Prof. Lei Wang, Changsha University, China
Dr. Lei Wang is a distinguished full professor and academic leader at the School of Computer Science and Engineering, Changsha University, P.R. China. He received his Ph.D. in Computer Science from Hunan University, China, in 2005. Following his doctoral studies, he conducted postdoctoral research at Tsinghua University and worked as a visiting scholar at Duke University and Lakehead University. Dr. Wang has had an extensive academic career, serving as an associate professor at Hunan University’s College of Software (2005–2011) and later as a full professor at Xiangtan University’s College of Information Engineering (2011–2018). With over 100 peer-reviewed publications, his research focuses on bioinformatics and the Internet of Things (IoT), contributing significantly to these fields. Dr. Wang’s expertise and leadership continue to shape advancements in computer science and engineering.
Professional Profile:
ORCID
SCOPUS
Summary of Suitability for Best Researcher Award: Prof. Lei Wang
Prof. Lei Wang is an exemplary candidate for the Best Researcher Award due to his extensive experience and profound contributions to the fields of bioinformatics and Internet of Things (IoT). His career, spanning over 20 years, has seen impactful research in computer science and his significant academic leadership positions.
🎓 Education
💼 Work Experience
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2005–2007: Postdoctoral Fellow
Tsinghua University, P.R. China 🇨🇳
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Visiting Scholar:
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2005–2011: Associate Professor
College of Software, Hunan University 🇨🇳
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2011–2018: Full Professor
College of Information Engineering, Xiangtan University 🇨🇳
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2018–Present: Full Professor & Academic Leader
School of Computer Science and Engineering, Changsha University 🇨🇳
🏆 Achievements
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📚 Published over 100+ peer-reviewed research articles
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🧬 Significant contributions to bioinformatics and Internet of Things (IoT)
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🌐 Experience across three countries: China, USA, and Canada
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🎓 Academic leader with a focus on interdisciplinary research and innovation
🥇 Awards & Honors
While specific awards are not listed in the provided text, Dr. Wang’s international academic engagements and leadership positions suggest a highly respected status in the research community 🌟. If you have details on specific awards or recognitions, I’d be happy to include them.
Publication Top Notes:
NNSFMDA: Lightweight Transformer Model with Bounded Nuclear Norm Minimization for Microbe-Drug Association Prediction: Lightweight Microbe-Drug Association Prediction Model
Dual graph-embedded fusion network for predicting potential microbe-disease associations with sequence learning
An Effective Method to Identify Cooperation Driver Gene Sets
Association prediction of lncRNAs and diseases using multiview graph convolution neural network
A novel end-to-end learning framework for inferring lncRNA-disease associations based on convolution neural network
CLMT: graph contrastive learning model for microbe-drug associations prediction with transformer
GLNNMDA: a multimodal prediction model for microbe-drug associations based on global and local features
A novel microbe-drug association prediction model based on graph attention networks and bilayer random forest
NMGMDA: a computational model for predicting potential microbe–drug associations based on minimize matrix nuclear norm and graph attention network