Zhaoze Wang

I am a Ph.D. student at the UPenn GRASP Lab. I study predictive world models and compact latent representations for multimodal sequence learning, long-horizon prediction, and planning, drawing inspiration from biological memory and navigation.

I previously interned at Adobe Research, where I worked on video world models, with a focus on efficient generation and long-horizon prediction.

I am seeking research internships from Summer through Fall 2027, with availability for a continuous internship across both seasons.

Email / Google Scholar / GitHub / LinkedIn / X

Research Interests

The brain's ability to build internal predictive models is a key inspiration behind world models. My research asks how such representations can support not just prediction, but fast updating and replanning as observations and goals change.

I study compact predictive representations, recurrent memory, and learned dynamics, drawing on how the brain integrates sensory experience to guide action. My goal is to translate these principles into fast, adaptive robotic systems.

I study how sensory sequences shape spatial representations [1] and how structured predictive states emerge [2]. I also work on memory-guided planning [3] and efficient video world models. To support model training, I develop compact visual encoders [4] and parallel sensory simulation [5].

Projects
	A Simple Model of Co-Emergence of Grid and Place Fields Zhaoze Wang, Genela Morris, Dori Derdikman, Pratik Chaudhari, Vijay Balasubramanian arXiv 2026 Paper Project Page GitHub
	RatatouGym: GPU-Parallel Multimodal Navigation Simulation Project Page Documentation Source Code
	REMI: Reconstructing Episodic Memory During Internally Driven Path Planning Zhaoze Wang, Genela Morris^†, Dori Derdikman^†, Pratik Chaudhari^†, Vijay Balasubramanian^† NeurIPS 2025 Paper Project Page Simulation Suite Vision Encoder
	Time Makes Space: Emergence of Place Fields in Networks Encoding Temporally Continuous Sensory Experiences Zhaoze Wang^†, Ronald W. Di Tullio^†, Spencer Rooke, Vijay Balasubramanian NeurIPS 2024 Paper Code Video Project Page
	Trading Place for Space: Increasing Location Resolution Reduces Contextual Capacity in Hippocampal Codes Spencer Rooke, Zhaoze Wang, Ronald W. Di Tullio, Vijay Balasubramanian NeurIPS 2024 Oral Paper Video
	BtnkMAE: Compact and Decodable Visual Representations Source Code
	NN4N: Neural Networks for Neurosimulations Documentation Source Code