesis is on seismic response prediction of a portfolio of buildings subjected to earthquake hazards.
Industry
2021 - now: Machine Learning Engineer, Pinterest
2019 - 2021: Senior Research Engineer, Yahoo Research
Education
2014 - 2019: Ph.D. in Earthquake Engineering, University of California, Los Angeles
2016 - 2018: M.S. in Statistics, University of California, Los Angeles
2012 - 2014: M.S. in Civil Engineering, University of Michigan, Ann Arbor
2008 - 2012: B.S. in Civil Engineering, Hong Kong Polytechnic University, Hong Kong
Hobbies
He is passionate about computer graphics, vision and gaming AI.
Contact
hansun2014@ucla.edu
Publications
Sun, H., Burton, H., & Huang, H. (2020). Machine Learning Applications for Building Structural Design and pereformance Assessment: State-of-the-Art Review. Journal of Building Engineering, 101816.
Mangalathu, S., Sun, H., Nweke, C. C., Yi, Z., & Burton, H. V. (2019). Classifying Earthquake Damage to Buildings Using Machine Learning. Earthquake Spectra, 36 (1), 183-208.
Sun, H., Burton, H., & Wallace, J. (2019). Reconstructing seismic response demands across multiple tall buildings using kernel‐based machine learning methods. Structural Control and Health Monitoring, 26(7), e2359.
Sun, H., & Yang, J. (2019, February). Domain-Specific Image Classification Using Ensemble Learning Utilizing Open-Domain Knowledge. In 2019 International Conference on Computing, Networking and Communications (ICNC) (pp. 593-596). IEEE.
Sun, H. (2019). A Data-driven Building Seismic Response Prediction Framework: from Simulation and Recordings to Statistical Learning (Doctoral dissertation, UCLA).
Zhang, Y., Burton, H. V., Sun, H., & Shokrabadi, M. (2018). A machine learning framework for assessing post-earthquake structural safety. Structural Safety, 72, 1-16.
Sun, H., Burton, H., Zhang, Y., & Wallace, J. (2018). Interbuilding interpolation of peak seismic response using spatially correlated demand parameters. Earthquake Engineering & Structural Dynamics, 47(5), 1148-1168.
Sun, H. (2018). Prediction Model Development of Seismic Building Responses (M.S. dissertation, UCLA).
Burton, H. V., Sreekumar, S., Sharma, M., & Sun, H. (2017). Estimating aftershock collapse vulnerability using mainshock intensity, structural response and physical damage indicators. Structural safety, 68, 85-96.
Featured Projects
A Data-Driven Building Seismic Response Prediction Framework
Proposed a data-driven framework to reconstruct structure responses through machine learning techniques from limited available sources which may potentially benefit for “real-time” interpolating monitoring data and enable rapid damage assessment and reducing computational effort for regional seismic hazard assessment (Ph.D. thesis at UCLA)
Spatial and Structural Correlation Patterns for Building Seismic Responses
Evaluated seismic building response patterns in the joint space of structure and geo-spatial domain and proposed a prediction model utilizing these patterns (journal paper published on Earthquake and Structural Dynamics)
A Hierarchical Framework Incorporating Domain Searching and Abstract Key Feature Extraction
Developed a framework that utilizes well-performed object detection model for domain searching and applies transfer learning to retrieve domain-specific features at the abstract level using public available dataset
Flask-SQL Interactive Website Development
Developed front and back-end of the UCLA Tall Building Resilience Project Showcase Website