Broadly, my research focuses on fluid human-machine teaming (HMT) through human augmentation (e.g., XR interfaces, exoskeletons, and other wearables) and artificial intelligence (AI), especially physical AI in various industrial and medical contexts. In particular, I am interested in multi-agent systems in which humans, robots (collaborative, industrial, and medical), and AI agents work side by side to achieve their goals safely and effectively.
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What is Human-Machine Teaming (HMT), and how do I define it?
In the context of my research, I understand HMT to be an operational paradigm in which humans and autonomous AI or robotic systems collaborate as interdependent partners to achieve a shared objective. Unlike traditional tool usage or basic automation, where a machine passively executes strict inputs, teaming relies on bidirectional communication, mutual trust, and dynamic task sharing based on real-time shared situational awareness.
My Research Trajectory
My research trajectory began with immersive visual analytics and virtual reality (VR) to help engineers, designers, and technicians visualize and navigate highly complex, multidimensional data in turbomachinery design. Over time, I shifted from visualizing machine data to investigating how digital twinning (DT) can enable more natural human-environment interactions. This evolution led me to my current focus on human-oriented cyber-physical systems and human-machine teaming (HMT), where I explore the seamless integration of human cognition with robotic and digital infrastructure. To address the bottleneck of manual modeling, I am now also leveraging Generative AI and neural rendering techniques, such as Gaussian Splatting, to automate and accelerate 3D content creation for these complex systems. By lowering the barrier to high-fidelity asset generation, I aim to create truly responsive environments where digital information is instantly accessible and intuitive for the human operator.
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