Greetings

I'm a 4th-year Ph.D. candidate in Computer Science at the University of Toronto, working under the supervision of Prof. Khai Truong.

My research centers human-AI interaction, with an emphasis on accessibility and creativity support, particularly in enhancing "music accessibility" for d/Deaf and hard-of-hearing individuals. One of my main projects involves song signing to support culturally responsive content creation and encourage collaboration between d/Deaf and non-d/Deaf artists. Another aspect of my work focuses on enhancing people's well-being. I am engaged in projects that support individuals with dementia in their out-of-home experiences and encourage mindful eating behaviours among children.

I completed my B.Sci in Computer Science and Engineering at Ewha Womans University, where I was advised by Prof. Uran Oh (Human-Computer Interaction Lab) and Prof. Hyokyung Bahn (Distributed Computing and Operating System Lab). Additionally, I worked as a research intern at the Samsung AI Centre Toronto under the guidance of Dr. Iqbal Mohomed, and at NAVER AI (HCI group) with Dr. Young-Ho Kim.
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Artificial Intelligence: POS tagging & Word Embedding (Spring, 2020)

Class: Artificial Intelligence
Date: Spring 2020
Professor: Hyunsuk Park
Main Idea: POS Tagging with Decision Tree, Word Embedding
Methods: CBOW and SKIPGRAM
Results: 


First of all, I completed building an artificial neural network model using GENIA for POS tagging. POS tagging was applied to GnI papers using decision trees.

The frequency of each part-of-speech, the type of preceding word, etc. were identified and visualized in a word cloud, and used for chunking.

Next is “word embedding”. 1. Direct model design using tensorflow 2. Modeling using Word2Vec 3. Google model on the model created in the previous 2Modeling by crossing,

A total of 3 modeling methods were conducted.

Compared word vectors made of SKIP-Gram and CBOW.





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