I am Tue Vu, an AI and Machine Learning Researcher Scientist, Engineer, and Educator with more than 15 years of experience working at the intersection of Data Science, High-Performance Computing, and real-world problem solving. My work spans Generative AI, Large Language Models, Computer Vision, NLP, Climate and Geospatial analytics. Across academia and applied consulting, I have focused on building end-to-end AI systems that move beyond experimentation and deliver practical value.

Currently, I serve as an AI and ML Research Scientist and Lead Data Scientist at Southern Methodist University, where I support advanced research and applied AI initiatives across multiple disciplines. In this role, I design and deploy Deep Learning and Generative AI solutions on GPU-accelerated infrastructure, including NVIDIA SuperPOD, and ManeFrame HPC System. My recent work includes building RAG pipelines, multimodal AI systems, production chatbots, finance-focused language model workflows, anomaly detection models, and large-scale data pipelines using RAPIDS, Dask, PyTorch, TensorFlow, and Hugging Face.

Before SMU, I worked at Clemson University as a Senior HPC Facilitator, Senior Data Scientist, and Research Assistant Professor, where I helped researchers leverage high-performance computing and machine learning for scientific discovery. Earlier in my career at the National University of Singapore, I applied AI and statistical modeling to climate science, hydrology, tsunami simulation, and environmental forecasting. That foundation gave me a deep appreciation for combining domain knowledge, scalable computation, and rigorous modeling to solve complex problems.

I hold a PhD in Computational Hydro-Informatics from the National University of Singapore and an MS in Data Science from Southern Methodist University, with a specialization in Machine Learning and Generative AI. I am also an NVIDIA DLI Ambassador and have delivered more than 80 workshops to students, researchers, and professionals on deep learning, accelerated data science, multi-GPU training, NLP, and generative AI. I enjoy translating complex technical ideas into practical workflows that others can adopt and build on.

What defines my work is a balance of research depth and implementation focus. I enjoy taking ideas from concept to deployment, whether that means training models, optimizing distributed GPU workloads, building retrieval pipelines, or creating AI tools that help organizations make better decisions. I am especially interested in generative AI, LLM evaluation and fine-tuning, multimodal systems, and scalable AI infrastructure.

I am always open to connecting with people working in AI, machine learning, HPC, research computing, and applied data science, especially where innovation can create meaningful impact across science, education, and industry.