Dongkuan (DK) Xu

I am an Assistant Professor of Computer Science at NC State University, where I lead the Generative Intelligent Computing (GIC) Lab. My research focuses on machine learning, natural language processing, and computer vision, with an emphasis on efficient and reliable agentic AI systems. I received several awards, including the AAAI Best Demonstration Award 2025, the Best Poster Award at AAAI 2025 Spring Symposium on GenAI@Edge, 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. I hold my Ph.D. from Penn State, M.S. from the University of Chinese Academy of Sciences, and B.E. from Renmin University of China.

I am also involved in MARINA, a research initiative at the intersection of AI and ocean sciences, focusing on advancing ocean modeling, environmental monitoring, and AI-driven scientific discovery. As part of my broader work on agentic AI systems, I have contributed to the development of Synthora and Gentopia. Synthora is an extensible AI platform designed to enhance the computational efficiency of LLM agent applications through full-stack software-system hardware optimization. Gentopia is a lightweight framework for LLM-driven autonomous agents, providing essential components for building, testing, and evaluating AI agent applications.

Outside of work, I am a fan of American football (Nittany Lions, Eagles, Cowboys) and enjoy working out and playing soccer.

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📣 Seeking 1-2 PhD students, encouraging candidates from underrepresented groups. Interested applicants should email "PhD Fall 2026" with their CV.

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My research focuses on scalable, efficient, and reliable learning for agentic AI systems, particularly in the context of large language models (LLMs), diffusion models, and other generative AI models. I explore methods that enhance inference-time performance, shift computational efficiency curves, and ensure robust and reliable learning across diverse applications.

• Test-Time Scaling & Adaptation. I study test-time scaling laws to dynamically enhance AI performance at inference and testing stages. My work explores adaptive inference strategies, while also leveraging test-time training techniques to generate and utilize high-quality data. Additionally, I focus on optimizing reasoning workflows to improve model accuracy through self-refinement mechanisms.

• Shifting the Computational Curve. To optimize the efficiency-performance tradeoff in AI systems, my research focuses on accelerating models and enabling dynamic computation. This includes model compression and distillation techniques, as well as conditional computation methods. I also explore system-algorithm co-design for large model inference and training, along with hardware-algorithm co-design to develop energy-efficient neural network architectures and optimize AI-accelerator collaboration.

• Reliable Learning & Robust Generalization. Beyond efficiency, I focus on trustworthy AI by ensuring robustness, uncertainty quantification, and adaptive learning. My research includes uncertainty-aware AI techniques that enable models to recognize and communicate prediction uncertainty, improving their reliability in deployment. Furthermore, I investigate adaptive learning strategies that allow AI systems to refine their knowledge efficiently from new data with minimal supervision.

• Applications. My research contributes to real-world challenges in Education, Robotics, Agriculture, Networking, Sustainability, Climate, Transportation, Healthcare.

• MISSION: To revolutionize LLM decoding with adaptive, compute-optimal strategies and test-time learning

  ✅ Fine-Tuning-Free Efficiency: Achieve 2.5x speedup without the need for fine-tuning

  ✅ Dynamic Adaptability: Align token predictions with evolving output distributions in real time

• Project Website [link] / AAAI'25 Oral Paper Award

• MISSION: To build artificial general intelligence through collective growth of generative intelligent agents

  ⭐ Demo I (Create Agents), ⭐ Demo II (Customize and Interact with Agents), ⭐ Demo III (Evaluate Agents)

• Project Website [link] / Full Documentation [link] / EMNLP'23 (System Demo Track)

• MISSION: To reshape next-generation education through trustworthy generative AI technologies

• ✅ Innovations: ① Trust & Transparency, ② Dynamic & Controllable, ③ Multimodal & Multifunctional

• Project Website [link] / Video Demo [link] / AAAI'25 (Best Demonstration Award)

*Last updated on 04/22/2025*