Jia Xu on Building a Global Career in Artificial Intelligence

Jia Xu is a computer scientist and AI researcher with a global academic career spanning Europe, Asia, and the United States. She is currently where her work 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 TU Berlin, studying and working entirely in German. She later earned her PhD from RWTH Aachen University under Professor Hermann Ney, a leading figure in machine translation. During this period, she also completed research visits at Microsoft Research and IBM Watson, gaining early exposure to industry-scale AI systems.

Her academic career continued in Asia. Xu served as an Assistant Professor and PhD adviser at Tsinghua University and later became an Associate Professor at the Chinese Academy of Sciences. Across these roles, she led research teams working on dialogue systems, machine learning generalisation, and efficient AI models.

Jia Xu is known for combining theory with real-world application. She has authored around 50 research papers and holds 12 patents and provisional patents. Her teams have ranked 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 true success in AI comes from lasting impact, not scale alone.

An Interview with Jia Xu on Building a Global Career in AI

Your career has taken you across Europe, Asia, and the United States. Where did it all begin?

I began my academic journey in Germany when I was nineteen. I moved there to study computer science and had to learn how to live, study, and think in a new language at the same time. I completed both my bachelor’s and master’s degrees at TU Berlin entirely in German. That experience shaped how I approach challenges. I learned early that progress often comes from patience and persistence rather than speed.

How did that early experience influence your research mindset?

It taught me resilience. When language is limited, fundamentals speak.. Fundamentals lead. Listening sharpens. Preparation deepens. That mindset stayed with me during my PhD at RWTH Aachen University, where I worked under Professor Hermann Ney in machine translation. At the time, machine translation was still considered very difficult. Seeing how long-term research could slowly turn impossible ideas into real systems left a strong impression on me.

You also spent time in industry research labs. What did those experiences add?

During my PhD, I had research visits at Microsoft Research Redmond and IBM Watson. Those environments showed me how research operates at scale. I am grateful for that time and my mentors and colleagues. Industry labs care deeply about whether ideas can work in real systems. That balance between theory and application stayed with me. It reinforced my belief that strong research should eventually connect to real use cases.

After your PhD, you moved into academic leadership roles in Asia. What stood out during that phase?

I served as an Assistant Professor and PhD adviser at Tsinghua University and later as an Associate Professor at the Chinese Academy of Sciences. These were intense and productive years. I worked with talented students and researchers on machine learning and natural language processing. Different academic cultures value different things, and adapting to those expectations 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 those important to you?

Competitions test whether ideas actually work. My teams contributed to 18 top-ranking results in major natural language processing challenges. One highlight was earning second place in the Amazon Alexa Prize Social Bot Challenge. That project forced us to think about long-term conversations, system robustness, and user experience. It showed clearly that accuracy alone is not enough. Real systems must be reliable, efficient, and engaging.

In recent years, your research has focused on efficiency and smaller models. Why does that matter?

Large language models are impressive, but they are expensive and resource-heavy. Many organisations cannot use them easily. I am interested in making models smaller and smarter so they can be deployed more widely. Efficiency is not about lowering standards. It is about better design. A well-built, smaller model can be more practical and trustworthy in real-world settings.

How do you personally define success in your field?

I measure success using two standards. One is my own judgement as a researcher. I understand the depth and impact of my work. The second is social feedback. If an idea is recognised and helps make the world better, then it matters. Decades ago, machine translation seemed unrealistic. Today, it is part of everyday communication. Being part of that long journey of turning the unreachable into something achievable is meaningful to me.

You place strong emphasis on values and integrity. Where does that come from?

Every career includes challenges that test your principles. I believe lasting success comes from staying aligned with one’s goals and social values, even when it can be difficult sometimes. Authenticity matters. It affects how one works with colleagues, mentors students, and chooses research problems. For me, success is not just about achievement. It is about contributing something that lasts beyond oneself.

What role does mentorship play in your work today?

Mentorship is at the heart of my work. I help students view research not as a series of immediate wins, but as a long-term journey where setbacks are stepping stones. Success is built through steady effort and curiosity. At the same time, I learn from my students, their questions, fresh perspectives, and fearless curiosity constantly push me to grow and evolve. For me, mentorship is a team journey of discovery, resilience, and shared growth.