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Andreas Haeberlen

Andreas Haeberlen

· Associate Professor

University of Pennsylvania · Computer and Information Science

Active 2000–2026

h-index36
Citations5.8k
Papers11311 last 5y
Funding$2.6M1 active

Academic metrics are sourced from OpenAlex and public funding records; values may differ from Google Scholar.

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Research topics

  • Computer Science
  • Distributed computing
  • Computer network
  • Data Mining
  • Theoretical computer science
  • Database
  • World Wide Web
  • Algorithm
  • Programming language
  • Operating system

Selected publications

  • Bounded-time recovery for distributed real-time systems

    2020-04-01 · 8 citations

    articleSenior author

    This paper explores bounded-time recovery (BTR), a new approach to making cyber-physical systems robust to crash faults. Rather than trying to mask the symptoms of a fault with massive redundancy, BTR detects faults at runtime and enables the system to recover from them – e.g., by transferring tasks to other nodes that are still working correctly. When a fault does occur, there is a brief period of instability during which the system can produce incorrect outputs. However, many cyber-physical sy…

  • Orchard: Differentially Private Analytics at Scale

    Operating Systems Design and Implementation · 2020-01-01 · 6 citations

    article
  • Metaverse as a Service

    2023-10-30 · 5 citations

    articleOpen access1st authorCorresponding

    We present a vision for the future of an emerging category of cloud service: the metaverse of 3D virtual worlds. Today, hundreds of millions of users are active daily in such worlds, but they are partitioned into small groups of at most a few hundred players. Each group joins a different virtual world instance, and players can only interact in 3D with others players in the same group during that session. Current platforms are designed in ways that simply cannot scale much further, and solutions…

  • Arboretum: A Planner for Large-Scale Federated Analytics with Differential Privacy

    2023-10-03 · 3 citations

    articleSenior author

    Federated analytics is a way to answer queries over sensitive data that is spread across multiple parties, without sharing the data or collecting it in a single place. Prior work has developed solutions that can scale to large deployments with millions of devices but, due to the distributed nature of federated analytics, these solutions can support only a limited class of queries - typically various forms of numerical queries, which can be answered with lightweight cryptographic primitives. Supp…

  • Fuzzi: A Three-Level Logic for Differential Privacy

    arXiv (Cornell University) · 2019-05-29 · 2 citations

    preprintOpen access

    Curators of sensitive datasets sometimes need to know whether queries against the data are differentially private [Dwork et al. 2006]. Two sorts of logics have been proposed for checking this property: (1) type systems and other static analyses, which fully automate straightforward reasoning with concepts like "program sensitivity" and "privacy loss," and (2) full-blown program logics such as apRHL (an approximate, probabilistic, relational Hoare logic) [Barthe et al. 2016], which support more f…

Recent grants

Frequent coauthors

  • Boon Thau Loo

    30 shared
  • Peter Druschel

    30 shared
  • Wenchao Zhou

    Alibaba Group (China)

    25 shared
  • Alan Mislove

    Northeastern University

    16 shared
  • Krishna P. Gummadi

    15 shared
  • Micah Sherr

    Georgetown University

    14 shared
  • Ang Chen

    Jiangsu University

    14 shared
  • Marcel Dischinger

    Max Planck Institute for Software Systems

    14 shared

Labs

  • Penn Engineering's TeamPI

Education

  • PhD, Computer Science

    Rice University

    2009

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