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faculty
Yanlai Chen, PhD
Professor
Mathematics
Contact
508-999-8438
508-910-6917
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Liberal Arts 394A
Education
| 2007 | University of Minnesota Twin Cities | PhD |
| 2007 | University of Minnesota Twin Cities | MS |
| 2002 | University of Science and Technology of China | BS |
Teaching
- Numerical algorithms
- Probability
- Calculus, Differential equation
- Optimization
Teaching
Programs
Programs
- Biomedical Engineering and Biotechnology MS, PhD
- Data Science BS, BS/MS
- Data Science Graduate Certificate
- Data Science MS
- Engineering and Applied Science PhD
- Mathematics BA, BS
Teaching
Courses
Course on numerical methods in science and engineering. Topics will include: numerical analysis and methods (quadrature, optimization, matrices, root-finding, ODEs, PDES, Monte-Carlo), and an introduction to multigrid and parallel computing. Programming exercises using MATLAB and individual research projects are an essential part of the course.
Research investigations of a fundamental and/or applied nature defining a topic area and preliminary results for the dissertation proposal undertaken before the student has qualified for EAS 701. With approval of the student's graduate committee, up to 15 credits of EAS 601 may be applied to the 30 credit requirement for dissertation research.
Research investigations of a fundamental and/or applied nature defining a topic area and preliminary results for the dissertation proposal undertaken before the student has qualified for EAS 701. With approval of the student's graduate committee, up to 15 credits of EAS 601 may be applied to the 30 credit requirement for dissertation research.
Theory and computer-oriented practice in obtaining numerical solutions of various problems. Topics include stability and conditioning, nonlinear equations, systems of linear equations, interpolation and approximation theory.
Research
Research awards
- $ 13,000 awarded by Mathematical Association of America for Mixed Model Implicit and IMEX Runge-Kutta Methods
- $ 130,113 awarded by U.S. Department of the Air Force for Computationally and Energy Efficient Mixed Precision and Mixed-model Numerical Methods
- $ 296,555 awarded by The National Science Foundation for Reduced Basis Enhancements of Neural Networks and Their Application to Quantum Materials Simulation
- $ 477,181 awarded by Office of Naval Research for UMassD MUST III: Multi-Fidelity Information Fusion for Accelerated Predictive Modeling and Optimal Design of High Entropy Alloys
- $ 435,393 awarded by Office of Naval Research for UMassD MUST II: Computational Strategies for Scientific Data-Driven Learning for Marine and Undersea Technology Applications
Research
Research interests
- Numerical Analysis, scientific computing, computational partial differential equations
- Dimension reduction, model order reduction, reduced order modeling
- Uncertainty quantification, fractional-order partial differential equations
- Data mining, machine learning, image processing, neural networks
- high performance computing
Yanlai Chen received his B.S. degree in Mathematics from University of Science and Technology of China (USTC), in 2002, M.S. in Computer Science and Engineering from Department of Computer Science and Engineering , and Ph.D. in Mathematics from School of Mathematics, University of Minnesota, in 2007. He then worked as a Postdoctoral Researcher at Brown University before joining the Department of Mathematics at University of Massachusetts Dartmouth in August 2010 where he currently serves as a Full Professor.
Dr. Chen’s research has been supported by the NSF, the AFOSR, and by ONR via UMassD's MUST program. His research interests are in numerical analysis and scientific computing, in particular, finite element methods, reduced basis methods and other fast algorithms, uncertainty quantification, design and analysis for machine learning algorithms. He has graduated three doctoral students.
Deeply caring about undergraduate training, Dr. Chen led a team and initiated the UMassD's ACCOMPLISH program via an NSF grant that aims to provide a multi-faceted financial and social support and contextualized computing-centered educational framework for eligible STEM students. Dr. Chen's administrative experience includes serving as a Co-Graduate Program Director of the Engineering and Applied Science program since September 2020, and as a Co-Director of the Center for Scientific Computing and Data Science Research since January 2022.