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- All Subjects: Applications
- Creators: Bennett, Jack
- Creators: Chen, Cynthia Yue
Description
Mental illness has always been a stark fact of life, whether it affects people directly or indirectly. More and more research is now showing that mental illness is becoming increasingly more widespread in the U.S. This poses a serious problem to our society in terms of treatment of mental illness, as well as the costs that are involved with treating those who are affected with different disorders. According to the CDC's (Centers for Disease Control and Prevention) Autism and Developmental Disabilities Monitoring (ADDM) Network, about 1 in 68 children have been identified with autism spectrum disorder (ASD). A large number of these children are given psychotropic medications when there is no real proof of their efficacy. Our children are our society's future, so how can we aide parents of these young children to ultimately benefit their future? As a result of my research in the gut-brain connection, I have developed an application called Portal. Portal will serve the parents of children with autism in assisting with their daily lives and teaching them about the most up to date research. This application will work in conjunction with thesis material developed in the Visual Communication Design program to create a well-rounded approach to incorporating knowledge of the gut-brain connection in everyday life. Portal will have a variety of capabilities including that of autism related news, diet plans, schedules, community resources, and medication planning. There will be a daily tip or reminder to incorporate knowledge of the gut-brain connection in daily life. This can be something as simple as a reminder to eat vegetables to a fact about the research. The main goal of Portal is to assist parents in adapting to a lifestyle with ASD easier and healthier for both parents and children alike.
ContributorsChen, Cynthia Yue (Author) / Sanft, Alfred (Thesis director) / Heywood, William (Committee member) / Barrett, The Honors College (Contributor) / The Design School (Contributor)
Created2014-05
Description
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
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 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.
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