I am currently on the job market! I am interested in both academia and industry positions. If you know of any openings that you think I might be a good fit for, please feel free to contact me. You can find my CV here.
I'm a Ph.D. Candidate at the Human-Computer Interaction Institute at Carnegie Mellon University, advised by Prof. Jason Hong. My main research interest lies at the intersection of Human-Computer Interaction, Security and Privacy, and Software Engineering. Specifically, I conduct two types of research:
- Understanding type of research: I conduct qualitative and quantitative studies to understand developers' challenges in creating privacy-respectful apps, e.g., developer interviews about privacy nutrition labels, analyzing privacy-related discussions on r/androiddev;
- Design and System type of research: I design and build developer tools to help developers improve the protection of users' privacy and comply with privacy regulations and platform policies, e.g., Coconut and Honeysuckle (Android Studio IDE plugins for privacy).
In addition to my primary focus in developer privacy research, I have always yearned to apply my "Understanding + Building" research methodology to tackle more real-world problems. Thus, I have side projects in investigating the adoption problem of COVID-19 contact-tracing apps, using AI to improve text entry and notification management, and using social media data to help people combat natural disasters.
News05/2022 📄 One paper is accepted to TOCHI 2022.
03/2022 🍀 Proposed my Ph.D. dissertation "Privacy-Enhancing Development Environment"
02/2022 📄 One paper is accepted to PETS 2022.
01/2022 👩⚖️ I started serving on the CHI 2022 Late-Breaking Work (LBW) Program Committee.
11/2021 📄 One paper is accepted to CHI 2022. (updates on 04/2022: My CHI'22 paper on developers' challenges about creating privacy nutrition labels won an honorable mention award!)
11/2021 📄 One paper is accepted to PETS 2022.
08/2021 🏆 I am awarded the 2021 CyLab Presidential Fellowship.
07/2021 📄 One paper is accepted to IMWUT 2021.
06/2021 📄 One paper is accepted to TOCHI 2021.
06/2021 📄 One paper is accepted to the Special Issue “IoT for Fighting COVID-19” of Elsevier Pervasive and Mobile Computing (PMC) Journal.
04/2021 👩⚖️ I started serving on the USENIX Security '22 Program Committee.
08/2020 📄 One paper is accepted to CSCW 2020.
PublicationsThe complete list of my publications can be found on my Google Scholar page.
C-PAK: Correcting and Completing Variable-length Prefix-based Abbreviated Keystrokes [ACM Link]
Tianshi Li, Philip Quinn, Shumin Zhai.
Understanding iOS Privacy Nutrition Labels: An Exploratory Large-Scale Analysis of App Store Data [preprint] [ACM Link]
CHI LBW 2022
Yucheng Li, Deyuan Chen, Tianshi Li, Yuvraj Agarwal, Lorrie Faith Cranor, Jason I. Hong.
Understanding Challenges for Developers to Create Accurate Privacy Nutrition Labels. [preprint] [ACM Link]
Tianshi Li, Kayla Reiman, Yuvraj Agarwal, Lorrie Faith Cranor, Jason I. Hong.
🏅 Best Paper Honorable Mention Award
Charting App Developers’ Journey Through Privacy Regulation Features in Ad Networks.
PETS 2022 (to appear)
Mohammad Tahaei, Kopo M. Ramokapane, Tianshi Li, Jason I. Hong, Awais Rashid
Understanding Privacy-Related Advice on Stack Overflow. [preprint]
PETS 2022 (to appear)
Mohammad Tahaei, Tianshi Li, Kami Vaniea.
Honeysuckle: Annotation-Guided Code Generation of In-App Privacy Notices [ACM link]
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. [ACM link]
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 “IoT 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]
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.