Kesong Cao is a graduating senior (4th year undergrad) at UW-Madison. He enjoys coding and problem-solving in general. He hopes to become a computational linguist that can do great research and/or engineering.
His broad research interests are languages and human behaviors.
At UW-Madison, he has gained rewarding experience in building web apps, tackling research questions, TAing an Operating Systems class, and constantly managing time and expectations. He takes courses all over the place in Comp Sci, Econ, Math, Stats, Psychology, Political Science, History, and even Dance and Accounting.
Kesong is seeking full-time employment in or after December 2020 as a researcher or software developer. He plans to apply for PhD programs in Computer Science starting Fall 2021.
B.A. in Computer Science and Economics, (Expected) December 2020
University of Wisconsin-Madison
Modeling, data analysis, presenting…
English, Chinese, some Japanese
The verbal fluency task—listing words from a category or words that begin with a specific letter—is a common experimental paradigm that is used to diagnose memory impairments and to understand how we store and retrieve knowledge. Data from the verbal fluency task are analyzed in many different ways, often requiring manual coding that is time intensive and error-prone. Researchers have also used fluency data from groups or individuals to estimate semantic networks—latent representations of semantic memory that describe the relations between concepts—that further our understanding of how knowledge is encoded. However computational methods used to estimate networks are not standardized and can be difficult to implement, which has hindered widespread adoption. We present SNAFU: the Semantic Network and Fluency Utility, a tool for estimating networks from fluency data and automatizing traditional fluency analyses, including counting cluster switches and cluster sizes, intrusions, perseverations, and word frequencies. In this manuscript, we provide a primer on using the tool, illustrate its application by creating a semantic network for foods, and validate the tool by comparing results to trained human coders using multiple datasets.