Simultaneous Measurement Imputation and Outcome Prediction for Achilles Tendon Rupture Rehabilitation

Achilles Tendon Rupture (ATR) is one of the typical soft tissue injuries. Rehabilitation after such a musculoskeletal injury remains a prolonged process with a very variable outcome. Accurately predicting rehabilitation outcome is crucial for treatment decision support. However, it is challenging to train an automatic method for predicting the ATR rehabilitation outcome from treatment data, due to a massive amount of missing entries in the data recorded from ATR patients, as well as complex nonlinear relations between measurements and outcomes. In this work, we design an end-to-end probabilistic framework to impute missing data entries and predict rehabilitation outcomes simultaneously. We evaluate our model on a real-life ATR clinical cohort, comparing with various baselines. The proposed method demonstrates its clear superiority over traditional methods which typically perform imputation and prediction in two separate stages

Data Monitoring and Analysis in Wireless Networks

Various wireless network technologies have been created to meet the ever-increasing demand for wireless access to the Internet, such as wireless local area network, cellular network, sensor network and many more. The communication devices have transformed from large computational servers to small wireless hand-held devices, ranging from laptops, tablets, smartphones to small sensors. The advances of these wireless networks (e.g., faster network speed) and their intensive usages result in an enormous growth of network data in terms of volume, diversity, and complexity. All of these changes have raised complicated issues of network measurement and management. In the first part of this thesis, I study how WiFi network characteristics impact network forensics investigation and home security monitoring. I first focus on network forensics investigation and propose a wireless forensic monitoring system to collect trace digests of WiFi activities and facilitate cybercrime investigation. Then, I design and develop a low-cost home security system based on WiFi networks for physical intruder detection. Two methods - MAC-based detection and RSSI-variance-based detection, are proposed based on the characteristics of WiFi networks. In the second part, I study how to effectively and efficiently model multiple coevolving time series, which is ubiquitous in network measurement especially in wireless sensor networks. Two comprehensive algorithms are proposed to address three prominent challenges of mining coevolving sensor measured traces: (a) high order; (b) contextual constraints; and (c) temporal smoothness

Probabilistic sequential matrix factorization

We introduce the probabilistic sequential matrix factorization (PSMF) method for factorizing time-varying and non-stationary datasets consisting of high-dimensional time-series. In particular, we consider nonlinear Gaussian state-space models where sequential approximate inference results in the factorization of a data matrix into a dictionary and time-varying coefficients with potentially nonlinear Markovian dependencies. The assumed Markovian structure on the coefficients enables us to encode temporal dependencies into a low-dimensional feature space. The proposed inference method is solely based on an approximate extended Kalman filtering scheme, which makes the resulting method particularly efficient. PSMF can account for temporal nonlinearities and, more importantly, can be used to calibrate and estimate generic differentiable nonlinear subspace models. We also introduce a robust version of PSMF, called rPSMF, which uses Student-t filters to handle model misspecification. We show that PSMF can be used in multiple contexts: modeling time series with a periodic subspace, robustifying changepoint detection methods, and imputing missing data in several high-dimensional time-series, such as measurements of pollutants across London.Comment: Accepted for publication at AISTATS 202

A Dynamic Additive and Multiplicative Effects Model with Application to the United Nations Voting Behaviors

We introduce a regression model for a series of networks that are correlated over time. Our model is a dynamic extension of the additive and multiplicative effects network model (AMEN) of Hoff (2019) In addition to incorporating a temporal structure, the model accommodates two types of missing data thus allows the size of the network to vary over time. We demonstrate via simulations the necessity of various components of the model. We apply the model to the United Nations General Assembly voting data from 1983 to 2014 (Voeten (2013)) to answer interesting research questions regarding to international voting behaviors. In addition to finding important factors that could explain the voting behaviors, the model-estimated additive effects, multiplicative effects, and their movements reveal meaningful foreign policy positions and alliances of various countries