Leming Zhou is an associate professor and director of the University of Pittsburgh Master of Science in Health Informatics (MSHI) program. Zhou has been at Pitt since 2008 and created foundational graduate courses that laid the foundation of the MSHI curriculum.
Read on to learn more about Zhou’s role as program director and how his research involvement has enriched the Pitt Health Information Management Department.
Program Director Role
In my role as director of the Master of Science in Health Informatics (HI) program, I oversee everything related to this program: from student enrollment to student graduation, course offering schedules, student support and course development.
As a program director, I always pay special attention to feedback from students and make adjustments to the courses offered in the program. We want our students in the program to have the best learning experiences.
When I joined SHRS in 2008, my initial focus was research. I have taken on more education-related responsibilities over the years because I realized that education is equally important as research, if not more important.
As a college educator, it is important to work on cutting-edge research alongside teaching. This ensures I stay current with the latest developments in the field and can adjust my teaching materials accordingly. Students therefore can obtain the latest information from my classes, which is essential in the fast-changing field of health informatics.
Over the years, I have created many successful HI courses from scratch and taught them in our programs, such as: Healthcare Analytics, Machine Learning and Data Visualization, and Data Science and Machine Learning in Health Science.
I am also developing new courses to meet the student needs and emerging industry demands, such as Generative AI in Healthcare, which will be offered in Spring 2027. I am currently teaching two undergraduate courses, Data Science in Health Informatics and Introduction to Statistics and Research Design.
Teaching Style
In my classes, I always have both theoretical foundations and practical examples. I want students to have a solid foundation in theory so that they know why we do certain things, and why certain approaches are more beneficial than others. At the same time, I teach them how to apply these theories into practical applications, for instance, choosing the right statistical methods to perform data analysis, conducting machine learning on a given data set and interpreting results properly.
The happiest part about teaching is that some graduates have come back and told me that they had a great career, and what they learned in this program directly helped them to achieve their current career position.
Finding Health Informatics
My background is computer science and my PhD dissertation focused on understanding DNA sequences using machine learning approaches. This specific research field is under the umbrella of bioinformatics, which is one of the fields in health informatics. So, it is quite natural that I joined the Pitt Health Informatics program after my graduation.
In the health informatics field, we can extract information from massive data sets and use the information to guide clinical decision making. We have also created multiple mobile health apps for people with chronic diseases and disabilities so that they can have more independence in terms of their own health management. Our research results can directly impact the quality of health care and accessibility of health services.
What Sets Pitt HI Apart
Pitt Health Informatics is a leading program in the nation. Our faculty are world-renowned researchers. They are leaders in their specific research fields. When they teach, they share their deep knowledge in the field using a highly accessible approach with students. They have a clear idea about the trend of the field and will make students well prepared for their future careers.
Research Interests
My current research is mainly on two areas: digital health and machine learning. Research in these areas can help patients as they can directly use mobile health apps we created to manage their own health. The data analysis and machine learning results can inform providers’ decision making. The ultimate goal is to achieve high quality, low cost and highly accessible health care.
Students are always welcome to contact me to find out research opportunities in my lab.
In my lab, we built methods to make personalized medicine feasible by identifying the best treatment options for individual patients. We also created mobile health apps to help patients access the latest information for their health care needs. In addition, we develop information security and privacy protection methods so that patients can confidently use digital health services, such as telehealth, mobile health and chatbots, without concerns about security or privacy.
Collaboration with the NIH Low Back Pain Research Grant
The general goal of the project is to identify the best treatment for each patient with chronic low back pain. For that purpose, researchers in the team worked with more than 1,000 patients with chronic low back pain and collected many different types of data, such as their electronic health records, genomic and proteomic data, wearable sensor data, images from X-rays and their performance in a variety of clinical tests.
The next challenge is to determine the best treatment for each patient from these data sets, which are dramatically different from each other. That is where I come in. In this project, I use my skills and experiences in large-scale data analysis and machine learning to analyze and understand these data sets. I work closely with other researchers to interpret the results from my analysis.
The findings have been presented at national conferences and published in high-impact journals, contributing to the broader research community’s understanding of personalized treatment for chronic low back pain.
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Interdisciplinary Pitt Team Awarded $20M Grant to Continue Low Back Pain Research
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