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CDA is a mobile application that helps students, specifically those without a mentor, break into the competitive investment banking industry. Investment banking is arguably one of the most competitive industries to break into because it is widely viewed as the premier finance career available out of undergraduate school. The industry

CDA is a mobile application that helps students, specifically those without a mentor, break into the competitive investment banking industry. Investment banking is arguably one of the most competitive industries to break into because it is widely viewed as the premier finance career available out of undergraduate school. The industry is unique in the sense that there are many unwritten rules on how to break into the industry. The large investment banks receive tens of thousands of applications every year, but only an extremely small percentage of those applications are viewed. This is a problem for a majority of students, who believe that simply having a high GPA and a passion for finance and submitting an application is adequate to get into investment banking. Many students who successfully make it into the industry are provided with programs and mentors to teach them everything necessary to do so, but I have noticed that there are many other qualified students that are not given these resources and are, accordingly, not as successful getting into the industry. This application is designed to provide some of the custom mentorship advice necessary to get into investment banking. I will approach this project as a business venture and will develop methods to monetize the services this program provides.
ContributorsRasmussen, Parker John (Author) / Balasooriya, Janaka (Thesis director) / Bennett, Jack (Committee member) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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Description
Attributes - that delineating the properties of data, and connections - that describing the dependencies of data, are two essential components to characterize most real-world phenomena. The synergy between these two principal elements renders a unique data representation - the attributed networks. In many cases, people are inundated with vast

Attributes - that delineating the properties of data, and connections - that describing the dependencies of data, are two essential components to characterize most real-world phenomena. The synergy between these two principal elements renders a unique data representation - the attributed networks. In many cases, people are inundated with vast amounts of data that can be structured into attributed networks, and their use has been attractive to researchers and practitioners in different disciplines. For example, in social media, users interact with each other and also post personalized content; in scientific collaboration, researchers cooperate and are distinct from peers by their unique research interests; in complex diseases studies, rich gene expression complements to the gene-regulatory networks. Clearly, attributed networks are ubiquitous and form a critical component of modern information infrastructure. To gain deep insights from such networks, it requires a fundamental understanding of their unique characteristics and be aware of the related computational challenges.

My dissertation research aims to develop a suite of novel learning algorithms to understand, characterize, and gain actionable insights from attributed networks, to benefit high-impact real-world applications. In the first part of this dissertation, I mainly focus on developing learning algorithms for attributed networks in a static environment at two different levels: (i) attribute level - by designing feature selection algorithms to find high-quality features that are tightly correlated with the network topology; and (ii) node level - by presenting network embedding algorithms to learn discriminative node embeddings by preserving node proximity w.r.t. network topology structure and node attribute similarity. As changes are essential components of attributed networks and the results of learning algorithms will become stale over time, in the second part of this dissertation, I propose a family of online algorithms for attributed networks in a dynamic environment to continuously update the learning results on the fly. In fact, developing application-aware learning algorithms is more desired with a clear understanding of the application domains and their unique intents. As such, in the third part of this dissertation, I am also committed to advancing real-world applications on attributed networks by incorporating the objectives of external tasks into the learning process.
ContributorsLi, Jundong (Author) / Liu, Huan (Thesis advisor) / Faloutsos, Christos (Committee member) / He, Jingrui (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2019