About me: I am a senior applied scientist at Amazon sponsored product ads, focusing on ads recommendation and LLM. Before joining Amazon, I worked at XPENG and Yahoo Research, working on behavior prediction in autonomous driving, contents (text & image) moderation, user profiling, and time series analysis. Before that I completed my PhD in
Electrical and Computer Engineering
at Duke University, advised by Professor
Lawrence Carin. I was also affiliated
with iiD (Information Initiative at Duke). Before
that, I obtained Master's degree in Physics, and Bachelor's degree in Software Engineering, both from Xiamen University in China.
Research: My research lies at the intersection of Bayesian statistics and machine learning, with an emphasis on large language models, autonomous driving and time series analysis.
Professional Activities:
Reviewer for IEEE PAMI
PC member for AAAI 2019, reviewer for AAAI 2019, 2017, 2016
PC member for ICMLA 2019, 2018
Reviewer for NeurIPS 2019, 2016
Reviewer for Techpulse 2018, 2017
PC member and reviewer for SMARTCOMP 2017
PC member for SmartMM 2017
Reviewer for IJCAI 2016
Reviewer for UAI 2016
Reviewer for IEEE Transactions on Knowledge and Data Engineering (TKDE)
Reviewer for Knowledge and Information Systems (KAIS)
Reviewer for IEEE Access
News:
09/2019: Three papers were accepted by EMNLP 2020.
09/2019: Our paper "A Deep Structural Model for Analyzing Correlated Multivariate Time Series" was accepted by ICMLA 2019.
09/2019: Our paper "Large-scale Gender/Age Prediction of Tumblr Users" was accepted by ICMLA 2019.
06/2017: The code for binary tensor factorization is released. See link
04/2017: Our paper "Deep Generative Models for Relational Data with Side Information" was accepted by ICML 2017.
01/2017: I won the 3rd prize in data visualization challenge held at Duke University.
12/2015: I had two papers accepted by AISTATS 2016.
09/2015: Our paper "Large-Scale Bayesian Multi-Label Learning via Topic-Based Label Embeddings" was accepted by NIPS 2015 for spotlight presentation.
08/2015: Our paper "Scalable Bayesian Non-Negative Tensor Factorization for Massive Count Data" submitted to ECML PKDD 2015 received the Best Student Paper Award.
08/2015: We received a research grant from Accenture for our research on machine learning for analyzing healthcare data, and I was honored as "Accenture Fellow".
05/2015: Our paper "Zero-Truncated Poisson Model for Scalable Bayesian Factorization of Massive Binary Tensors with Mode-Networks" was accepted by UAI 2015 for plenary oral presentation.
04/2015: Our paper "Scalable Probabilistic Tensor Factorization for Binary and Count Data" was accepted by IJCAI 2015.
