One decision very few high school student-athletes are faced with is which Division I athletic scholarship to accept. These highly coveted scholarships can cover for all expenses and provide support during and after the undergraduate experience. Yet, even if high school student-athletes accept a Division I offer, it doesn’t guarantee their success. Many athletes struggle with student identity as they try to remain eligible to complete at the Division I level. I draw from identity theory on figured worlds to investigate how student success as a Division I athlete is linked to negotiating their figured world of being a student as it informs their decision about which Division I scholarship to accept. Using data from interviews and observations, this study follows two elite high school student-athletes as they make their decision about which Division I scholarship to accept, and how their dual identities as students and athletes shaped their decision-making process.

Rapid decentralization of population and employment over the past several decades has chipped away at the American transit industry's market share. This paper examines the implications of decentralization on the ridership, operating performance, and fiscal health of the nation's largest transit operators. Based on the results of a national survey, a number of service strategies are explored that offer hope for reversing transit's decline, including: timed-transfers, paratransit services, reverse commute and specialized runs, employer-sponsored vanpools, and HOV/dedicated busway facilities. Land-use options, like traditional neighborhood designs and transit-based housing, are also examined. The paper ends with a discussion of various institutional, pricing, and organizational considerations when implementing service reforms and land-use initiatives. Century-old models involving joint public-private development of communities and transit facilities, it is argued, also deserve reconsideration.

Even though wireless and mobile devices have evolved with richer capabilities, resources in such devices are still limited, and thus in many cases, are insufficient to accomplish the tasks entrusted in them. The goal of my work is to improve the efficiencies of applications on wireless and mobile devices, focusing on resource constrained settings.

First, we develop TIDE, a user-centric framework that helps to identify high energy consuming applications on users’ smartphones. TIDE identifies energy hungry applications by looking at the correlation between applications’ activities and high drainage periods on the smartphones. Experiments on Android smartphones show that TIDE is able to identify correctly ≈ 90% of the energy hungry applications, while imposing reasonably low energy overheads.

Subsequently, we develop a framework to identify redundant images uploaded from multiple wireless devices in bandwidth constrained networks, e.g., the destructed networks at natural disaster scenes. Our framework intelligently combines state-of-the-art vision techniques to identify redundant images uploaded to a server. Suppressing the transfer

of redundant contents significantly lowers network load, so that the delay in transferring unique and important contents in such critical scenarios is reduced up to ≈ 44%.

We then design ACTION, a framework for accurate and timely object (e.g., human) detection in bandwidth constrained settings. In ACTION, the objects of interest are effectively detected at individual camera sensors. Metadata of detected objects is then aggregated at a designated fusion node to improve the detection accuracy. Most accurate information of each detected object is then chosen to upload to a central controller, while adhering to the bandwidth constraints. We show that ACTION helps reduce up to three folds the amount of transferred data, while still delivering important information to the central node.

Finally, we design EECS, a framework for adaptive detection algorithm selection in multi-camera settings. In EECS, only a subset of camera sensors is chosen to detect objects; further, the most energy efficient algorithm is assigned to each camera to reduce energy consumption, while still ensuring a desired accuracy. We show that EECS can be tuned to achieve the right trade-offs between energy efficiency and desired accuracy.

CPUs and dedicated accelerators (namely GPUs and FPGAs) continue to grow increasingly large and complex to support todays demanding power and performance requirements. Designers are tasked with evaluating the performance and power of increasingly large design spaces during pre-silicon design. Validating and evaluating the performance during the pre-silicon stage catches performance and device issues in advance of fabrication. This reduces time-to-market by reducing the bugs that must be found and fixed after manufacturing, or after synthesizing an FPGA accelerator using high-level synthesis tools.

Cycle-accurate simulators are integral to architectural design and are often the only tool at computer architects’ disposal to evaluate workloads on architectural design points in advance of manufacturing. To enable optimization of the architecture without the exorbitant cost of repeatedly fabricating designs, a high-level of simulator precision is required. As such, the simulators are compute intensive, limiting the number of simulations, and thus workloads or design points that can be evaluated before manufacturing. Historically the computational cost of simulation is absorbed, which slows down time to market and increases the cost of development.

Machine Learning and statistical prediction models have emerged as viable tools to avoid repeated cycle accurate simulations by accurately predicting the metrics generated by cycle-accurate simulators. After one-time model training, the predictive models can then be used in lieu of simulation. Here I will present my research into the development of machine learning frameworks for GPU and FPGA architectures, which leverage cross-architecture predictive statistical modeling to significantly reduce the time required to evaluate workloads and architectural design points.

Chronic exposure to high concentrations of pollutants such as NO2 and ultrafine particles is associated with negative health effects. Studies of exposure to these pollutants require estimates of concentrations at temporal and spatial scales relevant to exposure calculations. We have developed and applied methods to construct these concentration "maps" by using a combination of measurements and modeled results. To estimate concentration patterns at the urban scale of tens of kilometers we have formulated a Lagrangian model to estimate concentrations of NOx, NO2, and O3 over a domain extending over hundreds of kilometers. The model is evaluated with data collected at 21 regional monitoring stations in the San Joaquin Valley Air Basin during 2005. The model provides adequate descriptions of the spatial and temporal variation of concentrations of NO2, and NOx. We then use "residual" Kriging to combine the results from the dispersion model with observed concentrations to produce realistic concentration maps.

To estimate concentration patterns at scales of tens of meters in urban areas we developed a dispersion model that accounts for the effects of local building morphology on dispersion. The data used to evaluate the model was collected in field studies conducted in Los Angeles, California. The studies measured ultrafine particle concentrations and associated micrometeorology at several locations with different building morphologies. Surface concentrations in urban areas are primarily controlled by vertical dispersion, which depends on the street aspect ratio, defined as the ratio of the equivalent building height to the street width, and the vertical turbulent velocity ów. The presence of buildings increases the concentrations due to local traffic emissions relative to open areas. Since routine measurements of micrometeorological variables are usually not available in urban areas, we have developed models that allow us to estimate urban surface variables using values measured at an upwind rural location. Results from the urban street scale dispersion model can be combined with measurements from urban monitors to generate concentration maps with spatial resolution of meters and time resolution of minutes.