Jia Xu is a computer scientist and AI researcher with a global academic career spanning Europe, Asia and the United States. Her work currently focuses on natural language processing and large language models.
Xu began her academic journey in Germany. She completed her bachelor’s and master’s degrees at the TU Berlin and studied and worked exclusively in German. She later received her doctorate at RWTH Aachen under Professor Hermann Ney, a leading representative of machine translation. During this time, she also completed research stays at Microsoft Research and IBM Watson, giving her early insight into AI systems on an industrial scale.
She continued her academic career in Asia. Xu was an assistant professor and doctoral advisor at Tsinghua University and later became an associate professor at the Chinese Academy of Sciences. In these roles, she led research teams working on conversational systems, generalization of machine learning, and efficient AI models.
Jia Xu is known for combining theory with practical application. She is the author of around 50 research papers and the holder of 12 patents and provisional patents. Their teams have been among the top performers in 18 major AI competitions, including second place in the Amazon Alexa Prize Social Bot Challenge.
In recent years, Xu’s work has focused on making large language models smaller, smarter, and more sustainable. She believes that true success in AI comes from sustainable impact, not just size.
An interview with Jia Xu about building a global career in AI
Your career has taken you across Europe, Asia and the United States. Where did it all start?
I began my academic career in Germany at the age of nineteen. I moved there to study computer science and at the same time had to learn to live, study and think in a new language. I completed both my bachelor’s and master’s degrees at the TU Berlin entirely in German. This experience has shaped my approach to challenges. I learned early on that progress often comes from patience and persistence rather than speed.
How did this early experience influence your research mentality?
It taught me resilience. When language is limited, the basics speak. The basics lead. Listening is sharpened. The preparation deepens. This attitude remained with me during my doctorate at RWTH Aachen University, where I worked with Professor Hermann Ney in the field of machine translation. At that time, machine translation was still considered very difficult. Seeing how long-term research could slowly transform impossible ideas into real systems left a strong impression on me.
They have also spent time in industry research laboratories. What did these experiences add?
During my doctoral thesis I had research stays at Microsoft Research Redmond and IBM Watson. These environments showed me how large-scale research works. I am grateful for this time and my mentors and colleagues. Industrial laboratories place great value on whether ideas can work in real systems. This balance between theory and application has stayed with me. It reinforced my belief that sound research should ultimately be linked to real-world use cases.
After completing your doctorate, you took on academic leadership positions in Asia. What particularly struck you during this phase?
I was an assistant professor and doctoral advisor at Tsinghua University and later an associate professor at the Chinese Academy of Sciences. They were intensive and productive years. I have worked with talented students and researchers on machine learning and natural language processing. Different academic cultures value different things, and adapting to those expectations has helped me grow as a leader. I learned that thinking is just as important as directing.
Many people know your work through AI competitions. Why were these important to you?
Competitions test whether ideas actually work. My teams have contributed to 18 world-class results on major natural language processing challenges. A highlight was second place in the Amazon Alexa Prize Social Bot Challenge. This project forced us to think about long-term conversations, system stability and user experience. It became clear that accuracy alone is not enough. Real systems must be reliable, efficient and responsive.
In recent years, your research has focused on efficiency and smaller models. Why is this important?
Large language models are impressive, but they are expensive and resource intensive. Many organizations cannot easily use them. I’m interested in making models smaller and smarter so they can be used more widely. Efficiency is not about lowering standards. It’s about better design. A well-built, smaller model can be more practical and trustworthy in real-world environments.
How do you personally define success in your field?
I measure success using two metrics. One of them is my own judgment as a researcher. I understand the depth and impact of my work. The second is social feedback. If an idea is recognized and helps make the world a better place, then it is important. Decades ago, machine translation seemed unrealistic. Today it is part of everyday communication. It is meaningful for me to be part of this long journey of transforming the unattainable into something attainable.
They place great value on values and integrity. Where does that come from?
Every career comes with challenges that will test your principles. I believe that lasting success comes from staying true to your goals and social values, even if it can be difficult at times. Authenticity is important. It influences the way one collaborates with colleagues, supervises students, and selects research problems. For me, success is not just about performance. It’s about contributing something beyond yourself.
What role does mentoring play in your work today?
Mentoring is at the heart of my work. I help students view research not as a series of immediate successes, but as a long-term journey in which setbacks are a stepping stone. Success comes from constant commitment and curiosity. At the same time, I learn from my students. Her questions, new perspectives, and fearless curiosity constantly push me to grow and evolve. For me, mentoring is a team journey full of discovery, resilience and shared growth.
