Symmetric Tensor Regression for Functional Connectivity Analysis
Da Xu (2018). "Symmetric Tensor Regression for Functional Connectivity Analysis." Thesis.
CV
Education
- Master of Arts in Biostatistics and Statistics, UC Berkeley, 2016-2018
- Ph.D Student in Statistics, UC Berkeley, 2016-2018
- Chu Ko Chen Honors College, Bachelor of Science in Statistics, Zhejiang University, 2012-2016
Work experience
Dec. 2022 – Present: Staff AI Engineer, LinkedIn
- May. 2021 – Dec.2022: Manager of Machine Learning (& Staff ML Engineer), Walmart Labs
- Search & Personalization Team
- Leading ML and DS teams on pattern recognization, decision making, big-data infrastructure, and theoretical innovations with deep learning systems.
- Jan. 2020 – May.2021: Senior ML Engineer, Walmart Labs
- Search & Personalization Team
- Designing and productionizing end-to-end ML workflows for personalized product recommendation at Walmart.com.
- Jan. 2020 – May.2021: ML Engineer, Walmart Labs
- Personalization Team
- Developing ML models and data analysis pipelines for user understanding and product recommendation.
- May. 2018 - Aug. 2018: Machine Learning Scientist, Tencent
- Social Network Group
- Learning and Inference with Deep Causal Network Models
Publications
Tucker Tensor Regression and Neuroimaging Analysis
Xiaoshan Li, Da Xu, et.al (2018). "Tucker Tensor Regression and Neuroimaging Analysis; Statistics in Biosciences.
Generative Graph Convolutional Network for Growing Graphs
Da Xu, Chuanwei Ruan, et.al (2019). "Generative Graph Convolutional Network for Growing Graphs." IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Self-attention with Functional Time Representation Learning
Da Xu, Chuanwei Ruan, et.al (2019). "Self-attention with Functional Time Representation Learning." Neural Information Processing Systems Conference (NeurIPS).
Knowledge-aware Complementary Product Representation Learning
Da Xu, Chuanwei Ruan, et.al (2020). "Knowledge-aware Complementary Product Representation Learning." ACM Internation Conference on Web Search and Data Mining (WSDM).
Product Knowledge Graph Embedding for E-commerce
Da Xu, Chuanwei Ruan, et.al (2020). "Product Knowledge Graph Embedding for E-commerce." ACM Internation Conference on Web Search and Data Mining (WSDM).
Inductive representation learning on temporal graphs
Da Xu, Chuanwei Ruan, et.al (2020). "Inductive representation learning on temporal graphs." International Conference on Learning Representations (ICLR).
Adversarial Counterfactual Learning and Evaluation for Recommender Systems
Da Xu, Chuanwei Ruan, et.al (2020). "Adversarial Counterfactual Learning and Evaluation for Recommender Systems." Neural Information Processing Systems Conference (NeruIPS).
Theoretical Understandings of Product Embedding for E-commerce Machine Learning
Da Xu, et.al (2021). "Theoretical Understandings of Product Embedding for E-commerce Machine Learning." ACM Internation Conference on Web Search and Data Mining (WSDM).
Bidding via Clustering Ads Intentions: an Efficient Search Engine Marketing System for E-commerce
Cheng Jie, Da Xu, et.al (2021). "Bidding via Clustering Ads Intentions: an Efficient Search Engine Marketing System for E-commerce." 2nd Workshop of Industrial Recommender System, KDD.
Understanding the Role of Importance Weighting for Deep Learning
Da Xu, Yuting Ye, Chuanwei Ruan (2021). "Understanding the Role of Importance Weighting for Deep Learning." International Conference on Learning Representations (ICLR).
Rethinking Neural v.s. Matrix Factorization Collaborative Filtering: the Theoretical Prespectives,
Da Xu, Chaunwei Ruan, et.al (2021). "Rethinking Neural v.s. Matrix Factorization Collaborative Filtering: the Theoretical Prespectives,." International Conference of Machine Learning (ICML).
A Temporal Kernel Approach for Deep Learning with Continuous-time Information
Da Xu et.al (2021). "A Temporal Kernel Approach for Deep Learning with Continuous-time Information,." International Conference on Learning Representations (ICLR).
Towards the D-optimal Experiment Design for Online Recommender Selection
Da Xu, Chaunwei Ruan, et.al (2021). "Towards the D-optimal Experiment Design for Online Recommender Selection." ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD).
From Intervention to Domain Transportation: a Novel Perspective to Optimize Recommendation
Da Xu, Yuting Ye, et.al (2022). "From Intervention to Domain Transportation: a Novel Perspective to Optimize Recommendation." International Conference on Learning Representations (ICLR).
Towards Robust Off-policy Learning for Online Uncertainty
Da Xu, Yuting Ye, et.al (2022). "Towards Robust Off-policy Learning for Online Uncertain." AAAI conference on Artificial Intelligence.
Causal Structure Learning with Recommendation System
Xu S, Xu D, Korpeoglu E, et al. Causal Structure Learning with Recommendation System[J]. arXiv preprint arXiv:2210.10256, 2022
On the Advances and Challenges for Adaptive Online Testing
Da Xu, et.al (2022). "On the Advances and Challenges for Adaptive Online Testing." Workshop of Decision Making for Modern Information Retrieval (WSDM).
Talks
Workshop on Semantic Search and Recommendation of E-commerce
Keynote at INFORMS Annual Meeting,
Inductive Representation Learning on Temporal Graphs
Talk at A.I. Socratic Circles Spotlight Talk,
Workshop on Industrial Recommender System
Workshop at Knowledge Discovery and Data Mining (KDD),
Workshop on Representation Learning for Finance and E-commerce Applications
Keynote at International Conference of Machine Learning (ICML),
Modern Theoretical Tools for Understanding and Designing Next-generation Information Retrieval Systems
Tutorial at ACM Conference on Web Search and Data Mining (WSDM), Tempe, AZ, USA
Teaching
Service and leadership
- Workshop Chair for WWW 2023
- Title: Decision Making for Modern Information Retrieval and Recommender Systems
- Workshop Chair for WSDM 2022
- Title: Decision Making for Modern Information Retrieval Systems
- Session Chair for Informs Annual Meeting, 2021
- Title: Causal Inference for Information Retrieval
- Program Committee & Reviewer
- NeurIPS 2020,2021 / ICLR 2020,2021,2022 / KDD 2020,2021 / SIGIR 2020,2022 / AISTATS 2020 / ICML2022 / WWW 2022
Skills
- Pytorch
- Python
- Tensorflow
- Spark
- R
- C++
- Hive
- SQL