Donghao Ren

schoolApplebar_chart#2221 Global Rank

Total Papers

HCI Rank

#2221

Research Areas

Years Active

Recent Publications

Exploring Empty Spaces: Human-in-the-Loop Data Augmentation

Data augmentation is crucial to make machine learning models more robust and safe. However, augmenting data can be challenging as it requires generating diverse data points to rigorously evaluate model behavior on edge cases and mitigate potential harms. Creating high-quality augmentations that cover these "unknown un…

CHI '243rd AuthorHuman-LLM Collaboration +1

Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiences

On-device machine learning (ML) promises to improve the privacy, responsiveness, and proliferation of new, intelligent user experiences by moving ML computation onto everyday personal devices. However, today's large ML models must be drastically compressed to run efficiently on-device, a hurtle that requires deep, yet…

CHI '225th+ AuthorBest PaperInteractive Data Visualization +2

Neo: Generalizing Confusion Matrix Visualization to Hierarchical and Multi-Output Labels

The confusion matrix, a ubiquitous visualization for helping people evaluate machine learning models, is a tabular layout that compares predicted class labels against actual class labels over all data instances. We conduct formative research with machine learning practitioners at Apple and find that conventional confu…

UIST '202nd AuthorInteractive Data Visualization +2

mage: Fluid Moves Between Code and Graphical Work in Computational Notebooks

We aim to increase the flexibility at which a data worker can choose the right tool for the job, regardless of whether the tool is a code library or an interactive graphical user interface (GUI). To achieve this flexibility, we extend computational notebooks with a new API mage, which supports tools that can represent…

CHI '203rd AuthorInteractive Data Visualization +2

Tempura: Query Analysis with Structural Templates

Analyzing queries from search engines and intelligent assistants is difficult. A key challenge is organizing queries into interpretable, context-preserving, representative, and flexible groups. We present structural templates, abstract queries that replace tokens with their linguistic feature forms, as a query groupin…

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