Research

Summary

The traditional underwriting process used in life insurance is based on manually examining an applicant’s health, behavioral, and financial profile. The existence of large historical data sets provides an unprecedented opportunity for artificial intelligence and machine learning to transform underwriting in the life insurance industry. We present an overview of how a rich application data set and survival modeling were combined to develop a state of the art life score that is inpretable by consumers and carriers alike. Through a novel evaluation framework, we show that the score outperforms traditional underwriting by 6% on the basis of claims.

Summary

Using data from nearly 1,000,000 publicly available resumes, I am constructed a network of occupations. Using the ordered sequences of occupations contained in the resumes, I created a probabilistic model of career path dynamics that connected the network structure with workers' career path decisions. The goal of this research was to uncover relationships between the structure of occupation networks and the dynamics of career paths.

Summary

Using data from the last decade, I conducted a statistical analysis on millions of scoring events from four different professional sports: basketball, hockey, soccer, and American football. I studied inter-arrival times and correlations between events as well as distributions of streaks and final scores, among other statistical properties. This research developed tools for the analysis and modeling of scoring dynamics and shed new light on the existence of universal properties of scoring dynamics in sports.

Summary

Competition is ubiquitous in complex social systems, from informal online environments to professional sports, to economic interactions between firms. Traditional studies of competition use theory and small-scale controlled experiments to study the outcomes of competition, not the dynamics that occur within them. Here we have analyzed the dynamics of competition using a rich and vast data set from the video game Halo: Reach, using novel quantitative methods and big data processing systems. There are two goals of this research. The first is to produce data-driven mathematical models that contribute to a general understanding of the interactions between participants and the environment. The second is designing new algorithms and tools that have the ability to predict, influence, and control the dynamics in these systems. These results will produce an ability to systematically design competitive social systems to the preferences of participants.

Two-way TCP connections: old problem, new insight

ACM SIGCOMM Computer Communication Review, Vol. 41, No. 2, Apr. 2011.

M. Heusse, S. Merritt, T. Brown, and A. Duda