An MS-ADS Instructor’s Perspective on AI Innovation in China

Artificial intelligence is advancing at a remarkable pace. For faculty who teach the next generation of data scientists, keeping up with that pace requires more than reading papers or attending conferences. It means seeing how innovation unfolds where it is happening.

That was the goal when MS in Applied Data Science Professor Utku Pamuksuz joined a week-long AI and robotics innovation tour across China this spring. Traveling through Shanghai, Suzhou, Nanjing, and Beijing, he met with researchers, startup founders, investors, engineers, and technology leaders working at the forefront of artificial intelligence.

Pamuksuz visited innovation districts, AI companies, research institutes, and technology organizations exploring everything from industrial AI to indoor service robots.

A stop that stood out was Shanghai’s Jing’an innovation district, where companies, researchers, and public institutions operate in close proximity, an environment that reinforced the value of collaboration across academia and industry.

One recurring theme emerged throughout the week. Looking back on the trip, Pamuksuz says, “AI was discussed not only as a modeling or software capability, but as part of larger systems involving robotics, automation, digital twins, simulation, sensors, manufacturing software, and domain-specific workflows.” He described it as “a helpful reminder that applied AI often succeeds through integration.”

“The model matters,” he adds, “but so do the data pipelines, user interfaces, human workflows, evaluation methods, deployment environments, and governance structures around it.

Another observation centered on AI’s growing presence in the physical world. Throughout the tour, Pamuksuz encountered examples of physical AI and embodied intelligence. He saw AR translation glasses, holographic interfaces, AI-assisted radiology tools, and humanoid robots. He notes, “AI changes when it moves from a browser, app, or API into the physical world.” Those more physical technologies introduce new considerations, including safety, hardware costs, sensors, and real-world interaction data, that are becoming increasingly important as AI is deployed across healthcare, manufacturing, aviation, and infrastructure.

Together, those experiences reinforced something also emphasized in the program, which is that understanding machine learning algorithms is only one part of becoming an effective data scientist. Students also need to understand how AI systems are designed, deployed, monitored, and integrated into real organizations.

The trip also offered an opportunity to reconnect with the University of Chicago community abroad.

While in Shanghai, Pamuksuz caught up with current MS-ADS online student Joel Gallo, who lives and works in China, to hear about his experiences in the program and how he’s applying what he’s learned professionally. He also joined several MS-ADS alumni for dinner, which highlighted that the MS-ADS community extends well beyond the classroom, connecting students and alumni across industries around the world. 

While the technologies themselves were impressive, Pamuksuz said the greatest value came from the conversations they sparked. “The best part was not the robots, VLA models, agentic OPC or world models themselves, but the conversations, hearing different perspectives on where AI is taking us and what comes next.”

As someone who works at the intersection of academia and industry, those conversations prompted Pamuksuz to think less about individual technologies and more about how universities can prepare students for a rapidly evolving field.

That perspective comes from engaging with AI beyond a single classroom or ecosystem. “Seeing how AI is discussed and applied in different global settings can help us avoid teaching from only one perspective,” he said. “Faculty exposure to global AI environments can help us teach with more empathy and awareness.”

As AI continues to evolve, the technologies students use today will inevitably change. What remains constant is the need to think critically, understand how AI fits within larger systems, and apply those technologies responsibly to solve meaningful problems. For Pamuksuz, experiences like this are one way to ensure the classroom evolves alongside the technology.