Scholar
Sirani M. Perera
Google Scholar ID: QgrLwXAAAAAJ
Embry-Riddle Aeronautical University
Citations & Impact
Citations
274
H-index
9
i10-index
9
Publications
20
Co-authors
22
list available
Publications
Resume (English only)
Academic Achievements
- In 2024, named a Rising Star in Science by the Academy of Science, Engineering, and Medicine in Florida (ASEMFL). Her research spans applied linear algebra and low-complexity algorithms, including both classical and machine learning (ML) approaches. She has expertise in computational mathematics, scientific computing, FFT-like algorithms, ML algorithms, numerical accuracy and reliability, and algorithm generalization. Additionally, she specializes in structured matrix theory.
Research Experience
- Currently a tenured Associate Professor of Mathematics at Embry-Riddle Aeronautical University. Her research is supported by the National Science Foundation (NSF), including the divisions for Mathematical Sciences (DMS), Electrical, Communications, and Cyber Systems (ECCS), Computer and Network Systems (CNS), and Undergraduate Education (DUE). In 2023, she was selected as a Convergence Research (CORE) Fellow at the CORE Institute: The University of California San Diego, under the NSF Convergence Accelerator Program.
Education
- Graduated with a B.Sc. (First Class Honors) degree in Mathematics from the University of Sri Jayewardenepura, Sri Lanka, in 2004. In 2006, she graduated with a Master of Advanced Studies (the Part III Tripos) in Mathematics (with Honors) at the University of Cambridge, UK. Earned a Ph.D. in Mathematics from the University of Connecticut in 2012.
Background
- Research interests include applied linear algebra and low-complexity algorithms (including both classical and machine learning approaches), computational mathematics, scientific computing, FFT-like algorithms, ML algorithms, numerical accuracy and reliability, and algorithm generalization. She specializes in structured matrix theory, involving Vandermonde matrices, Delay Vandermonde matrices (DVM), quasiseparable and semiseparable matrices, Bezoutians, Toeplitz, Hankel, circulant, and banded matrices, as well as DCT, DST, and DFT matrices. She applies structured neural networks to solve problems in wireless communication, dynamical systems, digital signal processing, image processing, and unmanned autonomous aerial systems.
Miscellany
- She is passionate about proposing novel theories and developing low-complexity classical or ML algorithms that advance convergence across applied mathematics, engineering, celestial mechanics, and theoretical computer science to foster scientific discoveries.
