Hello!
My name is Shion Guha and I am an assistant professor at the Department of Mathematics, Statistics and Computer Science at Marquette University. Previously, I was at the Department of Information Science at Cornell University. I am also part of the Cognitive Science program at Marquette University.
Broadly, my work cuts across the fields of human computer interaction, computational social science, social network analysis, data science and privacy.
I apply a critical, socio-technical lens to all my work and often collaborate with social scientists and other folks who are not computer scientists. This enables me to combine inductive and deductive approaches to doing research.
I joined Marquette because of a particular philosophy:
- I strongly believe that computer scientists should have a rigorous liberal arts background. Marquette’s Jesuit history fused with a very unique common core accomplishes this for every student. I get to contribute to this by creating my own cultivar of our data science major.
- Our graduate program in computational sciences is a one-of-a-kind doctoral degree that enables students to model, analyze and build systems from complex datasets by merging mathematics, statistics and computer science. This is an excellent, technically rigorous sandbox for training and working with the best computational social scientists that the next generation has to offer.
Currently, I am interested in the following broad (and often intersecting) themes in my research:
- the networked dynamics of privacy aspects: There are many different aspects of privacy in social networks e.g. surveillance, deception, non-use, impression management etc. How can we examine and explain their dynamics through a network scientific lens?
- human centered data science: We all do data analysis in different ways everyday. Algorithms and data increasingly govern many facets of our daily lives. How do we (re) imagine humans in the algorithm-data loop? What does this look like in practice for data science methodologies? Is there a way in which we can do data analysis better and more transparent ?
- privacy aspects in non-WEIRD contexts: There have been many theories of privacy developed in WEIRD (Western Educated Industrialized Rich Democratic) contexts over the years. How do we think about theorizing and empirically probing theories of privacy and its variegated aspects in non-WEIRD contexts with respect to computing technologies?
I am looking for students at Marquette (BS, MS, PhD) to work with me. If you are interested, please read this and get in touch. This is my cv, my github repos and my infrequently updated, not always academic, blog.
If you want to get in touch please feel free to email me at shion [dot] guha [at] marquette [dot] edu.
