Wenxuan Tang

Trained in mathematics and data science, I have worked across both generative AI development and AI governance.

Previously, I worked as a Lead Generative AI Engineer, building LLM-based applications, retrieval systems, and fine-tuned language models for enterprise environments. Later, I moved into AI model evaluation and governance, focusing on the safety, reasoning, and deployment risks of generative AI systems.

Over time, I became increasingly interested in a different question:

How might AI help people observe and understand living systems more deeply, rather than simply optimize for efficiency?

My current work explores the intersection of ecology, field observation, explainable AI, and environmental storytelling — from plant intelligence and seasonal landscapes to human relationships with the natural world.

My work is grounded not only in machine learning, but also in long-term observation of plants, wetlands, birds, and seasonal ecological change.