An Interview with Dr. Jiebo Luo – 2025 2025 Edward J. McCluskey Technical Achievement Award Recipient
Dr. Jiebo Luo, the Albert Arendt Hopeman Professor of Engineering and Professor of Computer Science at the University of Rochester, is a visionary in computer vision, machine learning, and computational social science whose groundbreaking work has spanned over 600 publications, 90 patents, and numerous prestigious awards across academia and industry.
Your research spans computer vision, natural language processing, machine learning, and computational social science. How do you integrate these diverse fields to address complex real-world problems?
Let me offer a simple answer here, as I have much to talk about other questions. I normally do not tackle a problem that is out of my expertise. Given my research background in multiple fundamental subfields of AI, including computer vision, natural language processing, and machine learning, as well as data science, I consciously chose a few big-picture problems in the real world, including understanding human behaviors during the COVID-19 pandemic and improving dental care for all, that can take advantage of my expertise. I will talk more about these two studies in a later question.
Having authored over 600 technical papers and over 90 U.S. patents, what drives your prolific output, and how do you maintain innovation across such a broad spectrum of topics?
For me, the biggest driver of my research is curiosity and the passion to solve real-world problems by applying AI technologies. Looking back, this is likely most influenced by my R&D roots in the industry. Seeing my research making something happen in the real world gives me extra gratification from my research and motivation to do more.
While it is indeed challenging to maintain innovation across broad topics, I found it effective to compartmentalize multiple tasks and focus on a small number of them at a given time. Another useful strategy is to repurpose techniques developed for one problem for other problems, thus shortening the research cycles. After all, AI is intended to be reusable, even though that has been hard to achieve.
During your 15-year tenure at Kodak Research Laboratories, you contributed significantly to digital imaging. How did this industry experience influence your subsequent academic research?
I love to extract research questions from practical problems. First, this guarantees the relevance of the research because it is not contrived or out of the blue. Second, this also raises the value of the research because I already know where to apply the findings of the research once the research problems are solved. Consequently, I have maintained healthy collaborations with a good number of companies since I started my academic career, ranging from the big companies such as Google, Meta, Microsoft, and Amazon, to mid-size companies such as Adobe, and small companies in New York State. That also helped my students get job offers from these companies, which is always good.
You’ve been recognized as a Fellow by multiple prestigious organizations, including ACM, IEEE, AAAI, SPIE, and IAPR. How have these honors impacted your career and research focus?
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