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?
To me, these are simply milestones in my career as opposed to targets I pursue. They came as a result of the critical work I have done, both in research and in service to these research communities. They also allow me to develop close interactions and collaborations with experts in diverse research communities. I think this is the biggest benefit of these honors. They also allow me to focus on different research topics or agendas at different stages of my career. For example, I’m more focused on applying AI to medicine and healthcare in recent years. I think now is the time for AI to make the most impact to improve human health as we face so many challenges at an unprecedented scale, in mental or behavioral diseases, aging-related diseases, and infectious diseases, while at the same time, both AI and medical imaging technologies are making rapid advances.
Let me use COVID as an example of a complex real-world problem since it is in our recent memory. I chose to focus on what social media and machine learning can inform us. The COVID-19 pandemic severely affected people’s daily lives and caused tremendous economic losses worldwide. However, its influence on public opinions and people’s mental health conditions had not received as much attention at that time. In addition, the related literature in these fields primarily relied on interviews or surveys, largely limited to small-scale observations. In contrast, the rise of social media provides an opportunity to study many aspects of a pandemic at scale and in real-time. Meanwhile, the recent advances in machine learning and data mining allow us to perform automated data processing and analysis. We conducted a series of studies ranging from 1) understanding how college students respond differently than the general public to the pandemic, 2) monitoring depression trends throughout COVID-19, 3) analyzing different segments of people’s response to work from home, 4) understanding vacaccine uptake and hesitancy across demopgraphic groups, to 5) studying consumer hoarding behaviors during the pandemic. These findings can inform policymakers, organizations, and the public for a better response to ongoing and future events.
SMARTeeth is a project that I have been working on with colleagues in the University of Rochester Eastman Institute of Oral Health for the past 5 years. It was funded by NIH and NSF grants and involves the development and testing of a community-serving infrastructure that combines the use of artificial intelligence technology via smartphones with community engagement through interactive oral health community centers, mobile vans, and community health workers. The primary goals are to create supportive environments outside the traditional dental care setting, empower community self-care by reorienting health services from curative to preventive, and use AI technology to achieve population-wide dental screening and early detection, ultimately reducing the severity of tooth decay and dental disease-related emergencies.
As Editor-in-Chief of the IEEE Transactions on Multimedia from 2020 to 2022, what were your key initiatives, and how did you shape the journal’s direction during your tenure?
During my 3-year term, which coincided with the historic COVID-19 pandemic, my main initiatives included 1) reducing the review cycle, 2) reducing the workload for editors and reviewers in the face of mounting submissions, 3) promoting new research directions in the diverse field of multimedia computing, and 4) raising the impact of the journal.
Despite all the challenges, T-MM saw the biggest growth to that time by every measure, including the number of submissions (tripling over 3 years) and impact factor, since its inception in 1999.
One main goal I set personally at the beginning of my term was to significantly improve the reviewing time of papers. I implemented several new measures, including more rigorous desk screening by the EiC, quarterly recognition of Outstanding Reviewers, and annual recognition of Outstanding Associate Editors. With the tremendous effort by an enlarged editorial board of capable, responsive, and diverse Associate Editors, the first-round decision time was reduced from ∼18 weeks to ∼15 weeks halfway through my 3-year term, on pace to achieve the target of 13 weeks (3 months) I set at the beginning. Despite the explosive growth in the number of submissions, to the tune of 40% annually in the last two years, the improved first-round decision time was maintained at ∼15 weeks at the end of my term, placing T-MM much better than the average among competing IEEE Transactions.
With the help of the T-MM staff and Associate Editors, we also took a more vigilant stance against the increasing and evolving academic misconduct, including double submissions and plagiarism. Overall, the journal steadily reduced the acceptance rate and became more and more selective.
I also tried to steer T-MM away from beaten-down topics by declining to reconsider most of the resubmitted papers that other journals had rejected on those topics. Such papers strained the limited resources of T-MM and prevented papers on emerging topics from getting processed in a timely fashion.
I was very pleased to leave T-MM in a stronger state for the next EiC.
Dr. Jiebo Luo has been given the 2025 Edward J. McCluskey Technical Achievement Award has been to honor his sustained contributions to computer vision and multimedia computing technologies.
