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\textit{Version 1. 10/25/2015}   The focus of my research work up till now has been on Computational Social Science which can be considered as an intersection of Social Sciences and Computer Science. Computational Social Science is a big field employing different algorithmic tools and techniques to solve different problems in different areas of social sciences. My focus within Computational Social Science is on Human Behavior Analysis using Data Science Techniques. Study in this area will enable me to see that how different aspects of Human Behavior can be analyzed better using computational and statistical techniques.  Application of big data tools and techniques is becoming more and more important every day as the magnitude of information and data available for analysis has grown tremendously in the last decade. As a result, many research projects have been carried out which employ new and efficient techniques for mining human data. My research is focused on application of these big data tools and techniques on social problems. Some of the problems that I have been working on include Customer Churn Prediction using Telecommunication CDR data, Migration Trends Analysis using Telecommunication CDR Data and Latent Profile Features Predication using Twitter Data.  Human Behavior Analysis using Data Science is a natural progression of my background in Computer Science and theme of the projects that I have been involved in as a PhD Student in the Information School during the last 2 years.   Reading list for my general exam will consist of research material involving major techniques in data science and the application of the these techniques on the problems related to Social Science.   \\  \section{Methods (Data Science (Machine Learning) Literature)}  \begin{enumerate}  \item Tomaso Poggio and Steve Smale (2003). The Mathematics of Learning: Dealing with Data\cite{Poggio_2005} 
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\item Yaser S. AbuMostafa et al. (2012). Learning from Data \cite{Abu-Mostafa:2012:LD:2207825}  \end{enumerate}  \\  \\  \section{Application of Data Science to Social Problems}  \subsection{Data Science and Development Economics} \\  \subsection{Data Science and Development Economics}  \\  \begin{enumerate}  \item Esther Duflo (2000). Schooling and Labor Market Consequences of School Construction in Indonesia: Evidence from an Unusual Policy Experiment \cite{Duflo_2000}  \item Daniel Bjorkegren (2015). The Adoption of Network Goods \cite{Bjorkegren}  \end{enumerate}  \\  \subsection{Data Science and Measurements}  \begin{enumerate}  \item P Deville et al. (2014). Dynamic Population Mapping using Mobile Phone Data\cite{Deville_2014} 
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\item Wang et al. (2015). Forecasting Elections with Non-Representative Polls\cite{Wang2015980}  \item C. Smith-Clarke et al. (2014). Poverty on the Cheap: Estimating Poverty Maps Using Aggregated Mobile Communication Networks\cite{Smith-Clarke:2014:PCE:2556288.2557358}  \end{enumerate}  \\  \subsection{Migration, Mobility and Epidemiology using Big Data}  \begin{enumerate}  \item Understanding individual human mobility patterns\cite{Gonz_lez_2008} 
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\item Social Influence Bias: A Randomized Experiment  \item Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks  \item Inferring Networks of Diffusion and Influence  \item “Modeling Modeling  Information Propagation with Survival Theory \item Divided We Call: Disparities in Access and Use of Mobile Phones in Rwanda  \item Predictability of Population Displacement after the 2010 Haiti Earthquake  \item "Improved Improved  Response to Disasters and Outbreaks by Tracking Population Movements with Mobile Phone Network Data: A Post-Earthquake Geospatial Study in Haiti \item Quantifying Information Flow during Emergencies  \item Bagrow et al. 2011).  Collective Response of Human Populations to Large-Scale Emergencies \cite{Bagrow_2011} \item Eang Wang  et al. (2014). Learning to Detect Patterns of Crime\cite{Wang_2013} \end{enumerate}  \\  \subsection{Data Science, Human Behavior and Networks}  \begin{enumerate}  \item Jessica Su Quang Duong  et al. The Effect of Recommendations on Network Structure  \item Quang Duong. (2013) .  Sharding Social Networks Networks\cite{Duong_2013}  \item Daniel Goel,  Goldstein. (2014)  Predicting Individual Behavior with Social Networks \item Daniel Reeves. Predicting without Markets \cite{Goel_2014}  \end{enumerate}  \\  \subsection{Information Flow and Consumption}  \begin{enumerate}  \item Seth Flaxman. Filter Bubbles, Chambers and News Conumptions  \item Ashton Anderson et. Goel et  al. (2015).  The Structual Virality of Online Diffusion The Structual Virality of Online Diffusion\cite{Goel_2015}  \item Dafna Shahaf, Carlos Guestrin (2012). Connecting the Dots between news articles . \cite{Shahaf:2012:CTD:2086737.2086744}   \end{enumerate}  \\  \subsection{Advertisements and Recommendations}  \begin{enumerate}  \item Hill et al. (2006).  Network-Based Marketing: Identifying Likely Adopters via Consumer Networks \cite{Hill_2006}  \item Bhagat et al. (2012).  Maximizing Product Adoption in Social Networks\cite{Bhagat:2012:MPA:2124295.2124368} \end{enumerate}