Alfa Heryudono, PhD

Associate Professor

Mathematics

508-999-8516

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Liberal Arts 394H


Education

2008University of DelawarePhD
2002Southern Illinois University EdwardsvilleMS
2000University of IndonesiaBS

Teaching

  • Mathematical and Computational Consulting
  • Numerical Methods for PDEs
  • Numerical Linear Algebra
  • Numerical Optimization
  • Mathematical Modeling

Teaching

Programs

Teaching

Courses

Matrix methods with emphasis on applied data analysis. Matrix norms; LU, QR and SV decomposition of matrices; least squares problems, orthogonal vectors and matrices; applications to data analysis.

Matrix methods with emphasis on applied data analysis. Matrix norms; LU, QR and SV decomposition of matrices; least squares problems, orthogonal vectors and matrices; applications to data analysis.

Topics in high performance computing (HPC). Topics will be selected from the following: parallel processing, computer arithmetic, processes and operating systems, memory hierarchies, compilers, run time environment, memory allocation, preprocessors, multi-cores, clusters, and message passing. Introduction to the design, analysis, and implementation, of high-performance computational science and engineering applications.

Topics in high performance computing (HPC). Topics will be selected from the following: parallel processing, computer arithmetic, processes and operating systems, memory hierarchies, compilers, run time environment, memory allocation, preprocessors, multi-cores, clusters, and message passing. Introduction to the design, analysis, and implementation, of high-performance computational science and engineering applications.

Students research a topic of their choice in scientific computing over two successive semesters. Research skills taught include literature and web searches, reading scientific papers, and organizing and keeping research records.

Students research a topic of their choice in scientific computing over two successive semesters. Research skills taught include literature and web searches, reading scientific papers, and organizing and keeping research records.

Orthogonality and least square problems. Other topics include applications of eigenvalue, quadratic forms, Numerical Linear Algebra.

Topics in high performance computing (HPC). Topics will be selected from the following: parallel processing, computer arithmetic, processes and operating systems, memory hierarchies, compilers, run time environment, memory allocation, preprocessors, multi-cores, clusters, and message passing. Introduction to the design, analysis, and implementation, of high-performance computational science and engineering applications.

An intensive introduction to real-world mathematics using an assortment of mathematical challenges presented by industrial-problems. This course aims to prepare students to integrate and apply their mathematical knowledge to novel problems presented in industrial or research settings. Topics will be selected from the following: multidisciplinary projects solicited from various research groups at UMass Dartmouth, from local and national industries/universities/labs, and from crowdsourcing websites.

An introduction to numerical linear algebra. Numerical linear algebra is fundamental to all areas of computational mathematics. This course will cover direct numerical methods for solving linear systems and linear least squares problems, stability and conditioning, computational methods for finding eigenvalues and eigenvectors, and iterative methods for both linear systems and eigenvalue problems.

Research

Research Interests

  • Radial basis function methods
  • Spectral and pseudospectral methods
  • Tear film dynamics
  • Numerical conformal mapping
  • Mathematical problems in industry

External links

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