research

Humans are able to recognize and remember visual experiences effortlessly in rich detail, a feat that current artificial systems still struggle to accomplish.

Our goal is to understand how the human brain achieve this impressive computational feat to develop “cognitive machines”— biologically inspired computational models that can recognize and interact with the world like humans.

To achieve this goal, we investigate human perception and memory processes using a combination of human neuroimaging (fMRI and MEG/EEG), behavior, computational modeling and machine learning.

ARTIFICIAL INTELLIGENCE

Computer Vision & Deep Learning

Image, Video and Audio Understanding

Low Resolution Face Recognition
Object Recognition and Detection
Sound Recognition
Video/Audio Classification

Generative Models

Computer Vision & Deep Learning

Generative Adversarial Networks for Audio Vision

Machine Learning

Computer Vision & Deep Learning

Machine Learning

Pattern recognition using supervised, semi-supervised and self-supervised learning

COGNITIVE COMPUTATIONAL NEUROSCIENCE

Computational Neuroscience

Modeling human brain visual system with deep neural networks
Brain-inspired AI
Neural data analysis methods (fMRI, MEG, EEG, fNIRS)

Human Perception

Computational Neuroscience

Spatiotemporal neural dynamics of human vision and audition

Visual Memory

Computational Neuroscience

Visual Memory

Neural signature of image memorability in human brain in adults and children