Adaptive Matching for Expert Systems with Uncertain Task Types

A matching in a two-sided market often incurs an externality: a matched resource may become unavailable to the other side of the market, at least for a while. This is especially an issue in online platforms involving human experts as the expert resources are often scarce. The efficient utilization of experts in these platforms is made challenging by the fact that the information available about the parties involved is usually limited. To address this challenge, we develop a model of a task-expert matching system where a task is matched to an expert using not only the prior information about the task but also the feedback obtained from the past matches. In our model the tasks arrive online while the experts are fixed and constrained by a finite service capacity. For this model, we characterize the maximum task resolution throughput a platform can achieve. We show that the natural greedy approaches where each expert is assigned a task most suitable to her skill is suboptimal, as it does not internalize the above externality. We develop a throughput optimal backpressure algorithm which does so by accounting for the `congestion' among different task types. Finally, we validate our model and confirm our theoretical findings with data-driven simulations via logs of Math.StackExchange, a StackOverflow forum dedicated to mathematics.Comment: A part of it presented at Allerton Conference 2017, 18 page

Facilitating Cooperative Truck Platooning for Energy Savings: Path Planning, Platoon Formation and Benefit Redistribution

Enabled by the connected and automated vehicle (CAV) technology, cooperative truck platooning that offers promising energy savings is likely to be implemented soon. However, as the trucking industry operates in a highly granular manner so that the trucks usually vary in their operation schedules, vehicle types and configurations, it is inevitable that 1) the spontaneous platooning over a spatial network is rare, 2) the total fuel savings vary from platoon to platoon, and 3) the benefit achieved within a platoon differs from position to position, e.g., the lead vehicle always achieves the least fuel-saving. Consequently, trucks from different owners may not have the opportunities to platoon with others if no path coordination is performed. Even if they happen to do so, they may tend to change positions in the formed platoons to achieve greater benefits, yielding behaviorally unstable platoons with less energy savings and more disruptions to traffic flows. This thesis proposes a hierarchical modeling framework to explicate the necessitated strategies that facilitate cooperative truck platooning. An empirical study is first conducted to scrutinize the energy-saving potentials of the U.S. national freight network. By comparing the performance under scheduled platooning and ad-hoc platooning, the author shows that the platooning opportunities can be greatly improved by careful path planning, thereby yielding substantial energy savings. For trucks assembled on the same path and can to platoon together, the second part of the thesis investigates the optimal platoon formation that maximizes total platooning utility and benefits redistribution mechanisms that address the behavioral instability issue. Both centralized and decentralized approaches are proposed. In particular, the decentralized approach employs a dynamic process where individual trucks or formed platoons are assumed to act as rational agents. The agents decide whether to form a larger, better platoon considering their own utilities under the pre-defined benefit reallocation mechanisms. Depending on whether the trucks are single-brand or multi-brand, whether there is a complete information setting or incomplete information setting, three mechanisms, auction, bilateral trade model, and one-sided matching are proposed. The centralized approach yields a near-optimal solution for the whole system and is more computationally efficient than conventional algorithms. The decentralized approach is stable, more flexible, and computational efficient while maintaining acceptable degrees of optimality. The mechanisms proposed can apply to not only under the truck platooning scenario but also other forms of shared mobility.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163047/1/xtsun_1.pd

Adaptive Matching for Expert Systems with Uncertain Task Types

International audienceA matching in a two-sided market often incurs an externality: a matched resource maybecome unavailable to the other side of the market, at least for a while. This is especiallyan issue in online platforms involving human experts as the expert resources are often scarce.The efficient utilization of experts in these platforms is made challenging by the fact that theinformation available about the parties involved is usually limited.To address this challenge, we develop a model of a task-expert matching system where atask is matched to an expert using not only the prior information about the task but alsothe feedback obtained from the past matches. In our model the tasks arrive online while theexperts are fixed and constrained by a finite service capacity. For this model, we characterizethe maximum task resolution throughput a platform can achieve. We show that the naturalgreedy approaches where each expert is assigned a task most suitable to her skill is suboptimal,as it does not internalize the above externality. We develop a throughput optimal backpressurealgorithm which does so by accounting for the ‘congestion’ among different task types. Finally,we validate our model and confirm our theoretical findings with data-driven simulations vialogs of Math.StackExchange, a StackOverflow forum dedicated to mathematic

