Bio

Jasmine Lu is a Computer Science Ph.D. candidate at the University of Chicago. Through her work, she explores how we might build more sustainable computing ecosystems. Her research focuses on the issue of electronic waste, exploring new strategies to mitigate it or better recycle it. This includes building interactive tools to assist with electronic waste reuse, understanding existing networks of reuse practices, and designing interactions around caring for our devices. Jasmine’s work has been supported by University of Chicago Climate and Sustainable Growth Institute grants and the NSF Graduate Research Fellowship. Her work has also been recognized with an ACM SIGCHI Special Recognition for “pioneering research in ecological HCI through novel hardware interfaces and fostering communities around sustainable computing.”

(Re)computing electronic waste: computational methods to reduce, reuse, recycle e-waste

Computing innovations have created a world where devices facilitate almost every part of our lives. However, such pervasive computing infrastructure has also led to a tremendous amount of electronic waste. In my research, **I create computational tools that augment our ability to reduce, reuse, recycle, and repair electronic waste**, which I call **recomputing e-waste**.

E-waste is the fastest growing waste stream in the world with 62 billion kg of e-waste generated in 2022 (projected to increase to 82 billion kg in 2030). Current approaches to handling e-waste (collection and recycling) simply have not innovated to keep up with the rapid production of e-waste. In parallel, the computing industry’s paradigm of infinite growth across manufacturing and innovation will soon hit a wall as it becomes more difficult to mine important resources (Lithium, Silicon, etc.). Given such urgency, the field of computing needs to reinvent its approach to managing its waste.

During my PhD, I have addressed this issue primarily in two ways: (1) exposing the latent usefulness of standard e-waste through the development of computational tools to enable reuse and (2) developing designs/tools that empower users towards different relations with their electronic devices. My research translates methods from computer vision, machine learning, electrical engineering, and recommender systems to develop new processes of managing e-waste that are informed by how computing devices are designed.

My ongoing and future work aims to expand towards developing scalable infrastructure to make past, present, and future computing material conducive to reuse through approaches in both technical systems building and sociotechnical analysis. In doing so, my work builds towards a sustainable future of recycling, reusing, repairing, and reducing the waste generated by our computing infrastructures.