KITCHEN INVASION: RESTAURANTS’ BUSINESS MODEL INNOVATIONS DURING THE COVID-19 CRISIS

My dissertation explores how and why firms facing the same exogenous threats react differently, leading to different business model innovation (BMI) processes. I examine BMI in a context that has been hard-hit by COVID-19 pandemic restrictions—the restaurant industry. Employing a mixed-method research design, I conducted a longitudinal, inductive comparative case study of 17 restaurateurs in the same geographic region to explore how they have responded to the pandemic and how their BMI unfolded over time. To generalize my understanding of these processes, I then analyzed large-scale media data about the restaurant industry using topic modeling. In this quantitative analysis, I explored relationships identified in the inductive study. From these analyses, I identified a new theoretical lens to explain how entrepreneurs engage in BMI during a crisis: sensemaking. Using different sensemaking frames (opportunity and threat), restauranteurs in this study undertook different patterns of BMI actions. Specifically, those who adopted an opportunity sensemaking frame are linked to two BMI patterns, (1) replacing or adding new business concepts and (2) expanding the business’s physical structure. Those who had a threat frame are related to two BMI patterns, (3) improving operational efficiency and (4) implementing temporary changes. In addition, unlike these restauranteurs, some restauranteurs who engaged in low-level sensemaking are associated with a BMI pattern, (5) using the same old business model. My topic modeling findings identify similar BMI patterns from restauranteurs across the U.S. My dissertation contributes to our understanding of BMI actions and processes by identifying the factors affecting BMI and explicating the dynamic processes BMI can take, rather than forcing a single framework on what is inherently a multi-modal process

Assessing the Impact of Operational Constraints on the Near-Term Unmanned Aircraft System Traffic Management Supported Market

An economic impact market analysis was conducted for 16 leading sectors of commercial Unmanned Aerial System (UAS) applications predicted to be enabled by 2020 through the NASA UAS Traffic Management (UTM) program. Subject matter experts from seven industries were interviewed to validate concept of operations (ConOps) and market adoption assumptions for each sector. The market analysis was used to estimate direct economic impacts for each sector including serviceable addressable market, capital investment, revenue recovery potential, and operations cost savings. The resultant economic picture distinguishes the agricultural, pipeline and railroad inspection, construction, and maritime sectors of the nascent commercial UAS industry as providing the highest potential economic value in the United States. Sensitivity studies characterized the variability of select UAS sectors economic value to key regulatory or UTM ConOps requirements such as weight, altitude, and flight over populated area constraints. Takeaways from the analysis inform the validation of UTM requirements, technologies and timetables from a commercial market need and value viewpoint. This work concluded in August 2015 and reflects the state of the UAS industry and market projections at that time

FAA and NASA UTM Research Transition Team: Communications and Navigation (CN) Working Group (WCG) Kickoff Meeting

This is NASA FAA UTM Research Transition Team Communications and Navigation working group kick off meeting presentation that addresses the followings. Objectives overview Overall timeline and scope Outcomes and expectations Communication method and frequency of meetings Upcoming evaluation Next steps

Arrival Scheduling with Shortcut Path Options and Mixed Aircraft Performance

Previous work introduced the concept of using tactical shortcut options to improve schedule conformance in terminal airspace. When a scheduling point is congested, aircraft are scheduled to longer nominal paths, holding shortcut path options in reserve for tactical use if an aircraft is late, thereby improving the schedule conformance, reducing the required scheduling buffer, and increasing throughput. When the scheduling point is less congested, aircraft may be scheduled to the shorter path with original larger scheduling buffers. Previous work focused on a single generic merge point serving aircraft with uniform arrival precision. This paper extends the previous concept to enhance the performance of time-based arrival management and consider mixed aircraft performance. Aircraft equipped to achieve a high degree of schedule conformance may be scheduled to the shorter path under the same conditions that a less equipped aircraft would be scheduled to the longer path, giving the equipped aircraft an advantage that can be seamlessly integrated into the scheduler. The arrival scheduler with shortcut path options for mixed aircraft performance is applied to a model of first-come first-served terminal metering at Los Angeles International Airport. Whereas clear system benefits were found for tactical shortcut routing and higher percentages of equipped aircraft, very little advantage could be seen for equipped over unequipped aircraft that could be used to incentivize early equipage

