The following article is the first installment in our Day in the Life series, featuring Microsoft researchers. From the big problems they’re tackling to the emerging AI trends shaping their work, this series offers an inside look at the people building the systems that are becoming part of our daily lives.

What does a typical workday look like for you—from how you start your day to how you wrap up?

• 6am: reading research papers, and deep focused thinking

• 8am: online meetings with colleagues in the US time zone

• 12pm: workout

• 2pm: meetings and collaboration with colleagues in Taipei

• 5pm: online meetings with colleagues in the European time zone

With your current research focus, what major problems are you trying to solve? Why is this work meaningful or exciting to you? 

AI runs on trust. I lead a research team pioneering methods to ensure the authenticity of live users, which is critical now that AI touches nearly every part of our work and personal lives. If AI can’t tell whether it’s interacting with a real person in real time, its power risks doing more harm than good.

Face recognition, liveness, and anti-spoofing are at the heart of this. Recognition alone isn’t enough: printed photos, video replays, or even AI-generated faces can potentially trick a system. As spoofing and Generative AI race ahead, we push detection and trust validation to advance even faster. Our focus is the essence of live personhood and machine–person agency: ensuring AI knows not just who it interacts with, but that the interaction is real, current, and trustworthy.

And yes, our work area is scattered with spoofing props that double as Halloween material. With such rich material for both work and play, how could research in AI and trust be anything but exciting?

What’s something you wish more people understood about working in AI research?

Developing a better AI model is only a small part of the work. In AI research, true success comes from clearly understanding the use scenario, designing the process pipeline, preparing high-quality data, and aligning with human values. That’s what allows a new model to truly shine. As Microsoft CEO Satya Nadella has noted, AI’s success must be measured in its impact, which encompasses all these concepts in addition to the AI model itself. When everything beyond model training is done well, the success of an AI system becomes almost inevitable.

What emerging AI trends are you most curious about, and why? 

I’m most curious about neuro-symbolic AI—approaches that could dramatically reduce the data and compute demands of today’s deep learning models. If successful, they might capture intelligence in a form as elegant and profound as Einstein’s E = mc², which I believe is one of the most beautiful equations in the universe.

How do you show up for others in your work?

I strive to spark curiosity wherever I can: sharing how AI is built and applied and helping students prepare to thrive in the new AI era. I am also passionate about uplifting girls and women in tech, and about offering parents a transparent perspective, so they can cut through the noise and step into the future with confidence.

Who or what has influenced your thinking the most in your research journey? 

Albert Einstein influenced me most in my research journey with his maxim: "If you can’t explain it simply, you don’t understand it well enough." NVIDIA CEO Jensen Huang brings this to life through his whiteboard practice, sketching chips, systems, and strategies in real time. By reducing complexity to boxes and arrows, he distills ideas to their essence, making them clear to anyone in the room. For Huang, as for Einstein, drawing is both a test of clarity and a proof of understanding, serving as the bedrock of true innovation.

What advice would you give to someone curious about working in AI research? 

Stay true to your curious heart. Research is full of failures before success, and it’s curiosity and passion that carry you through—helping you notice small things like data discrepancies, misalignments, differences in representation, or subtle shifts in order that can spark big breakthroughs. In the end, great AI research is like great art: its impact comes from passion for both the subject (domain) and the craft (algorithms).

How do you stay inspired or recharge during the day?  

As I move through the day: keeping up with product developments, planning next steps, working with colleagues and coordinating, understanding customer needs, and mentoring team members, it can get exhausting. But to most people’s surprise, I don’t recharge with a coffee break or a good meal. I recharge by quietly reading research papers without distraction. Discovering a fresh idea makes my eyes light up and gives me new energy.