Jianfeng Wang

+44 7450974135 | [email protected]
London, UK

Introduction

I am Jianfeng Wang, a highly skilled software engineer and research assistant with extensive experience in developing intelligent systems, cloud technologies, and high-performance computing environments. My expertise lies in machine learning, statistical learning techniques, and software development, with a strong focus on API development, microservices architecture, and performance optimization. I have successfully led and participated in various international research projects, contributing to the advancement of data management platforms and intelligent systems. My current research focuses on AI applications in healthcare, aiming to enhance clinical decision-making and patient interactions through advanced machine learning models.

Skills

• Languages & Frameworks: Python (Pytorch, Numpy, Pandas), TensorFlow, JavaScript (TypeScript, React, Angular, Node.js), Django, Flask, Linux, Docker, Redis, OpenStack, K8S, AWS, GCP
• Database Management: API development, microservices architecture, cloud technologies, CI/CD, IaC (Terraform, CloudFormation), performance optimization, disaster recovery
• Software Development: API development, microservices architecture, cloud technologies
• Statistical Learning Techniques: Data Preparation and Visualization, Machine Learning Methods (Graph Convolution Network, Transformer, etc.), Model Analysis (Accuracy, Precision, etc.), Model Fine-Tuning (Cross Validation, Clip, Regularization, etc.)

Publications

Papers

• Jianfeng Wang, etc. MHKD-MVQA: Multimodal Hierarchical Knowledge Distillation for Medical Visual Question Answering. 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2022).
• Jianfeng Wang, etc. Knowledge-Enhanced Visual Question Answering with Multi-modal Joint Guidance. The 11th International Joint Conference on Knowledge Graphs. (IJCKG 2022).

Patents

• Wenqiang Zhang, Jianfeng Wang, etc. A method for knowledge-enhanced visual question answering based on multi-modal information guidance. CN Patent 202210174858.4, PCT/CN2022/200086.