EDBT 2026 Demo / reviewers in the wild / expert

Zhao Lucis Li

dblp:212/5753 · DBLP profile ↗
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5ranked-venue papers
2as first author
1since 2021 · last 2023
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 3 · 2 first-authorComputer networks · 2 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
4 papers
Performance modeling and evaluation · 44% Cloud and datacenter computing · 30% Distributed systems · 26%
Network and information security
1 paper
Privacy and data protection · 100%
Databases, data mining, and information retrieval
2 papers
Machine learning and data management · 100%

Topics — the 6 heaviest of 8, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning and data management
learned database components		 0.522020
AutoSys: The Design and Operation of Learning-Augmented Systems · USENIX ATC 2020
Accelerating Rule-matching Systems with Learned Rankers · USENIX ATC 2019
Cloud and datacenter computing › datacenter operations
cloud system operations		 0.412020
AutoSys: The Design and Operation of Learning-Augmented Systems · USENIX ATC 2020
Privacy and data protection
mobile app privacy		 0.312018
Characterizing Privacy Risks of Mobile Apps with Sensitivity Analysis · IEEE Trans. Mob. Comput. 2018
Privacy and data protection
privacy risk assessment		 0.312018
Characterizing Privacy Risks of Mobile Apps with Sensitivity Analysis · IEEE Trans. Mob. Comput. 2018
Cloud and datacenter computing › quality of service
tail latency		 0.312018
Metis: Robustly Tuning Tail Latencies of Cloud Systems · USENIX ATC 2018
Software testing
test coverage		 0.112018
Characterizing Privacy Risks of Mobile Apps with Sensitivity Analysis · IEEE Trans. Mob. Comput. 2018

Methods — techniques the papers use, named apart from their topics

machine learning · 0.9learned rankers · 0.8sensitivity analysis · 0.7modular learning · 0.7robust tuning · 0.3
YearPublicationVenuePosition
2023 On Modular Learning of Distributed Systems for Predicting End-to-End Latency
Chieh-Jan Mike Liang, Zilin Fang, Yuqing Xie 0005, Fan Yang 0024, Zhao Lucis Li, Li Lyna Zhang, Mao Yang 0004, Lidong Zhou
 NSDI5
2020 AutoSys: The Design and Operation of Learning-Augmented Systems
Chieh-Jan Mike Liang, Hui Xue 0004, Mao Yang 0004, Lidong Zhou, Lifei Zhu, Zhao Lucis Li, Qi Chen 0009, Quanlu Zhang, Chuanjie Liu, Wenjun Dai
 USENIX ATC6
2019 Accelerating Rule-matching Systems with Learned Rankers
Zhao Lucis Li, Chieh-Jan Mike Liang, Wei Bai 0001, Yongqiang Xiong, Guangzhong Sun
 USENIX ATC1
2018 Metis: Robustly Tuning Tail Latencies of Cloud Systems
Zhao Lucis Li, Chieh-Jan Mike Liang, Wenjia He 0001, Lianjie Zhu, Wenjun Dai, Guangzhong Sun
 USENIX ATC1
2018 Characterizing Privacy Risks of Mobile Apps with Sensitivity Analysis
abstract
Given the emerging concerns over app privacy-related risks, major app distribution providers (e.g., Microsoft) have been exploring approaches to help end users to make informed decision before installation. This is different from existing approaches of simply trusting users to make the right decision. We build on the direction of risk rating as the way to communicate app-specific privacy risks to end users. To this end, we propose to use sensitivity analysis to infer whether an app requests sensitive on-device resources/ data that are not required for its expected functionality. Our system, Privet, addresses challenges in efficiently achieving test coverage and automated privacy risk assessment. Finally, we evaluate Privet with 1,000 Android apps released in the wild.
Li Lyna Zhang, Chieh-Jan Mike Liang, Zhao Lucis Li, Yunxin Liu 0001, Feng Zhao 0001, Enhong Chen
 IEEE Trans. Mob. Comput.3