faculty
Alfa Heryudono, PhD
Associate Professor
Mathematics
Contact
508-999-8516
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Liberal Arts 394H
Education
2008 | University of Delaware | PhD |
2002 | Southern Illinois University Edwardsville | MS |
2000 | University of Indonesia | BS |
Teaching
- Mathematical and Computational Consulting
- Numerical Methods for PDEs
- Numerical Linear Algebra
- Numerical Optimization
- Mathematical Modeling
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
Application of knowledge discovery and data mining tools and techniques to large data repositories or data streams. This project-based capstone course provides students with a framework in which students gain both understanding and insight into the application of knowledge discovery tools and principles on data within the student's cognate area. This course is intended for data science majors only.
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.
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.
A team-based learning experience that gives students the opportunity to synthesize prerequisite course material and to conduct real-world analytics projects using large data sets of diverse types and sources. Students work in independent teams to design, implement, and evaluate an appropriate data integration, analysis, and display system. Oral and written reports and ethical aspects are highlighted.
Written presentation of an original research topic in Data Science which demonstrates the knowledge & capability to conduct independent research. The thesis shall be completed under the supervision of a faculty advisor. An oral examination in defense is required.
Written presentation of an original research topic in Data Science which demonstrates the knowledge & capability to conduct independent research. The thesis shall be completed under the supervision of a faculty advisor. An oral examination in defense is required.
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.
Investigations of a fundamental and/or applied nature representing an original contribution to the scholarly research literature of the field. PhD dissertations are often published in refereed journals or presented at major conferences. A written dissertation must be completed in accordance with the rules of the Graduate School and the College of Engineering. Admission to the course is based on successful completion of the PhD comprehensive examination and submission of a formal proposal endorsed by the student's graduate committee and submitted to the EAS Graduate Program Director.
Investigations of a fundamental and/or applied nature representing an original contribution to the scholarly research literature of the field. PhD dissertations are often published in refereed journals or presented at major conferences. A written dissertation must be completed in accordance with the rules of the Graduate School and the College of Engineering. Admission to the course is based on successful completion of the PhD comprehensive examination and submission of a formal proposal endorsed by the student's graduate committee and submitted to the EAS Graduate Program Director.
Research
Research awards
- $ 438,284 awarded by Office of Naval Research for UMassD MUST IV: Learning Nonlinear Dynamical Systems from Sparse and Noisy Data: Applications to Signal Detection and Recovery
- $ 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
- Radial basis function methods
- Spectral and pseudospectral methods
- Tear film dynamics
- Numerical conformal mapping
- Mathematical problems in industry