Yicong Huang: Building Data Systems to Broaden the Access of Data Science, AI, and ML

Abstract

In an era where data-driven decision-making shapes industries, governments, and everyday life, the ability to leverage data science has become an essential skill. Modern data science tools—encompassing data collection, analysis, and advanced techniques such as artificial intelligence (AI), machine learning (ML), and large language models (LLMs)—play a critical role across diverse fields. However, many of these tools rely heavily on programming expertise, which limits their accessibility to a broader audience. In this talk, I will discuss my work on Texera, an open-source system designed to make data science, AI, and ML accessible to everyone. Texera features a low-code and even no-code workflow interface, enabling users of varying technical backgrounds to engage in data science. It emphasizes cloud-based collaboration for data science, enabling multiple users to seamlessly work on the same shared execution, much like the collaborative experiences offered by Google Docs and Overleaf. I will discuss the design choices behind our actor-based parallel engine for executing data science workflows. I will also highlight my works on the system’s innovative features for interacting with data workflow executions, focusing on debugging capabilities that improve transparency and enhance usability. To conclude, I will outline future research directions aimed at developing a comprehensive ecosystem that integrates advanced interfaces and intelligent systems, enhancing accessibility, efficiency, and user empowerment in data science.

Bio

Yicong Huang is a final-year Ph.D. candidate from the Information Systems Group (ISG), Computer Science Department, University of California, Irvine.  Under the guidance of Dr. Chen Li, his research focuses on big data management, data-processing systems, and machine learning systems. Yicong has made significant contributions in the Texera project. He has published in top-tier database venues such as VLDB, SIGMOD and ICDE. His interdisciplinary reach spans venues like TOCHI, PNAS Nexus, JAMIA, AMIA, and PloS ONE. Yicong completed research internships at Bytedance, VISA, and Observe, and contributed to patents and papers. His research earned a Best Demo Runner-Up Award at SIGMOD 2024. He received honors such as the 2024 Graduate Dean’s Dissertation Fellowship and the 2023 Public Impact Fellowship from UCI. For more information about his work, please visit https://yicong-huang.github.io.