– jan 10-12: co-organizing a Mellon grant funded Spatial Data Analytics workshop on campus.
– dec 1: paper on parental-teen mobile regulation and surveillance accepted to chi’18.
– nov 10: featured in TMJ4-NBC on algorithmic failures.
I am an assistant professor at the Department of Mathematics, Statistics and Computer Science at Marquette University. I lead Data Science efforts and participate in the Cognitive Science program. Previously, I received a PhD from Cornell University under Steve Wicker and a MS from the Indian Statistical Institute under BS Daya Sagar.
Broadly, my research interests cut across human computer interaction, computational social science, ictd and privacy. I usually focus on marginalized and developing contexts in computing by applying a critical, socio-technical lens to all my work and often collaborate with social scientists and humanists.
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 very unique 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 next generation of computational social scientists.
Currently, I am interested in the following broad (and often intersecting) themes in my research:
- the networked dynamics of privacy: 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 algorithmic ethics: 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? Is there a way in which we can do data analysis better and more transparent ?
- privacy 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?
If you want to get in touch please feel free to email me at shion [dot] guha [at] marquette [dot] edu.