About
I am currently a second year Master's student in the Electrical and Computer Engineering Department at New York University, affiliated with the Neuroinformatics Lab under the supervision of Prof. Erdem Varol and Prof. Chinmay Hegde at NYU VIDA.
My research focuses on multimodal foundation modeling for neural signals, with an emphasis on neural decoding and representation learning across recording modalities, species, and cognitive domains. I am particularly interested in learning shared and transferable neural representations from complex brain data.
My research interests include brain-language representation learning, cross-species multimodal neural signal alignment, and machine learning methods for connecting neural activity to behavior. I have also worked on EEG–fMRI signal translation and speech and music reconstruction from neural activity.
Broadly, my goal is to bridge invasive and non-invasive neural signals and connect brain activity to language and behavior through machine learning.
Selected Publications
View All →Algorithmic Complexity of Tensor Network Contractions in Many-Body Localization
Jiale Liu, Ada Lovelace†
Physical Review E
Complexity analysis of tensor network contractions in MBL systems, identifying a complexity phase transition and proposing an adaptive algorithm.
Numerical Stability Analysis of High-Order Runge-Kutta Methods in Quantum Dynamics
Jiale Liu, Sarah O'Connor†
Journal of Computational Physics
A rigorous analysis of numerical stability boundaries for high-order Runge-Kutta integrators in the context of time-dependent quantum mechanics.
Monte Carlo Simulations of Phase Transitions in 2D Ising Models on Non-Euclidean Lattices
Charlie Brown, Jiale Liu†
2023 International Conference on Computational Physics (ICCP)
Monte Carlo study revealing mean-field-like phase transitions and critical exponent deviations for Ising models on hyperbolic non-Euclidean lattices.
News
Our work has been accepted by a prestigious journal 🎉
Starting my PhD at the University of Example