Tianyi Zhang

schoolPurdue Universitybar_chart#387 Global Rank

Total Papers

HCI Rank

#387

Research Areas

Years Active

Recent Publications

HEPHA: A Mixed-Initiative Image Labeling Tool for Specialized Domains

Image labeling is an important task for training computer vision models. In specialized domains, such as healthcare, it is expensive and challenging to recruit specialists for image labeling. We propose HEPHA, a mixed-initiative image labeling tool that elicits human expertise via inductive logic learning to infer and…

IUI '255th+ AuthorHuman-LLM Collaboration +1

Text-to-SQL Domain Adaptation via Human-LLM Collaborative Data Annotation

Text-to-SQL models, which parse natural language (NL) questions to executable SQL queries, are increasingly adopted in real-world applications. However, deploying such models in the real world often requires adapting them to the highly specialized database schemas used in specific applications. We observe that the per…

CHI '25CorrespondingHuman-LLM Collaboration +2

Dango: A Mixed-Initiative Data Wrangling System using Large Language Model

Data wrangling is a time-consuming and challenging task in the early stages of a data science pipeline. However, existing tools often fail to effectively interpret user intent. We propose Dango, a mixed-initiative multi-agent system that helps users generate data wrangling scripts. Compared to existing tools, Dango en…

UIST '24CorrespondingExplainable AI (XAI) +1

SQLucid: Grounding Natural Language Database Queries with Interactive Explanations

Though recent advances in machine learning have led to significant improvements in natural language interfaces for databases, the accuracy and reliability of these systems remain limited, especially in high-stakes domains. This paper introduces SQLucid, a novel user interface that bridges the gap between non-expert us…

CHI '24CorrespondingGenerative AI (Text, Image, Music, Video) +2

PromptCharm: Text-to-Image Generation through Multi-modal Prompting and Refinement

The recent advancements in Generative AI have significantly advanced the field of text-to-image generation. The state-of-the-art text-to-image model, Stable Diffusion, is now capable of synthesizing high-quality images with a strong sense of aesthetics. Crafting text prompts that align with the model's interpretation…

CHI '24CorrespondingAI Ethics, Fairness & Accountability +1

STILE: Exploring and Debugging Social Biases in Pre-trained Text Representations

The recent success of Natural Language Processing (NLP) relies heavily on pre-trained text representations such as word embeddings. However, pre-trained text representations may exhibit social biases and stereotypes, e.g., disproportionately associating gender with occupations. Though prior work presented various bias…

CHI '24CorrespondingHuman-LLM Collaboration +2

Is Stack Overflow Obsolete? An Empirical Study of the Characteristics of ChatGPT Answers to Stack Overflow Questions

Q&A platforms have been crucial for the online help-seeking behavior of programmers. However, the recent popularity of ChatGPT is altering this trend. Despite this popularity, no comprehensive study has been conducted to evaluate the characteristics of ChatGPT’s answers to programming questions. To bridge the gap, we…

UIST '23CorrespondingExplainable AI (XAI) +1

MIWA: Mixed-Initiative Web Automation for Better User Control and Confidence

In the era of Big Data, web automation is frequently used by data scientists, domain experts, and programmers to complete time-consuming data collection tasks. However, developing web automation scripts requires familiarity with a programming language and HTML, which remains a key learning barrier for non-expert users…

CHI '23CorrespondingExplainable AI (XAI) +1

DeepSeer: Interactive RNN Explanation and Debugging via State Abstraction

Recurrent Neural Networks (RNNs) have been widely used in Natural Language Processing (NLP) tasks given its superior performance on processing sequential data. However, it is challenging to interpret and debug RNNs due to the inherent complexity and the lack of transparency of RNNs. While many explainable AI (XAI) tec…

CHI '23CorrespondingExplainable AI (XAI) +2

DeepLens: Interactive Out-of-distribution Data Detection in NLP Models

Machine Learning (ML) has been widely used in Natural Language Processing (NLP) applications. A fundamental assumption in ML is that training data and real-world data should follow a similar distribution. However, a deployed ML model may suffer from out-of-distribution (OOD) issues due to distribution shifts in the re…

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