6 minute read
QRM Professor Jeong Hoon Jang's Vision for the Future of Risk Management and Data Integration
Can you please introduce yourself to our readers?
Hello, I’m Jeong Hoon Jang. I am a new incoming assistant professor affiliated with the Quantitative Risk Management (QRM) major at the Humanities, Arts, and Social Sciences (HASS) department of Underwood International College.
I was born and raised in South Korea, and my academic journey began at Daewon Foreign Language High School. After completing high school, I moved to the United States to pursue higher education at New York University. Initially, I was inclined towards studying finance, but gradually, I found my interest veering towards mathematical models and data handling. In the end, I decided to major in mathematics, statistics, and operations management.
During my undergraduate studies, my advisor suggested I pursue a Ph.D in statistics, which was growing at that time due to the increasing reliance on data in various fields. I took his advice and enrolled in a statistics Ph.D program at Emory University. During the five years I spent there, I conducted research in the field of statistics, and this experience further honed my skills and knowledge on the subject.
After graduating from Emory University in 2019, I was offered a position as an assistant professor at Indiana University School of Medicine. This teaching opportunity allowed me to venture into the field of health studies and medicine while leveraging my expertise in statistics. Here I worked in the Department of Biostatistics, and I had the chance to work with a variety of medical data generated from hospitals, including image and electronic health data, which was a rewarding experience. After working there for about two and a half years, I decided to return to my homeland, South Korea, and joined Yonsei UIC as an assistant professor in the QRM program, where I have been working for the past year and a half.
Can you tell us what you are currently working on in terms of your research?
In statistics, my research focuses on methodological and collaborative aspects. In methodological research, I specialize in functional data analysis, a statistical field that addresses the challenge of analyzing data represented as functions, like EEG or medical image data, within finite samples. The goal is to develop efficient methods for drawing meaningful conclusions from these infinite-dimensional functions. Collaborative research involves working across diverse fields, such as infectious diseases and health policies, and applying statistical methods to various real-world problems. My work at Indiana University centered on collaboration in infectious diseases, health policies, and rehabilitation. While at Yonsei, I concentrate on data analysis for public health. My publications mainly tackle complex data sets and contribute to statistical methods, particularly in the area of functional data analysis. This interdisciplinary approach extends to collaborative publications in medical fields, ranging from infectious diseases to kidney disease, and illustrates the impact of statistical methodologies on diverse health challenges.
What do you think is unique about your research?
I often deal with a plethora of complex data which combines electronic health records and medical image data from the same patients. Unlike traditional analyses focusing on one data type, my approach integrates data from multiple sources for meaningful interpretations. In my methodological work, I develop techniques, such as the “hybrid principal component analysis,” a unique dimension reduction method that simultaneously handles image data, multivariate data, and tensor data. This allows for a holistic analysis of heterogeneous data. As I continue to explore and expand, I want to improve the integration of data from various modes and formulate methods that effortlessly handle the complexity of diverse data forms for a more comprehensive understanding.
What are the real world implications of your research and how can you incorporate the principles of your research into your teaching?
When collaborating with medical professionals, they prefer data that can be represented in simpler forms, but this becomes challenging with heterogeneous and high-dimensional data. My developed methods handle this issue by reducing dimensionality and enables medical professionals to visually understand disease classifications, which is vital for quick patient group identification crucial in scenarios where rapid decisions impact outcomes significantly. Currently, my focus in teaching is on regression analysis, specifically the foundational tools for regression performance. In the realm of functional data analysis, our methods are extensions of traditional approaches. They serve as the base for extending methods into more complex scenarios. When working with complex data objects, such as functional data, it’s important to have a strong understanding of traditional tools.
For QRM majors, I recognize the evolving industry landscape where professionals encounter not only traditional data but also highly dimensional and diverse datasets. For example, in industries like insurance, data spans various dimensions and working with such data requires a strong foundation. Therefore, my goal is to teach my students step by step, starting with traditional data objects and methods and then gradually introducing methods that can handle complex data types, to ensure they have a comprehensive toolkit for real-world applications.
As our first full-time QRM faculty member, how do you find the program so far and how do you plan on building up the program?
I believe that my students possess significant potential to become adept quantitative risk managers. I would say four skills are key to being an effective quantitative risk manager. Firstly, students need coding proficiency for effective data handling. Secondly, a solid foundation in mathematics is essential to grasp the methodologies required for working with data. Thirdly, communication skills are vital, as translating data analyses into understandable insights for collaborators, such as clients, is integral. Lastly, a good understanding of the specific fields being worked in, whether economics, finance, or public health, is necessary.
My vision is to design the curriculum for the major in a way that allows students to excel in these four areas. I’ve taught a variety of courses and firmly believe that, with the right resources and additional professors, we can introduce more contemporary and challenging courses. Using this approach, we would focus on developing the essential skills required for students to become proficient quantitative risk managers ready to navigate the complexities of the real world. It’s necessary for students to understand the multidimensional nature of risk, especially in the context of climate change and natural disasters. Real-world events greatly highlight the need for statistical and quantitative analysis to model and address uncertainties effectively.
I feel the study of QRM will become more and more popular as time goes on. As I mentioned, the world is filled with uncertainties across various sectors such as business, insurance, finance, marketing, engineering, and physics. The current trend in modern statistics and science is geared towards systematically analyzing and predicting these uncertainties. Given the increasing demand in multiple sectors for professionals who can manage risks, the major is just going to become more and more popular.
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Interview Conducted by: Professor Howard KahmEdited by: Chaeeun Kim (IS '21)Spread Design by: Pho Vu (IID '20.5)
