Relevant Thesis-Based Degree Programs
Affiliations to Research Centres, Institutes & Clusters
My research lies at the intersection of human-computer interaction (HCI), computer-supported cooperative work (CSCW), computer-mediated communication (CMC), and educational technology. My primary research focus is to build rich collaboration systems that offer expressive multimodal interactions, i.e., interactions through multiple communication channels (e.g., speech, gesture, and grasp). My design approach translates natural human interactions into novel combinations of input modalities that serve as building blocks for fluid, rich, and lightweight interfaces. My evaluation approach deploys and evaluates high-fidelity systems in real world contexts (e.g., classrooms), from which we can obtain ecologically valid user data.
Complete these steps before you reach out to a faculty member!
- Familiarize yourself with program requirements. You want to learn as much as possible from the information available to you before you reach out to a faculty member. Be sure to visit the graduate degree program listing and program-specific websites.
- Check whether the program requires you to seek commitment from a supervisor prior to submitting an application. For some programs this is an essential step while others match successful applicants with faculty members within the first year of study. This is either indicated in the program profile under "Admission Information & Requirements" - "Prepare Application" - "Supervision" or on the program website.
- Identify specific faculty members who are conducting research in your specific area of interest.
- Establish that your research interests align with the faculty member’s research interests.
- Read up on the faculty members in the program and the research being conducted in the department.
- Familiarize yourself with their work, read their recent publications and past theses/dissertations that they supervised. Be certain that their research is indeed what you are hoping to study.
- Compose an error-free and grammatically correct email addressed to your specifically targeted faculty member, and remember to use their correct titles.
- Do not send non-specific, mass emails to everyone in the department hoping for a match.
- Address the faculty members by name. Your contact should be genuine rather than generic.
- Include a brief outline of your academic background, why you are interested in working with the faculty member, and what experience you could bring to the department. The supervision enquiry form guides you with targeted questions. Ensure to craft compelling answers to these questions.
- Highlight your achievements and why you are a top student. Faculty members receive dozens of requests from prospective students and you may have less than 30 seconds to pique someone’s interest.
- Demonstrate that you are familiar with their research:
- Convey the specific ways you are a good fit for the program.
- Convey the specific ways the program/lab/faculty member is a good fit for the research you are interested in/already conducting.
- Be enthusiastic, but don’t overdo it.
G+PS regularly provides virtual sessions that focus on admission requirements and procedures and tips how to improve your application.
ADVICE AND INSIGHTS FROM UBC FACULTY ON REACHING OUT TO SUPERVISORS
These videos contain some general advice from faculty across UBC on finding and reaching out to a potential thesis supervisor.
Graduate Student Supervision
Master's Student Supervision
Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.
Modern large-scale language models approach the quality of human-level writing. This promises the advent of Artificial Intelligence (AI) writing companions performing AI-led writing under human control, surpassing traditional writing tools limited to revision and ideation supports. However, human-AI co-writing may endanger writers’ control, autonomy, and ownership by overstepping co-creative boundaries. My research team and I first conducted a design workbook study with 7 hobbyists and 13 professional writers and elicited three sets of primary barriers to the adoption of human-AI co-writing. Storywriters desire retaining control over writing rather than letting AI take the lead when they (1) prioritize emotional values in turning ideas into words over the productivity of AI-generated writing; (2) have high self-confidence and distrust AI in challenging sub-tasks (e.g., creating characters and dialogue); and (3) expect the AI control mechanism to mismatch their writing strategies. To expand on the results of the design workbook study, I led the design of two AI-assisted writing tools which support UX scenario writing. I subsequently conducted a summative evaluation of these systems. Participants prioritized productivity and reacted positively to both systems, perceiving them as Creativity Support Tools (CST)’s for achieving prolific writing. The results reveal how the tool design interacted with designers’ value structure: both systems allowed them to maintain their integrity and enhance their enjoyment. Additionally, increased control promoted a greater sense of ownership and satisfaction, while adding additional overhead for the designer. This work lays the groundwork for AI companions that respect storywriters’ personal values and writing methods.
Pull Requests (PRs) are a frequently used method for proposing changes to source code repositories. When discussing proposed changes in a PR discussion, stakeholders often reference a wide variety of information objects for establishing shared awareness and common ground. Previous work has not considered how the referential behavior impacts collaborative software development via PRs. This knowledge gap is the major barrier in evaluating the current support for referencing in PRs and improving them. We conducted an explorative analysis of ~7K references, collected from 450 public PRs on GitHub, and constructed taxonomies of referent types and expressions. Using our annotated dataset, we identified several patterns in the use of references. We found that despite a prevalent use of references in PR discussions, GitHub's interface lacks the support for referencing the majority of information types. We provide qualitative descriptions of how different contextual factors shape the use of references in discussions. We also discovered distinct referencing patterns in merged and closed PRs which signifies a potential ground for future research to establish a relationship between reference use and PR outcomes. These findings suggest that what is and is not referenced within a PR discussion has an important impact on the software development process, and warrants continued platform support and research. We conclude with design implications to support more effective referencing in PR discussion interfaces. Supplementary materials available at: http://hdl.handle.net/2429/77435.
