Yingyu Liang, a new faculty member in machine learning in the University of Wisconsin-Madison Department of Computer Sciences, enjoys bringing his disparate interests together. In his free time, he enjoys hobbies ranging from badminton to classical Chinese poetry.
Within computer science, he was drawn to machine learning because it pulls together so many things that fascinate him. “It is an intersection of different areas, including mathematics, statistics, theoretical computer science, systems, and also neuroscience and cognitive science. That’s why it’s so amazing!” he says. “And there are lots of new ideas coming out.”
After finishing his Ph.D. at Georgia Tech in 2014, Liang headed to Princeton. There, he did a postdoc and then spent two years as a research scholar and lecturer. He arrived in Madison with his wife and toddler son in fall 2017. In the machine learning area, Liang joins Jerry Zhu, who holds the Sheldon B. and Marianne S. Lubar Professorship.
Liang’s interest in machine learning includes both theoretical and applied approaches. As an undergrad at China’s Tsinghua University, he focused on theory. His master’s research was application-focused. Then, while earning his Ph.D., he combined the two, and he remains excited about their intersection.
Says Liang, “I’d like to provide rigorous foundations and performance guarantees for modern machine learning methods, especially deep learning, an approach that leads to breakthroughs in many applications such as recognizing objects in images, machine translation between different languages, and building systems that beat human champions in games like chess and Go.”
As he explains, even though there are many successful applications of machine learning already, there is little understanding about underlying theoretical principles. “My research aims at obtaining more insights into these methods, allowing more effective, reliable and robust machine learning systems for social good,” he says.
Liang is also eager to contribute to progress in natural language processing (NLP)—the part of computer science that deals with the interaction between computers and human languages. For example, NLP comes into play when you interact with a personal digital assistant like Siri, Alexa or Cortana on a smartphone or in-home device like Amazon Echo.
He wants to provide theoretical models and also build practical systems for NLP. “I’m interested in this direction because, first of all, it’s tightly connected to human intelligence. It’s also a very important direction now in industry,” he says, referencing the array of personal assistants now on the market. “It’s important you can communicate with that system with natural language, either by speaking or typing.”
In the classroom, Liang strives to give undergraduates a solid foundation in CS concepts, especially so they can stay abreast of the frontiers of the research community. For graduate students, he hopes to nurture their individual research directions through recommended reading, potential project topics and more. As they find their footing as researchers in their own right, he wants to guide them.
A good professor should have “an ability to propose far-reaching research directions, and an openness to different kinds of approaches,” he says.