Mon, Jun 21, 2021 @ 10:00 AM - 11:30 AM
Thomas Lord Department of Computer Science
Title: Modeling Dynamic Behaviors in the Wild
PhD Candidate: Nazgol Tavabi
Committee: Kristina Lerman (Chair), Shrikanth (Shri) Narayanan, Bistra Dilkina, Xiang Ren
The abundant real-time data collected from people in the wild creates new opportunities to better understand human behaviors. One example, is temporal data collected from wearable sensors. The ability to analyze this data, offers new opportunities for real-time monitoring of physical and psychological health. Physiological data collected from wearable sensors has been used to detect activities, diagnose illnesses, and analyze habits and personality traits. However, temporal physiological data presents many analytic challenges: the data is multimodal, heterogeneous, noisy; may contain missing values, and long sequences with different lengths. Existing methods for time series analysis and classification are often not suitable for data with these characteristics, nor do they offer interpretability and explainability, a critical requirement in the health domain.
In this thesis, I address some of the challenges in learning representations from these complex temporal data. First, I propose a method based on non-parametric Hidden Markov Models to learn interpretable representations from time series. This method is applied to analyze, cluster, regress and classify multiple datasets. Second, I propose Pattern Discovery with Byte Pair Encoding method to better capture long-term dependencies in lengthy time series, which learns representations by extracting variable length patterns using Byte Pair Encoding compression technique. The proposed model is interpretable, explainable and computationally efficient, and beats state-of-the-art approaches on a real world dataset collected from wearable sensors. Finally, I systematically evaluate how the presence of missing data affects the performance of different state-of-the-art time series classification methods. My work shows how performance of different methods degrades as a function of missing data and, using imputation methods generally does not make a significant difference in the results.
The proposed models and findings, could help better understand and analyze dynamic behaviors within a population and offer new perspectives on monitoring and predicting human behaviors from data collected in the wild.
Audiences: Everyone Is Invited
Contact: Lizsl De Leon