Research Directions I Hope to Pursue with My Future Students

My research is motivated by a simple but powerful idea: many modern engineering systems cannot be designed effectively unless structure, dynamics, and control are considered together. This perspective—often referred to as control co-design—shapes how I think about research problems, how I mentor students, and how I aim to build a research group. Below, I outline several research directions that I am excited to pursue with future graduate and undergraduate students. These themes are closely connected, and students may engage with one or multiple areas depending on their interests and background.

Control Co-Design as a Unifying Research Framework

Across energy systems, robotics, and mechatronics, performance is increasingly determined by strong coupling between physical design choices and control strategies. A central focus of my group will be developing methods that jointly optimize plant design, control, and system-level objectives. Student projects in this area may involve formulating integrated design–control optimization problems, studying trade-offs among performance, robustness, cost, and reliability, and developing scalable workflows for complex, multi-physics systems.

Surrogate Modeling and Data-Driven Design Exploration

High-fidelity simulations and experiments are essential but often too expensive to use directly inside iterative design loops. A major thrust of my research is developing surrogate models that preserve physical structure while enabling fast evaluation. With students, I am interested in exploring physics-informed and structure-preserving surrogate models, dimensionality reduction for high-dimensional dynamic systems, and data-driven tools that accelerate early-stage design decisions without sacrificing interpretability.

AI-Guided System Architecture and Learning from Optimized Designs

Beyond tuning parameters, many engineering systems require choosing an underlying architecture: how components are connected, how power or information flows, and how control authority is distributed. I am particularly interested in how machine learning can extract reusable design knowledge from prior optimized solutions. Potential student projects include learning architecture–performance relationships from control co-design data, graph-based representations of engineered systems, and AI-guided screening of large design spaces prior to expensive optimization.

Hybrid Renewable Energy Systems and Hardware-Validated Research

Hybrid energy systems—such as floating wind–wave platforms—highlight the importance of control co-design in practice. Small design or control changes can propagate across subsystems and significantly affect performance, stability, and cost. Students working in this area may develop coupled simulation models of hybrid renewable platforms, study how control strategies reshape system-level trade-offs, and participate in hardware-in-the-loop or experiment-driven validation efforts.

What It Is Like to Work With Me

Beyond technical training, I aim to create a research environment that is supportive, rigorous, and sustainable. Students in my group can expect clear expectations and regular feedback, an emphasis on fundamentals and reproducibility, encouragement to develop independence, and respect for well-being and long-term growth.

Looking Ahead

These research directions will evolve as students contribute new ideas and help shape the direction of the group. I am always excited to talk with curious, motivated students who enjoy working at the intersection of modeling, control, data, and real-world systems.