Tianshi Li header image

About me

I'm a Ph.D. student at the Human-Computer Interaction Institute at Carnegie Mellon University, advised by Prof. Jason Hong. My main research interest focuses on helping developers make privacy-friendly apps. I am particularly interested in understanding why people create apps that violate privacy and building tools for developers to make protecting user's privacy an easier task. I also work on side projects about the adoption problem of COVID-19 contact-tracing apps and rethinking the role of AI in some classic interaction design research such as text entry and notification management.


Jul-2021πŸ“„ One paper accepted to IMWUT 2021.
Jun-2021πŸ“„ One paper accepted to TOCHI 2021.
Jun-2021πŸ“„ Our paper investigating factors that affect U.S. people's intentions to install and use COVID-19 contact-tracing apps has been accepted to the Special Issue β€œIoT for Fighting COVID-19” of Elsevier Pervasive and Mobile Computing (PMC) Journal.
Apr-2021πŸ‘©β€βš–οΈ I've accepted the invitation to serve on USENIX Security '22 Program Committee.
Aug-2020πŸ“„ Our paper studying Android developers' attitudes and practices about privacy by analyzing personal-data-related discussions on r/androiddev has been accepted to CSCW 2020.
Apr-2020πŸ“„ We started investigating the adoption issue of contact-tracing apps, a promising technology to help combat the global pandemic. Our paper summarizing the early findings from an MTurk survey has been released on arXiv.
Oct-2019βš™οΈ  We have released the source code of Coconut and provided a quick start guide. Looking forward to seeing more cool ideas built on top of Coconut to help developers better handle privacy-related tasks!
Oct-2018πŸ“„ Our paper on Coconut, an IDE plugin to help Android developers write privacy-friendly apps has been accepted to IMWUT 2018.


The complete list of my publications can be found on my Google Scholar page.

Honeysuckle: Annotation-Guided Code Generation of In-App Privacy Notices
IMWUT 2021/ UbiComp 2021
Tianshi Li, Elijah B Neundorfer, Yuvraj Agarwal, Jason I. Hong

Alert Now or Never: Understanding and Predicting Notification Preferences of Smartphone Users
TOCHI 2021
Tianshi Li, Julia Haines, Miguel Flores Ruiz de Eguino, Jason I. Hong, Jeffrey Nichols

What Makes People Install a COVID-19 Contact-Tracing App? Understanding the Influence of App Design and Individual Difference on Contact-Tracing App Adoption Intention. [Elsevier link] [arXiv preprint]
Special Issue β€œIoT for Fighting COVID-19” of Elsevier Pervasive and Mobile Computing (PMC) Journal.
Tianshi Li, Camille Cobb, Jackie (Junrui) Yang, Sagar Baviskar, Yuvraj Agarwal, Beibei Li, Lujo Bauer, Jason I. Hong.

How Developers Talk about Personal Data and What It Means for User Privacy: A Case Study of a Developer Forum on Reddit. [ACM link] [preprint] [5-minute talk]
CSCW 2020.
Tianshi Li, Elizabeth Louie, Laura Dabbish, Jason I. Hong.

Decentralized is not risk-free: Understanding public perceptions of privacy-utility trade-offs in COVID-19 contact-tracing apps. [arXiv preprint] [project website]
in arXiv preprint 2020.
Tianshi Li, Jackie (Junrui) Yang, Cori Faklaris, Jennifer King, Yuvraj Agarwal, Laura Dabbish, Jason I. Hong.

Analyzing the Role of General App Creators in Protecting the Privacy of Vulnerable Populations. [pdf]
CHI 2020 Workshop: Privacy and Power: Acknowledging the Importance of Privacy Research and Design for Vulnerable Populations.
Tianshi Li, Jason I. Hong.

Demystifying Complex Workload–DRAM Interactions: An Experimental Study. [arXiv preprint]
Saugata Ghose, Tianshi Li, Nastaran Hajinazar, Damla Senol Cali, Onur Mutlu

Coconut: An IDE Plugin for Developing Privacy-Friendly Apps. [pdf] [project website] [talk slides] [news story] [demo video]
IMWUT 2018/ UbiComp 2019.
Tianshi Li, Yuvraj Agarwal, Jason I. Hong.

Using ECC DRAM to Adaptively Increase Memory Capacity [arXiv preprint].
in arXiv preprint 2017.
Yixin Luo, Saugata Ghose, Tianshi Li, Sriram Govindan, Bikash Sharma, Bryan Kelly, Amirali Boroumand, Onur Mutlu.

Understanding Latency Variation in Modern DRAM Chips: Experimental Characterization, Analysis, and Optimization. [pdf]
Kevin K. Chang, Abhijith Kashyap, Hasan Hassan, Saugata Ghose, Kevin Hsieh, Donghyuk Lee, Tianshi Li, Gennady Pekhimenko, Samira Manabi Khan, Onur Mutlu.