DK is an Assistant Professor of Computer Science at NC State University, where he leads the Generative Intelligent Computing (GIC) Lab. His research spans machine learning, natural language processing, and computer vision, with a central focus on building scalable, efficient, and reliable AI systems. He investigates inference-time optimization, test-time adaptation, and computational efficiency in large language models, diffusion models, and other generative AI frameworks. His work explores test-time training, self-refinement, model compression, conditional computation, and hardware-algorithm co-design to improve performance while minimizing energy and compute costs. DK also advances trustworthy AI through uncertainty-aware learning and robust generalization, enabling adaptive systems that can learn effectively from limited supervision in dynamic tasks and data environments. His work has been applied across a wide range of domains, including education, networking, robotics, agriculture, sustainability, climate, transportation, and healthcare. He has received several honors, including the AAAI Best Demonstration Award (2025), the Best Poster Award at AAAI Spring Symposium on GenAI@Edge (2025), the NVIDIA Academic Grant Program Award (2025), the Microsoft Accelerating Foundation Models Research Award (2024), the NCSU Carla Savage Award (2024), the Best Paper Award Runner-Up at IEEE IPCCC (2024), and the Best Paper Award at ICCCN (2023). His research has been featured in top venues across AI, NLP, CV, and systems. DK has chaired or co-chaired workshops at DAC, CVPR, AAAI, and KDD, and actively serves as a panelist, area chair, senior program committee member, and reviewer for the NSF and major conferences. His long-term goal is to democratize access to generative AI and enhance its reliability and usability across real-world applications [Updated: April 13, 2025].

---> More information can be found at https://dongkuanx27.github.io/