Wenjian Hao
Ph.D. candidate, Purdue University AAE
PapersProjectsBlogMiscellaneous

I am a Ph.D. candidate in the School of Aeronautics and Astronautics at Purdue University, advised by Dr. Shaoshuai Mou. My research focuses on learning-based control for autonomous robots; data-driven modeling of nonlinear dynamics using globally linear representations; data-efficient reinforcement learning; optimal, safety-critical, and sampling-based control; and multi-agent systems.

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Wenjian Hao portrait

Highlighted Work

Accelerating sampling-based control via learned linear Koopman dynamics
This paper presents an efficient MPPI control framework using learned linear Koopman dynamics to reduce rollout cost while maintaining control performance.
W Hao, Y Fang, Z Lu, S Mou
2026 · [PDF] / [Code]
A control-barrier-function-based algorithm for policy adaptation in reinforcement learning
This paper formulates policy adaptation as constrained optimization and uses control barrier functions to guarantee objective-preserving adaptation.
W Hao, Z Lu, N Miguel, S Mou
2025 · [PDF] / [Code]
Deep Koopman learning using noisy data
This paper develops deep Koopman learning under bounded measurement noise by explicitly modeling and mitigating noise effects during training.
W Hao, D Upadhyay, S Mou
TMLR 2025 · [PDF] / [Code]
Distributed Koopman learning using partial trajectories for control
This paper proposes distributed deep Koopman learning with partial trajectories, allowing consensus dynamics learning without sharing private training data.
W Hao, Z Lu, D Upadhyay, S Mou
ACC 2026 · [PDF] / [Code]
Distributed Koopman learning with incomplete measurements
This paper develops distributed Koopman learning for networks with partial observations, enabling cooperative global dynamics reconstruction.
W Hao, L Wang, A Rai, S Mou
2024 · [PDF] / [Code]
C3d: cascade control with change point detection and deep Koopman learning for autonomous surface vehicles
This paper introduces C3D, a modular ASV control architecture combining deep Koopman learning and change-point detection for robust maritime autonomy.
J Li, H Park, W Hao, L Xin, J Chavez-Galaviz, A Chaudhary, M Bloss, ...
2024 · [PDF] / [Code]
Deep Koopman learning of nonlinear time-varying systems
This paper proposes deep Koopman learning for nonlinear time-varying systems with error analysis and computationally efficient prediction.
W Hao, B Huang, W Pan, D Wu, S Mou
Automatica 2024 · [PDF] / [Code]
A data-driven approach for inverse optimal control
This paper proposes an iterative data-driven inverse optimal control method that jointly learns unknown dynamics and objective weights.
Z Liang, W Hao, S Mou
CDC 2023 · [PDF] / [Code]
Optimal control of nonlinear systems with unknown dynamics
This paper presents a data-driven actor-critic Koopman framework for closed-loop optimal control of systems with unknown dynamics.
W Hao, PC Heredia, S Mou
2023 · [PDF] / [Code]
Deep learning of Koopman representation for control
This paper develops a model-free control pipeline using deep Koopman representations learned directly from interaction data.
Y Han, W Hao, U Vaidya
CDC 2020 · [PDF] / [Code]

Projects

Automatic Trading Platform
Project goal: Designed and developed an automatic trading platform for quantitative strategy research and execution. The platform integrates data ingestion, signal generation, portfolio construction, risk control, and...
March 2026
Explainable Reflexive Control (RefleXAI)
Project goal: RefleXAI is a DARPA-backed effort on adaptive control for uncrewed sea vessels, developed through the Saab and Purdue University collaboration. The project focuses on explainable reflexive control...
August 2022
Secure and Safe Assured Autonomy (S2A2)
Project goal: S2A2 is a NASA University Leadership Initiative project focused on secure and safe assured autonomy. The program develops foundations and practical methods for autonomy that remain reliable, trustworthy,...
August 2021
Virtual Prototyping of Autonomy-Enabled Ground Systems (VIPR-GS)
Project goal: VIPR-GS advances virtual prototyping methods for autonomy-enabled ground systems. The project emphasizes model-based engineering, simulation, and validation workflows to accelerate development and de-risk...
October 2020

Blog

Learning nonlinear systems using linear operators and machine learning
A concise introduction to learning nonlinear systems using linear operators and machine learning for prediction and control.
March 2026

Miscellaneous

News
Updates, announcements, and short highlights.
March 2026