Quick Q&A with Text Mining Research Group

Quick Q&A with Robert Leaman, Wanli Liu, and Sun Kim
Question	Robert Leaman, PhD	Wanli Liu, PhD	Sun Kim, PhD

What is the focus of your NLM research and why is it significant?	I research methods to extract information from text, such as PubMed abstracts, focusing on trainable computational models for locating and identifying biomedical terminology such as diseases, drugs, and genes. My work answers the “What?” of “Who, What, When, Where, Why and How?” Using a trainable computational model (machine learning) means that it can be adapted to different applications as needed.	I work primarily on the name project for PubMed authors and NIH-funded principal investigators. The project helps PubMed users search by name, which is the most frequent category of information in PubMed queries. While human names can be highly ambiguous, we apply advanced machine learning statistical methods to disambiguate similar author names. We have achieved and published state-of-the-art performance data and are working to make further improvements. I am also involved in other information retrieval projects—for example, MeSH term work with popular deep-learning techniques.	My research focus is on semantics. What is the meaning of a word? What is the relationship between words, phrases, or sentences in biomedical literature? These are fundamental questions in natural language processing because the journey to find the answers helps identify more relevant information in PubMed and PubMed Central (PMC) documents.
What or who inspired you to pursue your career?	I attended a computer programming camp in 4^th grade and was quickly hooked. I loved my undergraduate class in artificial intelligence. That was the first thing I explored when I returned for a graduate degree.	I was inspired by the overall working spirit of NCBI, especially, the leadership of David J. Lipman, MD, David Landsman, PhD, and those helping with my projects, John Wilbur, MD, PhD, and Zhiyong Lu, PhD. Their enthusiasm and professionalism encouraged me to pursue research in this field.	Friends, colleagues, and basically everything around me. When I entered college, computer science was not my first preference, but starting from there, it was natural to study machine learning and text mining.
How did you get started in your career?	My PhD advisor heard about a workshop (BioCreative) offering a shared task in identifying gene names. The task showed us that solving many information extraction problems depends on first being able to locate important terms. We decided not only to focus there but to release our systems open source so others could use our work directly.	Right before graduating from my PhD program, I heard of an open research position at NCBI. Since my PhD study is related to machine learning techniques, I can continue to work in this field and apply my related skills.	I was always a science loving guy, but I would say my first contact with Apple computers and basic programming in middle school opened my eyes at that time
What really gets you jazzed about science and research?	I enjoy creating new methods that enable solutions to problems not addressed before.	Machine learning and natural language processing are developing quickly and with broad applications. Recent developments, such as deep learning, are gaining popularity. I am excited to explore these latest techniques.	Because it is like a never-ending story. Something I did not know excites me.
If you weren’t doing this work, what other profession might you have pursued?	Before starting my PhD, I worked in industry as a software engineer automating the robotics used to build semiconductors.	I also trained as a computer architect. I would have designed computer chips with a focus on improving system performance.	Maybe astronomy? I enjoyed watching the sky and making a small telescope when I was about 10 years old.
Tell us something surprising about yourself.	I also enjoy cooking. Kitchen experiments have a faster turnaround time and the results are often delicious.	While others can workout when listening to music, I can read English news while listening to Chinese talk shows.	I should sleep a minimum of 8 hours every day.