Linlin You | Computing | Best Researcher Award

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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.
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Data Analysis Techniques

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Introduction to Data Analysis Techniques:

Data analysis techniques are fundamental tools across various scientific disciplines, enabling researchers to extract meaningful insights and knowledge from large and complex datasets. Whether in the realms of physics, biology, finance, or social sciences, effective data analysis is crucial for making informed decisions, identifying trends, and drawing conclusions.

Statistical Analysis:

Statistical techniques involve the application of probability theory and mathematical statistics to analyze data, including hypothesis testing, regression analysis, and Bayesian inference, to uncover patterns and relationships.

Machine Learning and Predictive Modeling:

Explore the use of machine learning algorithms to build predictive models, classify data, and make data-driven predictions, with applications in fields such as image recognition, natural language processing, and recommendation systems.

Data Visualization:

Delve into data visualization techniques that enable researchers to represent data graphically, creating informative charts, graphs, and interactive visualizations to communicate findings effectively.

Big Data Analytics:

Focus on the challenges and methods for handling and analyzing large-scale datasets, including distributed computing, data preprocessing, and scalable machine learning algorithms.

Time Series Analysis:

Examine techniques for analyzing time-ordered data, such as financial data, environmental monitoring, and physiological signals, to identify trends, periodicities, and anomalies.

 

 

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