Bringing Science Fiction to Reality with Natural Language Processing
For the first in a series of professor and student spotlights in the Department of Physics, Computer Science, and Engineering, I interviewed Dr. Samuel Henry. Dr. Henry has been a professor of computer science at CNU since 2020 and we are so happy to have him as a recent addition to the PCSE team.
A Richmond native, Dr. Henry always enjoyed coding, even spending his math classes programming his TI-83 calculator. This led him to pursue an undergraduate degree and career in computer science, but he soon found himself missing academia and wishing to work on his own research. For this reason, he returned to school to get a PhD in computer science from VCU. In 2020, Dr. Henry joined CNU as a professor of computer science, and he enjoys working at a school where he can focus on both education and research, while still being close to his hometown.
Dr. Henry’s current project aims to integrate biomedical knowledge bases into natural language processing systems that were originally meant for general language. The field of biomedicine creates a particularly exciting challenge for natural language processing due to the domain specific languages, chemical names, drugs, and various medical jargon. Ideally, the work being done in machine learning, AI, and natural language processing will eventually bring technology to a level where someone can communicate with an intelligent computer that is capable of understanding our language. This bridging of reality and science fiction is one of the many things that drew Dr. Henry to the field of natural language processing.
For any students looking to study natural language processing, machine learning, or AI, Dr. Henry recommends starting with Python, since that is the most commonly used language in this domain. Some classes offered at CNU that will be useful for pursuing natural language processing include DATA201 and 301- since machine learning is a subfield of data science- and CPSC471, an artificial intelligence class. Dr. Henry hopes to see more machine learning undergraduate classes offered in the future.