In human-computer interaction (HCI) studies, bias in the gender representation of participants can jeopardize the generalizability of findings, perpetuate bias in data driven practices, and make new technologies dangerous for underrepresented groups. Key to progress towards inclusive and equitable gender practices is diagnosing the current status of bias and identifying where it comes from. In this mixed-methods study, we interviewed 13 HCI researchers to identify the potential bias factors, defined a systematic data collection procedure for meta-analysis of participant gender data, and created a participant gender dataset from 1147 CHI papers. Our analysis provided empirical evidence for the underrepresentation of women, the invisibility of non-binary participants, deteriorating representation of women in MTurk studies, and characteristics of research topics prone to bias. Based on these findings, we make concrete suggestions for promoting inclusive community culture and equitable research practices in HCI. Supplementary materials available at: http://hdl.handle.net/2429/77303.
Throughout the COVID-19 pandemic, older adults have been encouraged to stay indoors and isolated, leading to potential disruptions in their social activities and interpersonal relationships. We conducted an interview study (N=24) to examine older adults' technology adoption and communication practices in light of new circumstances related to the pandemic. Our interviews revealed that the pandemic motivated many older adults to learn new technology and become more tech-savvy in an effort to stay connected with others. However, they also reported challenges related to the pandemic that were major impediments to technology adoption. These were: (1) lack of access to in-person technology support under physical distancing mandates, (2) lack of opportunities for online participation due to negative age stereotypes and assumptions, and (3) increased apprehension to seek help from family members and friends who were suffering from pandemic-related stresses. This study extends technology adoption literature and contributes an up-to-date examination of the "grey digital divide" (the gap between older adults who use technology and those who do not). Our findings demonstrate that despite the rapidly increasing number of tech-savvy seniors, a digital divide not only persists, but has been exacerbated by the transition to virtual-only offerings. We reveal the challenges and coping strategies of older adults who remain separated from technology, and propose actionable solutions to increase digital access during the COVID-19 pandemic and beyond.
With substantial industrial interests, conversational voice user interfaces (VUIs) are becoming ubiquitous through devices that feature voice assistants such as Apple’s Siri and Amazon Alexa. Naturalness is often considered to be central to conversational VUI designs as it is associated with numerous benefits such as reducing cognitive load and increasing accessibility. The literature offers several definitions for naturalness, and existing conversational VUI design guidelines provide different suggestions for delivering a natural experience to users. However, these suggestions are hardly comprehensive and often fragmented. A precise characterization of naturalness is necessary for identifying VUI designers’ needs and supporting their design practices. To this end, we interviewed 20 VUI designers, asking what naturalness means to them, how they incorporate the concept in their design practice, and what challenges they face in doing so. Through inductive and deductive thematic analysis, we identify 12 characteristics describing naturalness in VUIs and classify these characteristics into three groups, which are ‘Fundamental’, ‘Transactional’ and ‘Social’ depending on the purpose each characteristic serves. Then we describe how designers pursue these characteristics under different categories in their practices depending on the contexts of their VUIs (e.g., target users, application purpose). We identify 10 challenges that designers are currently encountering in designing natural VUIs. Our designers reported experiencing the most challenges when creating naturally sounding dialogues, and they required better tools and guidelines. We conclude with implications for developing better tools and guidelines for designing natural VUIs.
Speech shadowing, i.e., listening to some audio and simultaneously vocalizing the words, is a popular language-learning technique that is known to improve listening skills. However, despite strong evidence for its efficacy as a listening exercise, existing software tools do not adequately support listening-focused shadowing practice, especially in self-regulated learning environments with no external feedback. To bridge this gap, we introduce CAST, a shadowing system that makes self-regulation easy and effective through four novel interface features — contextual blurring for inducing self-reflection on misheard portions, in-situ annotations for self-monitoring progress through tracking and visualization, embedded recordings for post-practice self-evaluation, and adjustable pause-handles for self-paced practice. We base CAST on a formative user study (N=15) that provides fresh empirical grounds on the needs and challenges of those who practice shadowing using conventional software tools. We validate our design through a summative evaluation (N=12) that shows learners can successfully self-regulate their shadowing practice with CAST while retaining focus on listening.
If this is your researcher profile you can log in to the Faculty & Staff portal to update your details and provide recruitment preferences.