Ali Yousefi
Associate ProfessorDr. Yousefi’s research focuses on developing methodological solutions to problems in neuroscience data analysis. His work can be divided into two categories: first, a methodological element, which focuses on developing a statistical framework for linking neural activity to biological and behavioral signals, as well as creating statistical estimation and inference algorithms, goodness-of-fit analyses, and mathematical theory that can be applied to different modalities of neural data; and second, an application element, where these methods are applied to neural data recorded from neural systems to dynamically model the neural activity of individual neurons, characterize how neural ensembles maintain representations of associated biological and behavioral signals, and reproduce these signals in real time. In his research, he has worked to integrate methodologies related to model identification, statistical inference, signal processing, Bayesian analysis, and stochastic estimation and control, expanding these methodologies by incorporating neural analysis models to make them more suitable for modeling the dynamics of neural systems observed through neural data, such as local field potential and spike train data.
Our lab focuses on research at the intersection of neuroengineering, brain-computer interfaces (BCI), and neuroscience data analysis. We develop computational methods and technologies to enhance our understanding of the brain and improve human health. By combining expertise in neuroscience, engineering, and machine learning, we aim to address complex challenges in brain function and cognitive enhancement.
We are committed to fostering an inclusive, collaborative environment where passionate individuals can contribute to advancing neuroengineering and creating impactful solutions for brain health.
Neuroscience Data Analysis
Neural Engineering
Brain Computer Interface
Selected Publications
- Ziaei, Navid, Behzad Nazari, Uri T. Eden, Alik Widge, and Ali Yousefi. "A Bayesian Gaussian Process-Based Latent Discriminative Generative Decoder (LDGD) Model for High-Dimensional Data." IEEE Access (2024).
- Basu, Ishita, Ali Yousefi, Britni Crocker, Rina Zelmann, Angelique C. Paulk, Noam Peled, Kristen K. Ellard et al. "Closed-loop enhancement and neural decoding of cognitive control in humans." Nature biomedical engineering 7, no. 4 (2023): 576-588.
- Yousefi, Ali, Ishita Basu, Angelique C. Paulk, Noam Peled, Emad N. Eskandar, Darin D. Dougherty, Sydney S. Cash, Alik S. Widge, and Uri T. Eden. "Decoding hidden cognitive states from behavior and physiology using a Bayesian approach." Neural computation 31, no. 9 (2019): 1751-1788.
- Yousefi, Ali, Anna K. Gillespie, Jennifer A. Guidera, Mattias Karlsson, Loren M. Frank, and Uri T. Eden. "Efficient decoding of multi-dimensional signals from population spiking activity using a Gaussian mixture particle filter." IEEE Transactions on Biomedical Engineering 66, no. 12 (2019): 3486-3498.
- Yousefi, Ali, Ishita Basu, Angelique C. Paulk, Noam Peled, Emad N. Eskandar, Darin D. Dougherty, Sydney S. Cash, Alik S. Widge, and Uri T. Eden. "Decoding hidden cognitive states from behavior and physiology using a Bayesian approach." Neural computation 31, no. 9 (2019): 1751-1788.
- Yousefi, Ali, Yalda Amidi, Behzad Nazari, and Uri T. Eden. "Assessing goodness-of-fit in marked point process models of neural population coding via time and rate rescaling." Neural computation 32, no. 11 (2020): 2145-2186.
- Yousefi, Ali, Angelique C. Paulk, Ishita Basu, Jonathan L. Mirsky, Darin D. Dougherty, Emad N. Eskandar, Uri T. Eden, and Alik S. Widge. "COMPASS: an open-source, general-purpose software toolkit for computational psychiatry." Frontiers in neuroscience 12 (2019): 957.
- Handbook of Neuroengineering: State Space Models for Spike Data
- Google Scholar: https://scholar.google.com/citations?user=jieyeRUAAAAJ