NIAI Lab @ Hanyang

Neuro-inspired AI group

Our group is interested in understanding principles that underlie learning and inference in the brain and machines, mainly by using tools from machine learning and statistics.

Our research aims to uncover the fundamental principles of learning and inference that give rise to intelligent behavior in both biological and artificial systems. Humans and animals continuously learn from sensory experiences, infer hidden structure from incomplete observations, and flexibly adapt to changing environments. Understanding these capabilities remains a central challenge in neuroscience and artificial intelligence. By integrating ideas from machine learning, computational neuroscience, and cognitive science, we seek unified computational principles that explain how intelligence emerges from learning and interaction with the world.

Our long-term vision is to develop an integrated brain-inspired architecture that captures core functions of natural intelligence within a unified computational framework. Rather than designing isolated models for individual tasks, we seek to understand how these functions interact to produce adaptive and flexible behavior. Ultimately, our goal is to build intelligent systems that learn, perceive, remember, and act in ways that more closely resemble animals and humans, while also advancing our understanding of intelligence itself.

CompLearn Compositional learning

ModLearn Modular learning

ObjLearn Object-centric learning

SelfSup Self-supervised learning

NetInfer Inference of network structure

RuleInfer Inference of learning rule

PercInfer Perceptual inference

Causal Causal inference

NeuroData Neural data analysis

BioMemory Biological memory

NeuralDyn Neural dynamics and state-space modeling

BrainComp Brain-inspired computation

CompGen Compositional generalization

Transfer Transfer and out-of-distribution generalization

Abstract Abstraction and reasoning

We are currently seeking highly motivated individuals pursuing PhD degree to join our research team:

Key Responsibilities:

Design, implement, and evaluate novel deep neural networks and related methods.
Collaborate with research colleagues to efficiently implement structured machine learning architectures.
Effectively communicate research findings and developments, both internally and externally, through clear and concise written and verbal presentations.
Stay up to date with the latest advancements in machine learning by actively exploring and assimilating new research.
Foster collaborations with external collaborators and maintain relationships with relevant research labs and individuals.

Minimum Qualifications:

A Bachelor’s degree in a technical field or equivalent practical experience.
Proficiency in programming languages such as Python, PyTorch, JAX, etc.