林娟

副教授

计算机与信息学院




行政职务：


技术职称：
副教授

最后学位：
硕士学位

电话：
+86**

电子邮箱：
juan.lin AT fafu.edu.cn

办公地点：


通讯地址：
福建农林大学计算机与信息学院

邮编：
350002






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My current research interests include interactive mathematical visualization, big data visual-analysis presentation. In particular, to investigate how to visualize new geometry and analyze deformations of extreme complexity, by combining research that spans mathematical visualization, user interfaces, and high performance computing (HPC). Other research directions include bioinformatics and computational intelligence, with focus on the application of parallel intelligent optimization algorithms to protein and RNA structure prediction.




教育经历
M.S. in Biology Information Science and Technology, Fujian Agriculture and Forestry University, 2008-2011
Dissertation: The Research of Swarm Intelligence Algorithms for Prediction of RNA Secondary Structure
Advisor: Prof. Yiwen Zhong
B.S. in Computer Science and Technology, Fujian Normal University, 1999-2003





工作经历
Visiting Scholar
Visualization and Intensive Graphics Lab, J.B. School of Engineering, UofL, U.S., July 2016-July 2017
Associate Professor
College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou, China, October 2015-present
Visiting Scholar
Multimedia and Intelligent Software Technology Laboratory, Beijing University of Technology, Beijing, China, September 2012- June 2013
Lecturer
College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou, China, July 2009- September 2015














研究领域
1.Accelerate Mathematical Simulations with R on the web
Project description: Many geometric problems of interest to mathematical visualization applications involve changing structures requiring mathematical simulations, such as the moves that transform one knot into an equivalent knot. In this project, we explore a unique paradigm that makes use of self-deformable object models embedded in mathematical space, supplemented by energy-driven relaxation and user defined motion constraints to guide the simulation through the configuration space. Furthermore, we exploit web-based user interfaces and parallelization to accelerate mathematical simulations, and to extract key moments where successive terms in the sequence differ by one critical change to represent and analyze various mathematical evolutions.
2.The Swarm Simulated Annealing Algorithm for Protein Structure Prediction
Project description: Protein structure prediction (PSP) problem is considered as one of the most important and a highly challenging problem both for the biology and for the computational communities. Traditional Simulated Annealing (SA) algorithm is extremely slow in convergence, and the implementation and efficiency of parallel SA algorithms are typically problem-dependent. To overcome such intrinsic limitations and provide novel solutions for PSP problem, in this project, we explore a multiple population SA algorithm paradigm with variable sampling strategies for fine search. Furthermore, we employ parallelization to accelerate computation, present a series of high efficiency algorithms within a limited time and resource.
3.The Quantum Shuffled Frog Leaping Algorithm and Its Application on Protein Structure Prediction
Project description: Shuffled Frog Leaping (SFL) algorithm as a meta-heuristic optimization method that mimic the memetic evolution, has been used for solving complex problems in several areas. However, some problems are computationally intensive when it concerns the evaluation of solutions during the search process. Inspired by quantum-physics ideas, a quantum-based SFL algorithm is proposed, which is characterized by the probabilistic quantum bit representation. Through defining a new philosophy for exploring the search space, better convergence capability of the global optimization is expected.
4. RNA Secondary Structure Prediction with Pseudoknot
Project description: The diverse functional roles of RNA are determined by its underlying structure. Accurate and comprehensive knowledge of RNA secondary structure would inform a broader understanding of RNA biology and help exploiting RNA as a biotechnological tool and therapeutic target. Based on the concept of free-energy minimization, in this project, we design a set of discrete swarm intelligent optimization algorithms to facilitate both development of new discrete optimization algorithms and new prediction methods.




开授课程
A wide range of courses at Fujian Agriculture and Forestry University
·Python Language and Machine Learning
oclass size: 90 students
·Data Structure
oclass size: 30 students
·C programming
oclass size: 90 students
·VB programming
oclass size: 120 students
·Introduction to Information Technology
oclass size: 120 students




科研项目
1. "Many-Objective Optimization Problem Visualization for Classical and Protein Structure Prediction Problem".Principal Investigator, Natural Science Foundation of Fujian Province, Fujian, China (Project No. 2020J01570). Awarded amount: $10,000. (01/11/2020 – 01/11/2023).
2.“Study of Simulated Annealing Algorithm for Protein Structure Prediction”, Principal Investigator, Natural Science Foundation of Fujian Province, Fujian, China (Project No. 2014J01219). Awarded amount: $5000. (01/01/2014 – 12/31/2016).
3.“Quantum Shuffled Frog Leaping Algorithm and Its Application on Protein Structure Prediction Problem”, Principal Investigator, Foundation of Fujian Educational Committee, Fujian, China (Project No. JA14110). Awarded amount: $1500. (01/01/2014 – 12/31/2016).
4.“RNA Secondary Structure Prediction with Pseudoknot”, Principal Investigator, Foundation of Fujian Agriculture and Forestry University for Young Teachers, Fujian, China (Project No. **). Awarded amount: $1000. (01/01/2010-06/30/2012).




论文著作
SELECTED PUBLICATIONS
1.Juan Lin, Yiwen Zhang, Multi-agent list-based noising algorithm for protein structure prediction”, International Journal of Intelligent Information and Database Systems,Accepted.
2.Juan Lin, et al. Multi-Agent Simulated Annealing Algorithm with Parallel Adaptive Multiple Sampling for Protein Structure Prediction in AB off-lattice model. Applied Soft Computing, 2018(62):492-503.
3.Juan Lin, Di Zhong, Yiwen Zhong, Hui Zhang. Accelerating Mathematical Knot Simulation with R on the Web. IEEE Big Data 2016 Workshop.
4.JuanLin,YiwenZhong.Multi-agentList-basedThreshold-accepting Algorithm for Numerical Optimisation. JournalofComputingScienceandMathematics,6(5):501-509, 2015.
5.Juan Lin, Yiwen Zhong, Lijin Wang. The Improvement of Shuffled Leaping Frog Algorithm for Protein Structure Prediction. Journal of Bionanoscience, 8(5), 380-390, 2014.
6.Juan Lin, Qingliang Du, Hui Yang, Yiwen Zhong. Parallel Simulated Annealing Algorithm Based on Particle Swarm Optimization Algorithm.Journal of Frontiers of Computer Science & Technology, 8(7), 886-896, 2014 (in Chinese).
7.Juan Lin, Jing Ning, Qingliang Du, Yiwen Zhong. Multi-agent Simulated Annealing Algorithm Based on Particle Swarm Optimization Algorithm for Protein Structure Prediction. Journal of Bionanoscience, 7(1), 84-91, 2013.
8.Juan Lin, Yiwen Zhong, Senlin Ma. Improved Opposition-based shuffled Frog Leaping Algorithm for Function Optimization Problems. Application Research of Computers, 30(3), 760-763, 2013 (in Chinese).
9.Juan Lin, Yiwen Zhong. Improved Immune Particle Swarm Optimization Algorithm for RNA Secondary Structure Prediction. Computer Engineering and Application, 48(1), 40-43, 2012 (in Chinese).







科技成果








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