Sirani Perera

Sirani M. Perera graduated with a B.Sc. (First Class Honors) degree in Mathematics from the University of Sri Jayewardenepura, Sri Lanka, in 2004. She became the first Sri Lankan undergraduate in mathematics to be selected by the Center for Mathematical Sciences at the University of Cambridge, UK , and received a full scholarship from the Cambridge Commonwealth Trust. In 2006, she graduated with a Master of Advanced Studies (the Part III Tripos) in Mathematics (with Honors) at the University of Cambridge, UK, and earned a Ph.D. in Mathematics from the University of Connecticut in 2012. She is a full Professor of Mathematics at Embry‑Riddle Aeronautical University starting in the fall of 2026. Dr. Perera was awarded the ILAS Olga Taussky and John Todd Prize for outstanding mid-career achievement by the International Linear Algebra Society (ILAS) in 2026. In 2024, she was honored as a Rising Star in Science by the Academy of Science, Engineering, and Medicine in Florida (ASEMFL). In 2023, Perera was selected as A Convergence Research (CORE) Fellow at the CORE Institute: The University of California, San Diego, under the National Science Foundation (NSF) Convergence Accelerator Program. Dr. Perera's research is funded by the NSF, specifically by the divisions of Mathematical Sciences (DMS), Electrical, Communications and Cyber Systems (ECCS), Computer and Network Systems (CNS) and Undergraduate Education (DUE). Dr. Perera is an applied mathematician with expertise in applied linear algebra and low-complexity algorithms, including both classical and machine learning (ML) approaches. Her research spans computational mathematics, scientific computing, fast Fourier transform (FFT)-like algorithms, ML algorithms, algorithm generalization and numerical accuracy and reliability. She also specializes in structured matrix theory, with work involving Vandermonde matrices, Delay Vandermonde matrix (DVM), quasiseparable and semiseparable matrices, Bezoutians, Toeplitz, Hankel, circulant and banded matrices, as well as discrete cosine transform (DCT), discrete sine transform (DST) and discrete Fourier transform (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. Dr. Perera 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, thereby fostering scientific discoveries.