Coresets-Methods and History: A Theoreticians Design Pattern for Approximation and Streaming Algorithms

We present a technical survey on the state of the art approaches in data reduction and the coreset framework. These include geometric decompositions, gradient methods, random sampling, sketching and random projections. We further outline their importance for the design of streaming algorithms and give a brief overview on lower bounding techniques

Microgrid optimization, modelling and control

2014 Fall.To view the abstract, please see the full text of the document

Developing Techniques for the Identification of Non-Canonical RNA Pairing and Analysis of LC-MS Datasets

Non-canonical pairing dynamics in ribonucleic acid (RNA) structureand statistical analysis of metabolomics liquid chromatography mass spectrometry (LC-MS) datasets are two difficult problems that stand as open challenges. RNA folding algorithms are used across a variety of disciplines to predict structures when experimental elucidation techniques are inconvenient or impractical. Though successful and widely adopted, folding algorithms make simplifying assumptions for loop regions due to their complex interactions and associated difficulty with generating energy parameters for relevant non-canonical pairing interactions. Modeling assumptions and a lack of energy parameters for loops limit accuracy in these functional critical regions of RNA. This work describes a new technique for probing non-canonical loop interactions through the combined analysis of dimethyl sulfate (DMS) and three-dimensional crystallographic data. We demonstrate that DMS data encodes information about non-canonical pairing which describes these interactions in an efficient, high throughput manner. Metabolomics aims to understand biological processes through the analysis of small molecule metabolites. The field primarily uses 1H nuclear magnetic resonance (NMR) spectroscopy as well as LC-MS to identify and quantitate metabolites. With even simple samples having hundreds or thousands of metabolites, researchers in the field have developed software pipelines to make metabolomics studies a tractable task. Numerous packages exist for the analysis of either 1H NMR or LC-MS data, but current offerings force researchers to use multiple packages to analyze both data types. To address the need for a metabolomics package capable of analyzing both, we have developed new LC-MS functionality for the NMR metabolomics package MVAPACK. Advisor: Joseph D. Yesselma

Mobile localization : approach and applications

textLocalization is critical to a number of wireless network applications. In many situations GPS is not suitable. This dissertation (i) develops novel localization schemes for wireless networks by explicitly incorporating mobility information and (ii) applies localization to physical analytics i.e., understanding shoppers' behavior within retail spaces by leveraging inertial sensors, Wi-Fi and vision enabled by smart glasses. More specifically, we first focus on multi-hop mobile networks, analyze real mobility traces and observe that they exhibit temporal stability and low-rank structure. Motivated by these observations, we develop novel localization algorithms to effectively capture and also adapt to different degrees of these properties. Using extensive simulations and testbed experiments, we demonstrate the accuracy and robustness of our new schemes. Second, we focus on localizing a single mobile node, which may not be connected with multiple nodes (e.g., without network connectivity or only connected with an access point). We propose trajectory-based localization using Wi-Fi or magnetic field measurements. We show that these measurements have the potential to uniquely identify a trajectory. We then develop a novel approach that leverages multi-level wavelet coefficients to first identify the trajectory and then localize to a point on the trajectory. We show that this approach is highly accurate and power efficient using indoor and outdoor experiments. Finally, localization is a critical step in enabling a lot of applications --- an important one is physical analytics. Physical analytics has the potential to provide deep-insight into shoppers' interests and activities and therefore better advertisements, recommendations and a better shopping experience. To enable physical analytics, we build ThirdEye system which first achieves zero-effort localization by leveraging emergent devices like the Google-Glass to build AutoLayout that fuses video, Wi-Fi, and inertial sensor data, to simultaneously localize the shoppers while also constructing and updating the product layout in a virtual coordinate space. Further, ThirdEye comprises of a range of schemes that use a combination of vision and inertial sensing to study mobile users' behavior while shopping, namely: walking, dwelling, gazing and reaching-out. We show the effectiveness of ThirdEye through an evaluation in two large retail stores in the United States.Computer Science