Computer science and systems (Tacoma)
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Automated Parallelization to Improve Usability and Efficiency of Distributed Neural Network Training
The recent success of Deep Neural Networks (DNNs) [1] has triggered a race to build larger and larger DNNs [2]; however, a known limitation is the training speed [3]. To solve this speed problem, distributed neural network ... -
SMPCEngine: An N-Party Implementation of the Secure Multiparty Private Computation Protocol
In this thesis, we implement a framework for secure multiparty computation. Our framework works in the commodity-based model, where the players running the distributed computation receive pre-distributed data from a trusted ... -
Automatic Detection of Providers with Excess Healthcare Spending
This thesis aims to develop techniques to help large hospital systems to detect providers with excess spending. Identifying fraud, waste, and abuse resulting in super uous expendi- tures associated with care delivery is ... -
Performance Analysis of Real Time Streaming Systems for Smart Buildings
The Internet of Things (IoT) extends traditional cyber-physical systems by linking sensor-based edge devices to additional network-accessible services and resources. In most current IoT deployments, sensor data is streamed ... -
Integrating external biological knowledge in the construction of regulatory networks from LINCS data
The inference of gene regulatory networks is of great interest and has various applications. The recent advances in high-throughout biological data collection have facilitated the construction and understanding of gene ... -
A Sequence Based Approach for Predicting Clinical Events
The data associated to each patient increases almost linearly as the patient flows through the continuum of care. Analysis of the data collected during a patient’s admission to the hospital reveals that it grows vertically ... -
Communication and Round Balanced Oblivious FSM Evaluation
Privacy is a major issue in the age of the Internet. Many advances are being made in Cryptography regarding performing computations over private data, both in homomorphic encryption, multi-party computation, and applications ... -
Scalable Propagation Algorithms for Node Labeling in Bipartite Graphs
Bipartite graphs are graphs whose vertices can be partitioned into 2 different independent sets. Many interesting machine learning problems either present themselves naturally as problems of node ... -
Predictive Analytics and Decision Support for Heart Failure patients
In the last few years, legislations such as the Patient Protection and A ordable Care Act, also known as Obamacare, have emphasised the need for improving the quality of health care. Part of the programs introduced by this ... -
Classifying Child Maltreatment by Brain Imaging Data
Childhood maltreatment is a condition that leads to the development of behavioral issues and affect brain structure and functionality. Recent neuroimaging studies provide connection between deficits in brain volume, gray ... -
Application of Machine Learning Techniques to Acute Myeloid Leukemia
This thesis is inspired by the position paper ”Predictive, personalized, preventive, par- ticipatory (P4) cancer medicine” [1]. The basic concept of P4 medicine was “The right patient with the right drug at the right dose ... -
Gene Network Inference using Machine Learning and Graph Algorithms on Big Biomedical Data
Gene networks capture the interactions between different biological entities. These gene networks have many applications in modern day biology. In particular, gene networks can help to shed light on the underlying mechanisms ... -
An Evaluation of Complex Adaptive Evolvable System Simulation
Building models has been a human activity for centuries. Through the ages man has discovered patterns in how objects function together in the exchange of information, materials, and energy. We know these groups of objects ... -
Prediction and Privacy in Healthcare Analytics
In the past decade, the United States federal government has made improving the healthcare system a major focus with legislation such as the Patient Protection and Affordable Care Act and the financial incentives for ... -
Discrete Gaussian Sampling for Low-Power Devices
Sampling from the discrete Gaussian probability distribution is used in lattice-based cryptosystems. A need for faster and memory-efficient samplers has become a necessity for improving the performance of such cryptosystems. ... -
Energy-aware Workflow Scheduling and Fuzzy Logic Based Capacity Provisioning in Cloud Environment
The increasing need of large-scale data centers has brought new challenges to the development of energy-efficiency techniques. Although many optimization techniques have been proposed, most of them neglect the characteristics ... -
Selecting Robust Strategies in RTS Games via Concurrent Plan Augmentation
The multifaceted complexity of real-time strategy (RTS) games requires AI systems to break down policy computation into smaller subproblems such as strategic planning, tactical planning, and reactive control. To further ... -
Intervention Recommendations to Minimize the 30-Day Risk-of-Readmission for Heart Failure
In this thesis, we investigate the problem of designing personalized intervention strategies to minimize 30-day readmission risk for heart failure (HF) patients. In particular, we propose a novel framework that recommends ... -
Distributed Diverging Topic Models: A Novel Algorithm for Large Scale Topic Modeling in Spark
In their 2001 work Latent Dirichlet Allocation, Blei, Ng, and Jordan proposed the generative model of the same name that has since become the basis for most research in the field of topic modeling. The model overcame many ... -
Inferring Big 5 Personality from Online Social Networks
Online social networks are very popular with millions of people creating online profiles and sharing personal information including their interests, activities, likes/dislikes and thoughts with their friends and family. ...
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