In fall 2016, College of Computing & Informatics’ (CCI) first-year information science doctoral student Wei Quan earned a highly competitive internship at National Public Radio (NPR) Labs - the nation's only not-for-profit broadcast technology research and development center. Quan planned to utilize the opportunity to further his research interests in data analysis.
During his time as an intern at NPR, Quan shared more on his internship experience, his research interests, and advice for students who want to pursue similar opportunities.
CCI: Tell us a little about yourself - what are your research interests and how do you spend your time as an information studies doctoral student?
Wei Quan: Hi, I’m Wei. I’m Korean, but I was born and raised in China. I did my undergrad in information and computing science at Beijing University of Posts and Telecommunications before I studied library and information science at Syracuse University School of Information Studies. I’m now in the information science doctoral program at CCI trying to figure out how to tell a meaningful story with data. My research interests are data mining, text mining, information visualization, health informatics, as well as cross-culture communications. I just finished my first year at Drexel CCI, and all the core courses in the doctoral program. As I’m finishing up my internship at NPR, I’ll be back in the Winter Quarter and I plan to take some electives, do some independent study and prepare for my candidacy exam next summer.
CCI: Tell us more about your research interests. What inspired you to pursue a doctorate?
WQ: Many of my past experiences culminated in my decision to pursue a doctoral degree. It all began with the multicultural environment I grew up. As a Korean-Chinese, multi-language translation fascinated me. I always noticed and tried to understand the difficulties of interpretations between different languages. I used to think how it could be possible to interpret languages by machines when it is very hard to make the context precisely and clearly even by human, which became a motivator for me to study information technologies. As I grew up, I discovered the power of computational linguistics and natural language processing in improving the performances of machine translations. I wanted to understand how to apply computational methods to help people use language, and discover the knowledge hidden in the language. My passion kept growing when I started my master’s program. Finally, I decided to do research in these areas with the ultimate goal to help connect people with information.
When I came to Drexel, I got involved in several projects. Information science is rather interdisciplinary. I’ve been working on some projects because I’m originally interested in cross-culture communications or text analysis, such as investigating paralinguistic features used to self-present on Twitter across different languages and cultural boundaries, or detecting and tracking topics in proceedings of an international conference. But they’re also overlapping with other domains like linguistics. Then, I expanded my research interests and started to work on some other projects such as analyzing online learning discussion to quantitatively identify posting behaviors, reused themes, etc. Another project of mine is analyzing focus group data elicited from home health admission nurses related to tasks performed, decisions made and information needed during admission. Now, whenever I tell people about my research interests, I say they are basically data mining, text mining, information visualization, health informatics and cross-culture communications.
CCI: What are your job responsibilities at your current position? How do you balance your work and academic life?
WQ: I’m currently working on the mapping project at NPR Labs, which is the nation's only not-for-profit broadcast technology research and development center. We use state-of-the-art signal propagation tools along with a highly flexible geographic information system to simulate radio station signal coverage, and make interactive maps. I took this term off so I can work full-time here at NPR. But I do spare some time on the weekends to work on some ongoing research projects if necessary. For example, the first few weeks at NPR, I was busy finishing up a conference paper with collaborators from CCI.
CCI: What sparked your interest in working at NPR? How does your internship at NPR apply to your studies in information studies or your research interests?
WQ: First, I’m a big fan of radio. I’m so curious about how things work in the organization. And I toured NPR with a bunch of peers from Syracuse when we attended the Computers in Libraries conference in 2014 and 2015. We also had brown bag sessions with staff from the Research, Archive and Data Strategy Department at NPR. After that, I added NPR to my list of corporations/organizations that I’d like to work in. Also, as I said my research interests center around deriving insights from data, I have analyzed data in various forms to extract knowledge or insights, both in my master program and current research projects at Drexel. So I was very excited to learn of the opportunity to analyze data in NPR.
CCI: How did Drexel prepare you for your internship?
WQ: Researching in data science has broadened my knowledge and analytical skills, and has specifically introduced me to various programming languages and quantitative analysis tools. One of my core courses at Drexel in quantitative research methods expanded my research skills to carry out quantitative analysis for various problems in information systems, management of information resources, and scholarly and professional communication. This has increased my interest in data analysis with statistical techniques and how it helps to draw conclusion from the data. My teamwork and interpersonal skills have been developed from collaborating with fellow researchers and delivering research findings. Through these experiences I also learned the value of effective communication.
CCI: What are some of the best parts of your education at Drexel? At NPR?
WQ: I really like that we have both computer science and information science in one school here at Drexel, which also makes it easier for us to collaborate. And we can get more exposure to the technical side of the world. I have two favorite courses so far here at Drexel, one is the applied research methods, the other is the qualitative research. Both of them are doctoral core courses at CCI. I see myself more of a quantitative researcher than a qualitative one. Thus, it’s so interesting to learn about other research methods that are relevant.
I like working at the Labs comparing signal propagation models and making interactive maps. And my manager is really nice and knowledgeable, I can ask lots of questions. The work environment and the general culture of NPR is also very nice. You can reach out to people, network, and attend all of the free events NPR has to offer, which helps me feel a little more comfortable and integrated in the organization. One of the coolest parts of working at NPR is being able to watch Tiny Desk concerts live. What's even cooler is getting to know various artists and genres that I maybe wouldn't have found on my own. The #DearWashington project is another great part. This is a project launched by NPR on social media in September. The topic is #DearWashington, and the question is “What should the next president know about YOUR life?” I was one of the four interns who held up a paper/whiteboard and it kind of went viral on social media. NPR encourages cross department collaboration, which is really cool!
CCI: Where do you see yourself after you graduate?
WQ: I’m open to either academia or industry right now. I hope to contribute to interdisciplinary research in the field of information science, and I would like to integrate various techniques so that people can communicate more effectively and get information more efficiently. I believe my educational background, research experience and academic objectives coincide with the vision of the program and will lead to my success eventually.