Associate Professor of Technology, Operations, and Statistics
NYU Stern School of Business
Xi Chen of New York University’s Stern School of Business hit the sweet spot of what we were looking for on this recognition of the 40 best business school professors under 40 for 2021. He has more than 3,300 Google Scholar citations and received dozens of nominations on behalf of his teaching and mentoring prowess. Chen has received numerous research and teaching awards.
“My research is interdisciplinary, lying on the interface between machine learning, statistical learning, and operations research,” Chen says of his research. “My current research focuses on designing new machine learning and online learning algorithms to facilitate real-time intelligent decision-making in a wide range of businesses. Although machine learning has achieved great success in computer vision and natural language processing, many business applications involve online structured data and complex constraints such as budget, volatility, risk, and human behavior.”
We were happy and pleased to include Chen on this year’s 40 Under 40 list.
Current age: 34
At current institution since what year? Since 2014
Postdoc in Computer Science and Statistics, UC Berkeley, 2014
Ph.D. in Machine Learning, School of Computer Science, Carnegie Mellon University, 2013
M.Sc. in Operations Research, Tepper School of Business, Carnegie Mellon University, 2009
List of MBA courses you currently teach: Statistics and Data Analysis
TELL US ABOUT LIFE AS A BUSINESS SCHOOL PROFESSOR
I knew I wanted to be a business school professor when… I was about to graduate from Carnegie Mellon University in 2012. At that time, I realized machine learning should go beyond applications in computer vision, speech recognition, and natural language processing. Data science and machine learning technology will make a huge impact on business in the next decade, and I wanted to be part of those research efforts while teaching the next generation.
What are you currently researching and what is the most significant discovery you’ve made from it?
My research is interdisciplinary, lying on the interface between machine learning, statistical learning, and operations research. My current research focuses on designing new machine learning and online learning algorithms to facilitate real-time intelligent decision-making in a wide range of businesses. Although machine learning has achieved great success in computer vision and natural language processing, many business applications involve online structured data and complex constraints such as budget, volatility, risk, and human behavior. To deal with complicated online data arising from challenging business needs, I have been developing efficient machine learning and intelligent decision-making algorithms with consideration of complex resource constraints. The developed algorithms have been widely applied to essential business applications, with notable examples being crowdsourcing, dynamic pricing, and personalized recommendations.
In one of my recent papers, I addressed the challenge of privacy protection of customers in e-commerce. In particular, the prevalence of e-commerce has made customers’ detailed personal information readily accessible to retailers, and this information has been widely used in pricing decisions. When involving personalized information, how to protect the privacy of such information becomes a critical issue in practice. We first introduced the notion of anticipating differential privacy that is tailored to the dynamic pricing problem. We further proposed a personalized pricing policy that achieves both the privacy guarantee and the performance guarantee in revenue maximization.
If I weren’t a business school professor… I would probably be an entrepreneur, starting my own technology company.
What do you think makes you stand out as a professor?
I have a quite different background and training (as my Ph.D. is in machine learning) as compared to many other business school professors. My training in machine learning and data science greatly helps me incorporate machine learning technology to address business challenges and to the education of MBA students.
One word that describes my first time teaching: Excited
Here’s what I wish someone would’ve told me about being a business school professor: Teaching MBA students is not daunting but a very rewarding experience. A professor can learn a lot from their MBA and Executive MBA students.
Professor I most admire and why: My Ph.D. advisor Jaime Carbonell from CMU (who passed away last year) and my Postdoc advisor Michael I Jordan. They have great enthusiasm in advancing modern AI and are dedicated to the success of each student.
TEACHING MBA STUDENTS
What do you enjoy most about teaching business students?
Through teaching business students, I have learned many interesting new business models. I can work with students to see how data science could play an essential role in business problems such as operations management, finance, and accounting.
What is most challenging?
Finding the right examples and applications to motivate students balanced with explaining high-level intuitions and technical details.
In one word, describe your favorite type of student: Motivated
In one word, describe your least favorite type of student: Disengaged
When it comes to grading, I think students would describe me as… Thoughtful but fair
LIFE OUTSIDE OF THE CLASSROOM
What are your hobbies?
Reading, traveling with family, and chatting with friends.
How will you spend your summer?
Pre-pandemic, I usually go on vacation with my family. I am not sure about this summer and it will depend on the pandemic situation.
Favorite place(s) to vacation: Shanghai and Hong Kong
Favorite book(s): I like to read the Chinese martial arts and chivalry books written by Louis Cha Leung-yun.
What is currently your favorite movie and/or show and what is it about the film or program that you enjoy so much?
I like movies with inspiring characters and that tell stories embedded in history. A typical example would be “Forest Gump.” I have seen this movie easily more than half a dozen times.
What is your favorite type of music or artist(s) and why?
I like country music, which makes me feel relaxed.
THOUGHTS AND REFLECTIONS
If I had my way, the business school of the future would have much more of this… More emphasis on education in modern technology, e.g., artificial intelligence, machine learning, and deep learning, blockchain. These technologies will completely change the way business operates in the 21st century. Therefore, it is essential to prepare our future business leaders with a deep background and knowledge of these technologies. Stern has been at the forefront in some of these areas and it will be exciting to see this progress.
In my opinion, companies, and organizations today need to do a better job at… Leveraging data and machine learning to make intelligent and effective decisions.
I’m grateful for… Being a professor at NYU Stern School of Business. NYU Stern provides an excellent environment for interdisciplinary research in business and has a fantastic body of highly motivated students.
Faculty, students, alumni, and/or administrators say:
“I never would’ve imagined a statistics core professor could be my favorite professor, but Prof. Chen truly is! His energy is contagious and I particularly loved how he would constantly tie stats both to trading applications and his own research background in machine learning.”
“Uses several real life examples in his teaching of Statistics and Data Analytics. This helped me understand the concepts and allows me to apply them. For example, during the regression analysis portion of the course Professor Chen used NYC housing and restaurant data to explain how regression analysis is done and how it can be applied.”
“Concrete examples that relate to the real world made learning statistics super easy. He also focuses on letting the students drive the class with any questions, rather than worrying about getting through the material – one of the better professors I’ve had!”
“Professor Chen is a terrific stats professor and did a great job of implementing interesting real-world examples, such as the NYC housing market and restaurant data, into all of his classes. As someone interested in finance and public markets, I thoroughly enjoyed his connecting statistics to public equities.”
“My colleague Xi has devoted for high-impact research for many years since he joined Stern School of Business NYU. In the recent 5 years, Xi has received 2500 citation, which is much higher than the average in a business school. He has been an expert in data science and statistical machine learning.”