I advance mathematical and computational social science. My research spans data science, machine learning, simulation, operations research, network science, and the development of new technologies for sociological data collection. I study complex non-linear social phenomena by developing new mathematical techniques, building agent-based simulation models, and applying machine learning of big data collected via new and custom mobile platforms. I use these methods in a wide range of domain interests, most recently to better understand hard to reach populations (e.g. people who inject drugs, sex workers), the co-evolution of social networks and individuals therein, and processes of behavioral epidemiology, addiction and recovery.
I also work on emerging social organization within digital wireless communication networks and the “Internet of Things”. In this area, I advance our understanding of how to optimally manage and allocate spectrum resources, and map the evolutionary trajectory of emerging social structures in wireless device societies. As it turns out, current spectrum co-use strategies used by cellphones have many similarities to the resource co-use strategies recorded in early (primitive) human and animal societies. I consider how radio spectrum co-use etiquette among cellphones can be profitably improved by incorporating quantitative data on resource co-use etiquette from contemporary human societies.
I am interested in working with undergraduate and graduate students who have some background and (more importantly) a strong desire to master computational, mathematical, and data analytic approaches to sociological science. An ideal candidate will be irreverent towards disciplinary boundaries and will be open to working in a team science environment. Ideally, the student's thesis or dissertation interests should align with the current topics of the REACH Lab, though this is not an exclusionary criterion. The REACH Lab foci include: addiction, social determinants of health and health disparities, network science and social science methodologies, and rural, Native American, or hard-to-reach populations.
Student who join the REACH Lab are immersed in “lab culture”—meaning that they will work as a team member on a range of projects, and often many different projects at the same time, and that students’ roles change through time as they gain experience and take on more responsibility. Throughout, multiple lab-wide weekly meetings serve as an unofficial additional course (or two), and lab members gain exposure to a range of projects, methodologies and theoretical ideas from each other and from an interdisciplinary group of post-docs (whose backgrounds range from anthropology to psychology, and computer science to mathematics). Throughout students are encouraged to take an aggressive role in contributing to the research outputs of the Lab. It is not uncommon for graduate students who have worked at the REACH Lab for 5 years to complete their PhD with 5-10 published papers, often as lead authors by their last year or two in the lab. In addition, all lab members contribute to grant writing and thus gain exposure to this critical aspect of current academic life.
I generally teach graduate and undergraduate courses on the modeling and simulation of social systems. The course teaches algorithmic thinking, agent-based modeling, and computer programming for social scientific research. It uses simulation to arrive at "generative" answers to sociological questions like: How and why did cooperation or altruism emerge in human societies? Do ideological distinctions extend to leisure activities because of meaningful differences, or is it just peer influence at play? How did the practice of Osotua giving emerge among the Maasai of Tanzania? Under what circumstances does hypercompetition arise between firms?
Bilal Khan , Hsuan-Wei Lee, Ian Fellows, Kirk Dombrowski. One-step estimation of networked population size: Respondent-driven capture-recapture with anonymity, PLoS One, 2018.
Bilal Khan, Patrick Downey, Meredith Dank, and Kirk Dombrowski. A method for determining the size of the underground cash economy for commercial sex in seven U.S. cities, Handbook of the Economics of Prostitution, Pages 348-368, Published by Oxford University Press, Editors S. Cunningham and M. Shah, 2016.
2018 Bilal Khan, Hsuan-Wei Lee, Courtney Thrash, and Kirk Dombrowski, “Agency and Social Constraint among Victims of Domestic Minor Sex Trafficking: A Method for Measuring Free Will” Social Science Research.
2018 (forthcoming) Hsuan-Wei Lee, Miranda Melson, Jerreed Ivanich, Patrick Habecker, G. Robin Gauthier, Lisa Wexler, Bilal Khan, and Kirk Dombrowski. “Using Perceptual Tomography for Balance Clustering in Helping Networks of Alaska Natives”. PLoSOne (forthcoming)
2018 Anna Wisniewska, Mohammad Abu Shattal, Bilal Khan, Ala Al-Fuqaha, Kirk Dombrowski. Emergence of pecking order in social Cognitive Radio societies, INFOCOM (Workshop on SCAN: Advances in software-defined and context-aware cognitive networks).
Recent Grant Activity
“REU Site: Social Network Analysis for Solving Minority Health Disparities”. February 2018-January 2020. National Science Foundation 1757739
“Modeling Social Behavior via Dynamic Network Interaction” May 2016-April 2019.R01 GM118427 National Institutes of Health, General Medical Sciences.
“Applying Behavioral-Ecological Network Models to Enhance Distributed Spectrum Access in Cognitive Radio” August 2014-Dec 2018. National Science Foundation AST 1443985.
“Injection Risk Networks in Rural Puerto Rico” August 2014-July 2019. National Institutes of Health / National Institute of Drug Abuse R01 DA037117. NIDA Minority Supplement R01 DA037117-S1 -S2. “Competing Supplement: Injection Risk Networks in Rural Puerto Rico”. National Institutes of Health, National Institute on Drug Abuse R01 DA037117-S3.