Jia Xu is a computer scientist and AI researcher with a global academic career spanning Europe, Asia, and the United States. Currently his work is focused on natural language processing and examples of large languages.
Xu started his study tour in Germany. He completed his bachelor’s and master’s degrees at TU Berlin, studying and working entirely in German. He later received his PhD at RWTH Aachen University under Professor Hermann Ney, a pioneer in machine translation. During this time, he also completed research visits to Microsoft Research and IBM Watson, gaining early exposure to industrial-scale AI applications.
His academic work continued in Asia. Xu worked as an Assistant Professor and PhD advisor at Tsinghua University and later became an Associate Professor at the Chinese Academy of Sciences. In all of these roles, he led research teams working on conversational programming, machine learning, and active AI models.
Jia Xu is known for combining theory with real-world application. He has authored about 50 research papers and holds 12 patents and provisional patents. His 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 stable. He believes that true success in AI comes from lasting impact, not scale alone.
A Conversation with Jia Xu About Building a Global Career in AI
Your work has taken you to Europe, Asia, and the United States. Where did it all begin?
I started my study journey in Germany when I was nineteen. I moved there to study computer science and had to learn 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 the way I deal with challenges. I learned early on 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, the basics speak.. The basics lead. Listening sharpens. The preparation goes deep. That idea stayed with me during my PhD at RWTH Aachen University, where I worked under Professor Hermann Ney on machine translation. At that time, machine translation was still considered very difficult. Seeing how long-term research can slowly transform impossible ideas into real plans left a powerful impression on me.
He also spent time in industry research labs. What does that experience add?
During my PhD, I had research visits to Microsoft Research Redmond and IBM Watson. Those places showed me how research works at scale. I am grateful for that time and my mentors and colleagues. Industry labs care deeply about whether ideas can work in real applications. That balance between theory and practice stayed with me. It reinforced my belief that rigorous research should ultimately be connected to real use cases.
After your PhD, you moved into academic leadership roles in Asia. What stood out during that phase?
I worked as an Assistant Professor and PhD advisor at Tsinghua University and later became an Associate Professor at the Chinese Academy of Sciences. These were strong and productive years. I have worked with talented students and researchers in machine learning and natural language processing. Different educational cultures value different things, and adapting to those expectations helped me grow as a leader. I learned that thinking is 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 have contributed 18 top results to the grand challenges of natural language processing. Another highlight was getting second place in the Amazon Alexa Prize Social Bot Challenge. That project forced us to think about long-term conversations, system stability, and user experience. It clearly showed that accuracy alone is not enough. Real systems must be reliable, efficient, and interactive.
In recent years, your research has focused on efficiency with small models. What does that matter?
Large language forms are amazing, but they are expensive and very difficult. Most organizations cannot easily implement it. I like to make models smaller and smarter for wider use. Efficiency is not about lowering standards. It’s about better design. A well-built, compact model can be very useful and reliable in real-world settings.
How do you define success in your field?
I measure success using two standards. Some of my judgment as a researcher. I understand the depth and impact of my work. The second is the public response. If an idea is recognized and helps make the world a better place, then it is important. Decades ago, machine translation was seen as a no-brainer. Today, it is part of everyday communication. Being a part of that long journey of turning the unattainable into something achievable makes sense to me.
He puts a lot of emphasis on values and integrity. Where does that come from?
Every job includes challenges that test your principles. I believe that lasting success is achieved by staying true to one’s goals and societal values, even when it may be difficult at times. Authenticity is important. It affects how one works with colleagues, advises students, and selects research problems. For me, success is not just about achieving. It’s about giving something beyond yourself.
What role does teaching play in your work today?
Mentorship is at the heart of my work. I help students look at research not as a series of quick wins, but as a long-term journey where obstacles are stepping stones. Success is built on hard work and curiosity. At the same time, I learn from my students, their questions, new ideas, and fearless curiosity always push me to grow and evolve. To me, training is a team journey to gain knowledge, become stronger, and share growth.
