Shitao Fang

schoolUniversity of Tokyobar_chart#2269 Global Rank

Total Papers

HCI Rank

#2269

Research Areas

Years Active

Recent Publications

What We Talk About When We Talk About Frameworks in HCI

In HCI, frameworks function as a type of theoretical contribution, often supporting ideation, design, and evaluation. Yet, little is known about how they are actually used, what functions they serve, and which scholarly practices that shape them. To address this gap, we conducted a systematic review of 615 papers f…

CUI '252nd AuthorElectrical Muscle Stimulation (EMS) +2

Multimodal Silent Speech-based Text Entry with Word-initials Conditioned LLM

Although exhibiting great potential in enabling seamless communication between humans and conversational agents, large vocabulary recognition is still challenging for silent speech interfaces. In this research, we propose a novel interaction technique that combines silent speech and typing to enable more efficient tex…

CHI '252nd AuthorConversational Chatbots +2

Proactive Conversational Agents with Inner Thoughts

One of the long-standing aspirations in conversational AI is to allow them to autonomously take initiatives in conversations, i.e. being proactive. This is especially challenging for multi-party conversations. Prior NLP research focused mainly on predicting the next speaker from contexts like preceding conversations.…

CHI '242nd AuthorGenerative AI (Text, Image, Music, Video) +2

Examining Human Perception of Generative Content Replacement in Image Privacy Protection

The richness of the information in photos can often threaten privacy, thus image editing methods are often employed for privacy protection. Existing image privacy protection techniques, like blurring, often struggle to maintain the balance between robust privacy protection and preserving image usability. To address th…

CHI '232nd AuthorBest PaperBrain-Computer Interface (BCI) & Neurofeedback +2

LipLearner: Customizable Silent Speech Interactions on Mobile Devices

Silent speech interface is a promising technology that enables private communications in natural language. However, previous approaches only support a small and inflexible vocabulary, which leads to limited expressiveness. We leverage contrastive learning to learn efficient lipreading representations, enabling few-sho…

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