PhD. Candidate in Computer Science and Technology, 2021-now
MEng. in Computer Science and Technology, 2018-2021
BEng. in Electrical Engineering and its Automation, 2018
From an engineering perspective, a design should not only perform well in an ideal condition, but should also resist noises. Such a design methodology, namely robust design, has been widely implemented in the industry for product quality control. To achieve data-efficient robust design, we propose Robust Inverse Design under Noise (RID-Noise), which can utilize existing data to train a conditional invertible neural network. With the visual results from experiments, we clearly justify how RID-Noise works by learning the distribution and robustness from data. Further experiments on several real-world benchmark tasks with noises confirm that our method is more effective than other state-of-the-art inverse design methods.
Conversion rate (CVR) prediction is one of the most critical tasks for digital display advertising. However, conversions usually do not happen immediately after a user click. We propose Elapsed-Time Sampling Delayed Feedback Model (ES-DFM), which models the relationship between the observed conversion distribution and the true conversion distribution.
Program Committee Member (PC member): AAAI'21, AAAI'22
Advanced C++ Programming. Fall, 2018.
Digital Signal Processing. Fall, 2021.
2022 AAAI student scholarship
2016 ACM/ICPC Asian Regional, HongKong Onsite, Gold Award, Problem A First Blood
2016 ACM/ICPC Asian Regional, East Continent Final, Silver Award
2016 China Collegiate Programming Contest(CCPC), South China Invitational Contest, Gold Award
2016 China Collegiate Programming Contest(CCPC), Changchun Onsite, Silver Award
2015 ACM/ICPC Asian Regional, Shanghai Onsite, Bronze Award
2018 Alibaba Global Scheduling Algorithm Competition, Champion(1/2116)
2018 Huawei Code Craft, Most Exquisite Code Award
2018 HackHarvard, KENSHO prize
2019 KDD cup 2019 (Humanity RL Track), 12/247