Linlin You | Computing | Best Researcher Award

Posted on by ScienceFather

Assoc Prof Dr. Linlin You | Computing | Best Researcher Award

PHD at the University of Pavia,  Italy

Linlin You is an Associate Professor at Sun Yat-sen University, specializing in Smart Cities, Autonomous Systems, Distributed Computing, and Federated Learning. She obtained her Ph.D. from the University of Pavia and has conducted research at MIT and Singapore-MIT Alliance for Research and Technology. Linlin has authored over 70 publications and holds 21 patents. She serves as an editorial board member for prestigious journals like The Innovation and is a Senior Member of IEEE. Her contributions in advancing distributed computing and federated learning have earned her recognition, including the Smart City Technology Innovation Award, highlighting her impact in sustainable urban development and intelligent systems. computing

Professional Profiles

Academic and Professional Background

Linlin You is an Associate Professor at Sun Yat-sen University and a Research Affiliate at MIT. Formerly a senior postdoc at Singapore-MIT Alliance for Research and Technology, she earned her Ph.D. in Computer Science from the University of Pavia in 2015. Her research spans Smart Cities, Autonomous Systems, Distributed Computing, Federated Learning, and AI-driven solutions for transportation and energy. She has authored over 70 publications in top-tier journals/conferences and holds 21 patents. Linlin serves on editorial boards, including The Innovation (IF 33.2), and is an IEEE Senior Member. computing

Research and Innovations

CEO of Naftan Payesh Integrity and Corrosion Management Co., driving innovation in corrosion management and root cause failure analysis Senior Corrosion Consultant at LifeTech Engineering, advising on global energy projects Technical Manager and Chairman at Miad Tech. Co., specializing in materials characterization and corrosion solutions computing

Areas of Research

Her research focuses on systems and services, computing, and learning.

Research Focuses

Linlin You’s research focuses on Smart Cities, Autonomous Systems, Distributed Computing, and Federated Learning. Her work explores the impact of urban morphology on solar capacity in three-dimensional cities, accurate modeling of photovoltaic modules using deep learning networks, and personal mobility service systems in urban areas. She has developed innovative federated learning mechanisms and contributed to understanding solar accessibility and sustainable urban development. Linlin’s research also addresses mobility sensing systems, public sentiment analysis for smart city design, and cloud computing service quality management. Her contributions span over 70 publications, numerous patents, and editorial roles in leading journals, demonstrating leadership in advancing technologies for intelligent urban environments.

Publications

  1. From cell tower location to user location: Understanding the spatial uncertainty of mobile phone network data in human mobility research, Publication date: 2024.
  2. Precise Landmark-Map for Bundle Adjustment LiDAR Odometry, Publication date: 2024.
  3. Resource-Aware Split Federated Learning for Edge Intelligence,, Publication date: 2024.
  4. SiG: A Siamese-Based Graph Convolutional Network to Align Knowledge in Autonomous Transportation Systems.Publication date: 2024.
  5. Unravelling the effects of dynamic urban thermal environment on utility-scale floating photovoltaic electricity generation, Publication date: 2023.
  6. FedRSM: Representational-Similarity-Based Secured Model Uploading for Federated Learning, Publication date: 2023.
  7. Efficiency-Improved Federated Learning Approaches for Time of Arrival Estimation, Publication date: 2023.
  8. Enterprise oriented software engineering education: a preliminary framework, Publication date: 2023.
  9. Multi-source data management mechanism and platform,  Publication date: 2022.
  10. Integrated Mobility for Individuals in Smarter Cities: a Crowd-sourcing approach, Publication date: 2022.
.

Qingfeng Chen | Bioinformatics | Best Researcher Award

Posted on by ScienceFather

Prof Dr. Qingfeng Chen | Bioinformatics | Best Researcher Award 

PHD at the University of Technology Sydney, Australia

Professor Qingfeng Chen is a distinguished scholar in bioinformatics, data mining, and artificial intelligence at Guangxi University. Holding a Ph.D. from the University of Technology Sydney, he also serves as an honorary research fellow at La Trobe University, Australia. Dr. Chen’s leadership includes roles as Chairman of the Guangxi Bioinformatics Association and Executive Deputy Director of the Biomedical Informatics Committee of Guangxi Artificial Intelligence Society. He has chaired numerous international conferences and serves on editorial boards for prominent journals. His research spans computational biology, with a focus on developing advanced algorithms for biomedical data analysis and application of artificial intelligence in healthcare.

Professional Profiles

Education

Doctor of Philosophy in Computer Science and Technology University of Technology Sydney, 2004 Master of Mathematics Guangxi Normal University, 1998 Bachelor of Mathematics Guangxi Normal University, 1995

Current Positiions

Professor of Bioinformatics, Data Mining and Artificial Intelligence Research Department Guangxi University, July 2009 – Present Honorary Research Fellow La Trobe University, Australia, May 2016 – Present Chairman Guangxi Bioinformatics Association, August 2022 – Present Executive Deputy Director Biomedical Informatics Committee, Guangxi Artificial Intelligence Society, August 2020 – Present Honorary Visiting Professor University of Technology Sydney, April 2013 – December 2014 Research Fellow City University of Hong Kong, February 2013 – April 2013

Research Focus

Professor Qingfeng Chen’s research focuses on the application of deep learning and advanced computational methods in bioinformatics and biomedical data analysis. His work includes developing models for predicting responses to treatments in hepatocellular carcinoma using multiphase CT images, integrating multi-omics data with transformer and graph convolutional networks, and predicting circRNA-disease associations. Additionally, he explores frameworks like DeepKEGG for cancer recurrence prediction and biomarker discovery, as well as neural network interpretability in lung adenocarcinoma. His contributions also extend to drug-target interaction prediction and entity alignment, showcasing a diverse range of expertise in computational biology and artificial intelligence.

Publications

  1. LGCDA: Predicting CircRNA-Disease Association Based on Fusion of Local and Global Features, Publication date: 2024.
  2. Similarity-guided graph contrastive learning for lncRNA-disease association prediction, Publication date: 2024.
  3. SSLDTI: A novel method for drug-target interaction prediction based on self-supervised learning, Publication date: 2024.
  4. NGCN: Drug-target interaction prediction by integrating information and feature learning from heterogeneous network, Publication date: 2024.
  5. IBPGNET: lung adenocarcinoma recurrence prediction based on neural network interpretability, Publication date: 2024.
  6. DeepKEGG: a multi-omics data integration framework with biological insights for cancer recurrence prediction and biomarker discovery, Publication date: 2024.
  7. Bi-SGTAR: A simple yet efficient model for circRNA-disease association prediction based on known association pair only, Publication date: 2024.
  8. A semi-supervised approach for the integration of multi-omics data based on transformer multi-head self-attention mechanism and graph convolutional networks, Publication date: 2024.
  9. Deep learning of pretreatment multiphase CT images for predicting response to lenvatinib and immune checkpoint inhibitors in unresectable hepatocellular carcinoma, Publication date: 2024.
.