CV

Education

• B.S. in Information and Communication Engineering, GitHub University, 2013
• Ph.D in Computer Science, Shanghai Jiao Tong University, 2017

Work experience

• Spring 2022 - Current: Machine Learning Engineering
  • Intel Corporation
  • Duties included: Researh on E2E AI democratization
• Fall 2019: Intern
  • Intel Corporation
  • Duties included: Researh on E2E AI democratization
• Fall 2018: Research Assistant
  • University of Macau
  • Duties included: Development and Maintenance of Knowledge Graph
  • Supervisor: Professor Weijia Jia

Skills

• Programming Languages: C/C++, Python, Java, Bash
• Familiar Projects: Tensorflow, Pytorch, Scrapy, Numpy, HuggingFace/Tranformers, Linux DeviceMapper
• Development Tools: GCC/Makefile/Cmake, Git, Vim

Publications

Neural Relation Extraction via Inner-Sentence Noise Reduction and Transfer Learning

Improving Abstractive Document Summarization with Salient Information Modeling

Robust Neural Relation Extraction via Multi-Granularity Noises Reduction

Regularized Attentive Capsule Network for Overlapped Relation Extraction

Fine-grained relation extraction with focal multi-task learning

Fine-grained Question-Answer Sentiment Classification with Hierarchical Graph Attention Network

Data Diversification Revisited: Why Does It Work?

Distantly Supervised Relation Extraction using Multi-Layer Revision Network and Confidence-based Multi-Instance Learning

Relation construction for aspect-level sentiment classification

Tag-assisted Multimodal Sentiment Analysis under Uncertain Missing Modalities

Robust Multimodal Sentiment Analysis Via Tag Encoding of Uncertain Missing Modalities