Fine Grain Data is a series of interviews with our senior research scientists conducted by our communications intern, Megan Ciszek. The aim of the series is to get to know the people who make up ACTNext, and explore some of the motivations and thoughts driving their work on developing the next generation of tools for learning and assessment.
We invite you to check back frequently over the coming weeks as we post the rest of the Fine Grain Data Interviews. This week, ACTNext Sr. Research Scientist Yuchi Huang shares his thoughts on the projects he’s working on, his personal research aspirations, and more!
What brought you to ACTNext?
I’ve always had a passion for education and educational research. ACTNext is an institute which integrates different disciplinary research to deliver an innovative environment for educational research – this aligns with my research interest and expertise
What do you do at ACTNext?
At ACTNext, I am a senior research scientist and I will be the tech lead on the machine learning research in education
What are some of the projects you are currently working on?
The projects that I am working on include the Micro-content generation, which includes item generation, handwriting recognition, and automatic scoring for essays, automatic item tagging, assessing of social and emotional learning skills, etc.
What is your personal research mission/vision/aspiration? Goals?
My personal research aspiration is to bridge the gap between machine learning/deep learning and educational research. Despite the success of machine learning and deep learning in a lot of applications, many researchers in education are not familiar with the methods in these two fields. My purpose is fill the gap and make machine learning techniques accessible to the community of education and push innovations forward in educational research with my expertise
How does this align with ACT and ACTNext’s mission?
As mentioned above, ACTNext integrates interdisciplinary research in computational psychometrics, machine learning and multi-modal analytics to deliver innovative learning and assessment systems to help people and organizations worldwide achieve education & workplace success. Machine learning and deep learning will play an important role in educational research – that is to say, ACTNext’s mission aligns well with my research aspiration
What are your personal research interests?
My personal research interests includes different aspects of machine learning, deep learning and artificial intelligence research for educational applications – like computer vision, natural language processing, and multi-modal data analysis
At this point in your career, what are you most proud of professionally?
- Led research and development on facial bio-metrics for test centers. Developed a deep learning-based algorithm that outperforms a third-party face recognition system that once deployed at test centers would result in signiﬁcant cost savings
- Led research on generating photo-realistic video sequences of avatars for human-agent interaction based on generative adversarial networks
- Led externally-funded programs on CPS (Collaborative Problem Solving) of team skills by utilizing multi-modal information and machine learning models
What future trends do you see in your field, and are there any that you are particularly excited or concerned about?
Two big trends: 1) Deep learning has been seceding in almost every sub-field of machine learning applications and is creating numerous opportunities to subvert traditional approaches 2) Multi-modal data integrated and processed by machine learning algorithms in all practical applications
How do you see yourself contributing to these trends?
I would contribute my expertise to deep learning-based educational applications. Most of these applications will be based on multi-modal data and we will have a chance to utilize multi-modal data integration techniques
What is a fun/interesting fact about you?
I love art and music. I was a choir member at my college when I was an undergraduate student. I am also proficient in the art of Chinese calligraphy