Anuththara Rupasinghe, Ph.D.
Postdoctoral Research Fellow, Princeton University
Machine Learning | Statistical Signal Processing | Data Science | Computational Neuroscience | Biomedical AI
I am a machine learning and computational neuroscience researcher with a Ph.D. in Electrical and Computer Engineering and postdoctoral training at the Princeton Neuroscience Institute. My work focuses on developing statistical and machine learning methods for analyzing complex neural and biological data, with an emphasis on probabilistic modeling, statistical signal processing, time-series analysis, uncertainty quantification, and interpretable inference.
My path has been shaped by a long-standing interest in mathematics, statistics, engineering, and data-driven scientific discovery. During my undergraduate studies in Electrical and Electronic Engineering at the University of Peradeniya, Sri Lanka, I received the Best Performance in Engineering award in 2016, along with several prizes in electrical engineering, engineering mathematics, communications, and power systems.
In 2017, I joined the Department of Electrical and Computer Engineering at the University of Maryland, College Park, supported by the Clark School of Engineering Distinguished Graduate Fellowship. There, I completed my Ph.D. under the supervision of Professor Behtash Babadi, developing Bayesian methods for neural and physiological time-series analysis. In 2022, I was selected as a Rising Star in EECS.
Since graduating in 2022, I have been a postdoctoral research fellow at Princeton University, working with Professor Jonathan Pillow. At Princeton, I develop machine learning and statistical modeling approaches for understanding neural population activity and behavior. My postdoctoral work has been recognized by the Princeton Neuroscience Institute QCN T32 Postdoctoral Fellowship Award.
Across my research, I am interested in using statistical modeling and machine learning to extract meaningful structure from high-dimensional, noisy, and temporally structured data. I am currently exploring research scientist, applied scientist, machine learning, data science, computational neuroscience, and biomedical AI roles where I can contribute to method development, data analysis, and interdisciplinary applications.
Research interests
- Machine learning
- Bayesian inference
- Predictive modeling
- Data science
- Time-series analysis
- Statistical signal processing
- Computational neuroscience
- Biomedical AI
- Uncertainty quantification
Selected Highlights
2026
Continuous Multinomial Logistic Regression accepted at ICLR 2026.
2026
Continuous partitioning of neuronal variability published in eLife.
2025
Patchy harmonic functional connectivity of the mouse auditory cortex published in PNAS.
2024
Received the Princeton Neuroscience Institute QCN T32 Postdoctoral Fellowship Award.
2024
Received the SfN Trainee Professional Development Award.
2022
Started as a Postdoctoral Researcher at Princeton University, Princeton Neuroscience Institute.
2022
Received my Ph.D. in Electrical and Computer Engineering from the University of Maryland, College Park.
2022
Selected as a Rising Star in EECS.
2021
2020
Multitaper Analysis of Semi-Stationary Spectra From Multivariate Neuronal Spiking Observations published in IEEE Transactions on Signal Processing.
2018
Received the Outstanding Teaching Assistant Award, Department of Electrical & Computer Engineering, University of Maryland.
2017
Received the Clark School of Engineering Distinguished Graduate Fellowship, University of Maryland.
2016
Received multiple prizes at the General Convocation, University of Peradeniya, Sri Lanka, including the Best Performance in Engineering, Best Performance in Electrical & Electronic Engineering, Best Performance in Engineering Mathematics, Best Performance in Electronic Communications, and Best Performance in Electrical Power and Machines.
2011
Received Most Outstanding Performance of the Year at Mahamaya Girls’ College, Kandy, Sri Lanka; ranked 4th in Sri Lanka in the G.C.E. Advanced Level Examination, Physical Science stream; and ranked 9th in Sri Lanka in the Olympiad Mathematics Competition.
Contact
Email: ar0621@princeton.edu
LinkedIn: Anuththara Rupasinghe
Google Scholar: Google Scholar Profile
GitHub: Anuththara-Rupasinghe