Algebraic and semi-algebraic invariants on quadrics

This dissertation consists of two topics concerning algebraic and semi-algebraic invariants on quadrics. The ranks of the minimal graded free resolution of square-free quadratic monomial ideals can be investigated combinatorially. We study the bounds on the algebraic invariant, Castelnuovo-Mumford regularity, of the quadratic ideals in terms of properties on the corresponding simple graphs. Our main theorem is the graph decomposition theorem that provides a bound on the regularity of a quadratic monomial ideal. By combining the main theorem with results in structural graph theory, we proved, improved, and generalized many of the known bounds on the regularity of square-free quadratic monomial ideals. The Hankel index of a real variety is a semi-algebraic invariant that quantifies the (structural) difference between nonnegative quadrics and sums of squares on the variety. This project is motivated by an intriguing (lower) bound of the Hankel index of a variety by an algebraic invariant, the Green-Lazarsfeld index, of the variety. We study the Hankel index of the image of the projection of rational normal curves away from a point. As a result, we found a new rank of the center of the projection which detects the Hankel index of the rational curves. It turns out that the rational curves are the first class of examples that the lower bound of the Hankel index by the Green-Lazarsfeld index is strict.Ph.D

Method to Enhance Scheduled Arrival Robustness (MESAR)

Method to Enhance Scheduled Arrival Robustness (MESAR) briefing to CAMIFAA Tech center visitor

Rapid Trajectory Prediction for a Fixed-Wing UAS in a Uniform Wind Field with Specified Arrival Times

This paper presents an algorithm to rapidly generate trajectories for a kinematic fixed-wing Unmanned Aircraft System (UAS) model flying at constant altitude in a uniform wind field. Arrival times are specified by operators and rapid generation is accomplished via an elliptic integral problem formulation. Simulations are provided that illustrate this approach in the context of NASA's UAS Traffic Management Project

You can't always sketch what you want: Understanding Sensemaking in Visual Query Systems

Visual query systems (VQSs) empower users to interactively search for line charts with desired visual patterns, typically specified using intuitive sketch-based interfaces. Despite decades of past work on VQSs, these efforts have not translated to adoption in practice, possibly because VQSs are largely evaluated in unrealistic lab-based settings. To remedy this gap in adoption, we collaborated with experts from three diverse domains---astronomy, genetics, and material science---via a year-long user-centered design process to develop a VQS that supports their workflow and analytical needs, and evaluate how VQSs can be used in practice. Our study results reveal that ad-hoc sketch-only querying is not as commonly used as prior work suggests, since analysts are often unable to precisely express their patterns of interest. In addition, we characterize three essential sensemaking processes supported by our enhanced VQS. We discover that participants employ all three processes, but in different proportions, depending on the analytical needs in each domain. Our findings suggest that all three sensemaking processes must be integrated in order to make future VQSs useful for a wide range of analytical inquiries.Comment: Accepted for presentation at IEEE VAST 2019, to be held October 20-25 in Vancouver, Canada. Paper will also be published in a special issue of IEEE Transactions on Visualization and Computer Graphics (TVCG) IEEE VIS (InfoVis/VAST/SciVis) 2019 ACM 2012 CCS - Human-centered computing, Visualization, Visualization design and evaluation method

Nearest Neighbor Guidance for Out-of-Distribution Detection

Detecting out-of-distribution (OOD) samples are crucial for machine learning models deployed in open-world environments. Classifier-based scores are a standard approach for OOD detection due to their fine-grained detection capability. However, these scores often suffer from overconfidence issues, misclassifying OOD samples distant from the in-distribution region. To address this challenge, we propose a method called Nearest Neighbor Guidance (NNGuide) that guides the classifier-based score to respect the boundary geometry of the data manifold. NNGuide reduces the overconfidence of OOD samples while preserving the fine-grained capability of the classifier-based score. We conduct extensive experiments on ImageNet OOD detection benchmarks under diverse settings, including a scenario where the ID data undergoes natural distribution shift. Our results demonstrate that NNGuide provides a significant performance improvement on the base detection scores, achieving state-of-the-art results on both AUROC, FPR95, and AUPR metrics. The code is given at \url{https://github.com/roomo7time/nnguide}.Comment: Accepted to ICCV202