Meet Our Students

Graduate Student Profiles

Below is a list of international students at UBC Applied Science who provided profiles about their research. It will give you an idea why students chose to come to UBC, what they like about Vancouver and what their career goals are. Profiles courtesy of the Faculty of Graduate + Postdoctoral Studies.

Assimilation, Place, and Indigenous Identity in the Era of Reconciliation
My doctoral research will focus on the experience, negative impacts, and substantial limits of assimilation policies in Canada. My project includes three case studies, which I will examine using a combination of archival research and in-depth interviews: Piikani opposition to the Old Man River Dam; Blackfoot traditional adoption practices; and contemporary urban Indigenous cultural revitalization projects. I have chosen these three areas of study because they provide excellent examples of major assimilation policies and processes in Canada: economic development, the residential school system, and urbanization. My objective is to bring together the valuable insights from these three studies to answer my main research question: How do land relations, past assimilation policies, and the agency of First Nations communities work together to inform contemporary Indigenous identities and ideas of nationhood?
Advanced Process Control and Analytics
Our proposed research objective is to develop a data-centric control framework that applies scalable machine learning techniques on multilevel heterogeneous process data in order to extract knowledge for improved process operation. Specifically, the objective is to successfully detect and diagnose process conditions of interest (i.e., disturbances or process upsets) and to subsequently control for or against such conditions. Ultimately, we wish to enlighten the way the process analytics community perceives value in data, in order to achieve some of the many benefits other industries are experiencing.
Attack Detection and Diagnosis in Cyber-Physical Systems
Cyber-Physical Systems (CPS) are being widely deployed in security-critical scenarios such as smart homes and medical devices. Unfortunately, the connectedness of these systems and their relative lack of security measures makes them ripe targets for attacks. Speci€cation-based Intrusion Detection Systems (IDS) have been shown to be effective for securing CPSs. Unfortunately, deriving invariants for capturing the speci€cations of CPS systems is a tedious and error-prone process. Therefore, it is important to dynamically monitor the CPS system to learn its common behaviors and formulate invariants for detecting security attacks. Existing techniques for invariant mining only incorporate data and events, but not time. However, time is central to most CPS systems, and hence incorporating time is essential for achieving low false-positives and false-negatives. This paper proposes ARTINALI, which mines dynamic system properties by incorporating time into other important properties of the system. We demonstrate ARTINALI-based Intrusion Detection Systems (IDS) for two case studies, namely smart meters and smart medical devices, and measure their overheads.
Non-orthogonal Multiple Access for the Internet of Things
My research is concerned with medium access control optimization for the Internet of Things (IoT). The IoT is the concept of connecting IoT devices such as sensors, alarms, healthcare monitoring devices, smart vehicles, and wearables to the Internet with minimal human intervention. These devices require supporting a massive number of connections simultaneously and energy efficiency. In order to satisfy these requirements, I work on optimizing medium access control algorithms to allocate the limited communication resources, i.e. bandwidth and power, efficiently in order to enable the 5th Generation (5G) cellular networks to support the IoT. I use different non-orthogonal multiple access schemes to enable multiple IoT devices to share the same radio communication resources to provide connectivity for a massive number of IoT devices. In addition, we work on designing the medium access control scheme to be flexible and dynamic to adapt to network changes using software-defined multiple access concept.
Mirza Sarwara
Transparent and Flexible Tactile Interface for Human Computer Interaction
The aim of my research is to develop a next generation flexible transparent tactile interface for human computer interaction that can work even while being physically deformed, a property necessary for a true wearable device. In addition to sensing touch, the device can detect the presence of a hovering finger, a stretch, and a bend. It is highly transparent and is fabricated using a simple and scalable process, and will be widely available and made using low-cost polymer materials. The working principle of the sensor is based on the well-established mutual capacitance technology used in current touch screen devices rendering it easily adaptable. The technology is being further enhanced to detect localized shear for electronic skin application. This would enable robot hands to effectively pick up fragile objects and even provide haptic feedback to surgeons using robotic arms for surgeries.
Cryohydrogeology - Modeling unsaturated water flow through heterogeneous waste rock under freezing conditions
Water is one Canada's most important resources and is vital to ecosystem sustainability. Stockpiles of waste rock generated by mining activities are often important sources of acid rock drainage, a challenging environmental issue. My research aims to quantify the numerous processes controlling infiltration through an experimental pile in the Northwest Territories based on the comprehension of flow mechanisms occurring through its four-meter-thick sediment cover and underlying waste rock. Numerical models will be used to interpret an extensive data set accumulated over a period of eight years. My objective is to advance the understanding of groundwater flow in typical Nordic settings. In northern areas of Canada, frequent unsaturated conditions, heterogeneity of waste rock, freeze-thaw cycles and the occurrence of permafrost all affect infiltration of water through stockpiles. The results of this work are expected to help mitigate environmental and economic risks of future geotechnical, mining or energy projects. Such risks include lake and river pollution, drinking water contamination, damage to fauna and flora’s habitat, as well as requirements for major investments in water treatment and remediation.
Resource Allocation and Content Caching In 5G Mobile Networks
Resource allocation, optimization, and content caching in mobile networks.
Underdetermined Blind Source Separation: Theory, Methods and Applications
In my Ph.D thesis I focus on developing novel underdetermined blind source separation (BSS) methods and appling these methods in real-world applications. To overcome limitations of currently available EMD-BSS based methods and recover the underlying source signals accurately, in my first project I propose a novel blind source separation framework. This framework combines noise-assisted multivariate empirical mode decomposition and multiset canonical correlation analysis. Upon applying the proposed method on the nano-sensor data, we found that the proposed framework can achieve better performance than other state-of-the-art approaches. Existing BSS approaches are mainly designed for a single dataset BSS or the determined joint BSS problems (i.e., the number of sensors is greater than the number of sources for each dataset). To fill this gap, the main technical objective of my thesis focuses on developing underdetermined joint blind souce separation (UJBSS) approaches. In my second project, I exploit the second-order statistics of observations and introduce a novel UJBSS method which can extract the buried sources jointly from two datasets. Considering the dependence information between two datasets, the problem of jointly estimating the mixing matrices is tackled via canonical polyadic (CP) decomposition of a specialized tensor in which a set of spatial covariance matrices are stacked. Furthermore, the estimated mixing matrices are used to recover the sources from each dataset separately. I intend to further extend this idea to multiple datasets in a future paper.
Developing a Taxonomy of Technology-Mediated Adverse Events
Information technologies such as electronic health records, computerized order entry, and mobile communication devices are playing an ever-increasing role in the delivery of healthcare, and there is a growing body of evidence that suggests these technologies contribute to better patient outcomes. However, early research has also identified the potential for information technologies to contribute to adverse events. Little is known about the relationship between information technology and adverse events in clinical practice, particularly in a Canadian context. In partnership with the BC Patient Safety and Learning System (BCPSLS), my research aims to identify and articulate the ways in which information technology in healthcare can also inadvertently lead to adverse events.