Title: Archibald C. and Frances Fulk Rufty Research Professor of Law, Associate Dean of Information Services, and Director of J. Michael Goodson Law Library
Library/Department: Duke Law School
Location: 210 Science Drive, Durham Campus
Years at Duke: A bit over 1.5 years
In a nutshell, what do you do at Duke University Libraries?
I am the Director of the Goodson Law Library. My team and I work closely with colleagues at the Duke University Libraries and other independent libraries on campus on many fronts, from access, reference, data, and research services to library management system, collection development, archive, and scholarly communication.
If you had to pick one thing, what’s the best part of your job so far?
People and their wisdom. I cannot express enough how fortunate I feel to have joined the Goodson Law Library crew. They are approachable, intelligent, diligent, knowledgeable, and open-minded in many aspects! I know this is true with our colleagues at other libraries on campus too, and I am eager to learn more from everyone!
What the best thing you’ve read/watched/listened to recently?
This is an interesting time to be asked this question, because I am so drawn into natural language processing at the moment. I have been watching Stanford’s CS224U (natural language understanding) course online, where I have learned so much about NLP. For someone with no prior training in data science, or computer science, I found this course to be highly engaging, easy to follow, and fun. It introduces me to a wide range of tools and conceptual frameworks that I could use for analyzing legal texts, examining lawmaking and rulemaking process, and evaluating legal research databases and search engines.
Without a doubt, we are rapidly entering the “data-driven” age. It may seem paradoxical, but what is absolutely indispensable at the current stage is subject-specific knowledge and expert reasoning. To me, this means now is a crucial time for everyone, especially non-data and computer science domain experts, librarians, and researchers, to comprehend the architecture and different models and theories that underpin machine learning and computational research methodologies, to explore how we can integrate our domain expertise into the process, and more importantly, to investigate how we may benefit from all the machine learning-driven research and development to further refine our own domain knowledge and expertise.
What do you like most about living in The Triangle?
Weather and nature.
What do you like to do outside of work?
Binge-watching TV shows, shopping, and eating (but not cooking).
