About Me. I am a tenure-track Assistant Professor of Computer Science at Boise State University. My research focuses on computer vision, embodied agents, continual learning, domain adaptation, AI efficiency and safety, and their real-world applications. I bring extensive industry experience to my academic role. Prior to joining Boise State, I worked as a Machine Learning Engineer at Toyota Automated Logistics, where I developed machine learning solutions for complex problems in computer vision and robotics. I did research on AI for medical imaging at Siemens in the summer of 2022. I earned my Ph.D. in Computer Science from the University of Kentucky in March 2023, under the supervision of Dr. Nathan Jacobs. In my free time, I enjoy outdoor activities across Idaho and the broader Pacific Northwest.

Collaboration with Me. I am open to research collaborations, invited talks, and other opportunities from both within and outside my institution. I believe that impactful research often grows from long-term, meaningful collaborations. If you're interested in working together or starting a conversation, feel free to drop me an email. Let’s connect!

Join My Lab. We always welcome new members at all levels to join my Lab! We encourage a supportive culture where you can do impactful research, learn new skills, and achieve your personal goals.

Research Interests: My research aims on continual and adaptive perception and decision-making for real-world autonomy. I am interested in a wide range of research fields from computer vision, embodied agents, continual learning, domain adaptation, AI efficiency and safety, digital twins, remote sensing, and AI for Interdisciplinary Sciences. Currently I am actively working on:

1. Continual Learning and Adaptation
   As large models are increasingly deployed in dynamic environments, they must handle changing scenarios and conditions. We build robust systems by studying continual learning to learn from evolving data streams while mitigating catastrophic forgetting, and domain and test-time adaptation to improve generalization under distribution shifts.
   Keywords: Continual Learning, Test-Time Adaptation, Domain Adaptation, Computer Vision
2. 3D Computer Vision and Embodied Agents
   Understanding 3D structure is essential for intelligent robots. We study core 3D vision tasks such as depth estimation, pose regression, and reconstruction to enable how robots perceive and interact with their environment. This supports research on embodied agents, weakly supervised learning, multimodal learning, and progress toward real-world autonomy.
   Keywords: Embodied Agents, Robotics, 3D Vision
3. Scalable and Trustworthy AI Systems
   Modern AI systems must be fast, adaptive, and trustworthy. Instead of training large models from scratch, we focus on efficient adaptation techniques such as LoRA and lightweight fine-tuning, enabling powerful models to run on limited resources. We also study privacy-preserving and secure learning, including federated and decentralized approaches, to ensure data safety and robustness in real-world deployment.
   Keywords: Parameter-Efficient Learning, Trustworthy AI, Privacy-Preserving Learning

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