Evaluating and Designing Computing Systems for the Future of Work

Speaker: Hancheng Cao
         Stanford University

Title: "Evaluating and Designing Computing Systems for the Future of Work"

Date:   Tuesday, 19 March 2024

Time:   11:00am - 12 noon

Zoom link:

Meeting ID: 966 8851 6988
Passcode: 202425


From collaborative software to generative AI, computing technologies are
redefining the way we work, communicate and collaborate. Yet with the
growing complexities of computing platforms, it becomes increasingly
challenging to foresee their impacts on human behavior, leading to not
only poor user experience but also problematic applications that mirror
and amplify societal issues. How can we better understand machine behavior
and machine-mediated user behavior over computing platforms? How can we
build applications that align with our needs and values with emerging
computing technologies? My research aims to answer these questions through
developing novel empirical measurements, technical methods and design. In
this talk, I will present my work demonstrating this approach in the
future of work context, where I have established data-driven, AI-powered
and human-centered methods to understand, evaluate and design computing
systems at the workplace. I will present an analysis of remote meeting
experience through mining millions of meetings, a study on how an AI
algorithm can be built to predict team fracture, and a development and
evaluation study on a generative AI-based scientific feedback system for
researchers. These projects exemplify the opportunities to leverage
computation to better understand, support and augment work practices. I
will conclude by discussing my ongoing and future research agenda on human
AI interaction and computational social science.


Hancheng Cao is a final year PhD candidate in computer science (with a PhD
minor in management science and engineering) at Stanford University
working with Prof. Daniel McFarland and Prof. Michael Bernstein. He works
in the field of human computer interaction and computational social
science, where he mines large-scale data, develops algorithms and builds
systems to study and augment human activities Recognized as a Stanford
Interdisciplinary Graduate Fellow, he has published 30 academic papers
across fields, with three works he led recognized as Best Paper (CHI 2023)
or Honorable Mention (CHI 2021, CSCW 2020) awards. His research has also
appeared in leading social science journals (e.g. American Sociological
Review). His research has been widely covered in the media, including
Wired, Forbes, New Scientist, TED among others.