- SIAT News
- Body RemiXer: SIAT PhD students launch a new VR installation
- Convocation Spotlight: Mirjana Prpa
- SIAT MA candidate Meta Vaughan awarded this year's Terry Fox Gold Medal
- Using Data Visualization to Quickly Forecast Avalanches and Enhance Public Safety ↗
- Convocation Spotlight: Yasamin Heshmat
- Convocation Spotlight: Naghmi Shireen
- Rethinking VR for the benefit of society ↗
- Exploring VR for the health sector
- Rui shares his graduate experience
- Designing VR for Self-Transcendence
- Erick shares his graduate experience
- Speculating on everyday computational things
- Alumni Spotlight Audrey Desjardins
- Next generating drawing systems in VR
- Grad Student Spotlight Reese Muntean
- ACM IDC 2019 Design Contest Finalists ... The Future of Children's Future Well-Being
- Grad student spotlight: Weina Jin
- Research & Exhibitions
- Project & Story Submission
- Staff & faculty resources
- Fall 2020 Showcase Submissions
Grad student spotlight: Weina Jin
Weina Jin is a 3rd-year PhD student in SIAT. Her thesis has a focus on developing explainable AI (artificial intelligence) to support doctors' decision-making processes. As a research assistant in supervisor Dr. Diane Gromala’s Pain Studies Lab, she designs virtual reality technologiesfor chronic pain patients. We sat down with Weina to talk about her research, her experience at SIAT, and her advice for future grad students.
What motivated you to pursue graduate school?
My original motivation to pursue graduate school was that I wanted to dramatically change my career and spend time in the fields of technology and design. When I was admitted to SIAT four years ago, I was expecting to earn a degree and some new skills so that I could find a good job after graduation.
After consistent learning, discovering, trying and failing, I am now lucky enough to find myself on the path toward discovering and pursuing my intrinsic interest, which brings me more enjoyment and satisfaction than the extrinsic motivations such as a degree or a job. For me, that’s the serendipity of being at graduate school.
Why did you choose SFU’s School of Interactive Arts & Technology?
SIAT is a unique place in that it is an interdisciplinary and research-oriented program. Although the disciplines in SIAT vary from humanities, arts, and design to computer science, engineering, psychology, neuroscience, and healthcare, there is a common theme across the various disciplines in SIAT: human beings.
Technology, design or the arts are all different ways to understand people. Creating new algorithms is hard, but understanding people is even harder. Unlike traditional technology-oriented programs that focus on the algorithm only and reduce the complexity of the problem by ignoring the human factor, in SIAT we tackle the problems simultaneously, which makes the research even more challenging.
To tackle the complex problem of people + technology, doing interdisciplinary research is not an end in itself, but a means an end. In SIAT, the boundaries or barriers among disciplines are blurry, and we are used to seeing researchers with multiple backgrounds.
This is rewarding because the research problem is thus no longer fragmented through the lens of a single discipline.
How would you describe your transition into grad school?
When I joined SIAT, I was so excited to embrace my new environment. For a while, I got busy fulfilling short-term goals, such as course assignments, conference deadlines, project presentations, and scholarship applications. Gradually, I realized I had lost my original interest when replacing some extrinsic goals (such as publications) as my ultimate goals. When it was time to decide my thesis research topic, I didn’t know what I was going to do.
Professor Gromala encouraged me to keep a diary on things I am interested in and try to distill the common attributes. I did so, and although it was very slow and difficult, after almost a year I reached my thesis topic.
What is one of the highlights of your PhD experience at SIAT?
One of my favourite projects is LikeMind. This is a video game designed to address the stigma of depression. This was my first project in SIAT. Before my admission, I worked in the healthcare domain for several years. Through these experiences, I realized that many medical issues cannot simply be solved by applying knowledge within the medical domain.
In our lab, I began to better see how games can be repurposed beyond pure entertainment. In addition to my medical background, I have longstanding interests in art, design and technology. When I arrived at SIAT, I dove into the program and began to explore how to use tools in technologies and arts to tackle medical problems.
What advice would you give to those considering graduate school?
To get the most out of the graduate school, ask yourself these questions when deciding if graduate school is right for you:
1) Do I feel autonomy in my decision or am I being largely influenced by external motives (others’ expectations, a degree for the sake of it, money, finding a job).
2) Can I feel relatedness in the research lab? Is the research lab a supportive environment for my pursuit or interest, for my enhancement of skills, for me to have freedom in my research decisions?
What are your future aspirations?
Before joining SIAT, I received my Doctor of Medicine (MD) in Neurology and had worked in a hospital as well as pharmaceutical and technological companies. After experiencing different jobs, I discovered that being a researcher in developing AI and HCI (human-computer interaction) technologies for healthcare was the best route for me.
Doing research in the interdisciplinary field of AI, HCI and medicine also fits my interests pretty well. I enjoy the problem-solving process, which is a common attribute in research, medicine and programming; and I love the human focus in medicine and HCI.
For example, my PhD thesis deals with how doctors use AI and how to design AI for doctor-AI collaboration. I am also very interested in the human mind; that’s why I chose neurology in the first place with my specialty in medical training. AI is another approach by reverse engineering the neural network. My thesis is engaged in understanding the decision-making process for both natural intelligence (the doctors) and artificial intelligence and how can both AI and doctors learn better by explaining.