Brett Hemenway

About

Dr. Brett Hemenway Falk is a research professor in the Department of Computer and Information Sciences at the University of Pennsylvania. He serves as the director of the Crypto and Society Lab, which focuses on privacy and security in digital environments as well as facilitating transparency and trust. Dr. Falk has published extensively in the fields of cryptography, coding theory, and network analysis. In addition to his research, he teaches a highly popular course on Blockchain technology for Penn’s Master’s in Computing and Information Technology program. Dr. Falk received his Sc.B. in mathematics from Brown University and his Ph.D. in mathematics from UCLA.

Research topics

• Computer Science
• Computer Security
• Data science
• Sociology
• Data Mining
• Economics
• Management science
• World Wide Web
• Knowledge management
• Psychology

Selected publications

• Token-Weighted Crowdsourcing

  Management Science · 2020 · 108 citations

  Senior authorCorresponding

  Blockchain-based platforms often rely on token-weighted voting (“τ-weighting”) to efficiently crowdsource information from their users for a wide range of applications, including content curation and on-chain governance. We examine the effectiveness of such decentralized platforms for harnessing the wisdom and effort of the crowd. We find that τ-weighting generally discourages truthful voting and erodes the platform’s predictive power unless users are “strategic enough” to unravel the underlying…

  PublisherDOI

• Scaling Blockchains: Can Committee-Based Consensus Help?

  Management Science · 2023-10-10 · 23 citations

  article

  In the high-stakes race for scalability, some blockchains have turned to committee-based consensus (CBC), whereby the chain’s recordkeeping rights are entrusted to a committee of block producers elected via approval voting. Smaller committees boost speed and scalability but can compromise security when voters have limited information. In this environment, voting strategies are naturally nonlinear, and equilibria can become intractable. Despite this, we show that elections converge to optimality…

  PublisherDOI

• Privacy-Preserving Network Analytics

  Management Science · 2022 · 17 citations

  We develop a new privacy-preserving framework for a general class of financial network models, leveraging cryptographic principles from secure multiparty computation and decentralized systems. We show how aggregate-level network statistics required for stability assessment and stress testing can be derived from real data without any individual node revealing its private information to any outside party, be it other nodes in the network, or even a central agent. Our work bridges the gap between e…

  PublisherDOI

• Choices in networks: a research framework

  Marketing Letters · 2020 · 14 citations

  PublisherDOI

• Balancing Power in Decentralized Governance: Quadratic Voting under Imperfect Information

  SSRN Electronic Journal · 2023-01-01 · 13 citations

  articleOpen access
  PublisherDOI

Frequent coauthors

• Rafail Ostrovsky

  55 shared

• Mary Wootters

  31 shared

• Daniel Noble

  Philadelphia University

  25 shared

• Gerry Tsoukalas

  24 shared

• Noga Ron‐Zewi

  13 shared

• Nadia Heninger

  University of California, San Diego

  11 shared

• Steve Lu

  9 shared

• Ted Chinburg

  8 shared

Labs

• Crypto and Society LabPI

  Privacy and security in digital environments, facilitating transparency and trust

Education

• Ph.D., Mathematics

  UCLA

  2010

• BSc, Mathematics

  Brown University

  2004

Awards & honors

• 2011: RAND Idea Showcase Winner
• 2007: Alumni Mentorship Award, UCLA

Similar researchers at University of Pennsylvania

• Kostas Daniilidish-68
• Vijay Kumarh-67
• Junhyong Kimh-62