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
• Business

Selected publications

• Detecting Crypto Wash Trades via Machine Learning

  Mendeley Data · 2026-04-23

  datasetOpen access1st authorCorresponding

  This dataset accompanies the paper "Detecting Crypto Wash Trades via Machine Learning" and the associated Github repository (see Related Links). It contains labeled machine learning samples constructed from on-chain transactions of major NFT exchanges and Mt. Gox. Each row corresponds to a trade with engineered features and a binary label indicating legitimate or wash transaction. These files are the inputs used to train and test the machine learning models described in the paper. The raw data sources and preprocessing steps are documented in the paper and GitHub repository.

  PublisherDOI

• Detecting Crypto Wash Trading via Machine Learning

  Mendeley Data · 2026-04-23

  datasetOpen access1st authorCorresponding

  This dataset accompanies the paper "Detecting Crypto Wash Trading via Machine Learning" and the associated Github repository (see Related Links). It contains labeled machine learning samples constructed from on-chain transactions of major NFT exchanges and Mt. Gox. Each row corresponds to a trade with engineered features and a binary label indicating legitimate or wash transaction. These files are the inputs used to train and test the machine learning models described in the paper. The raw data sources and preprocessing steps are documented in the paper and GitHub repository.

  PublisherDOI

• Balancing Power in Decentralized Governance: Quadratic Voting and Information Aggregation

  Management Science · 2025-11-03

  article

  In decentralized governance, quadratic voting (QV)—where the cost of acquiring voting power is convex—optimally aggregates voter preferences, outperforming simpler linear voting (LV) mechanisms when voters have complete information. But what if they do not? We show that uncertainty not only breaks QV optimality but can also cause it to underperform LV. Intuitively, this is because cost convexity can disincentivize better-informed voters from adequately conveying their private information. The optimal mechanism varies with the distribution of stakes and information among voters, implying that QV’s known advantages in preference aggregation do not readily extend to common-value information aggregation settings. This paper was accepted by Will Cong for the Special Issue on the Digital Finance. Supplemental Material: The online appendices are available at https://doi.org/10.1287/mnsc.2024.08469 .

  PublisherDOI

• Malicious Security for PIR (Almost) for Free

  Lecture notes in computer science · 2025-01-01

  book-chapter1st authorCorresponding
  PublisherDOI

• Why Will a Small Increase in Global Temperature Lead to a Large Increase in the Number of Heat Waves? Truncation and Extreme Events

  CHANCE · 2024-10-01

  article1st authorCorresponding
  PublisherDOI

• DORAM Revisited: Maliciously Secure RAM-MPC with Logarithmic Overhead

  Lecture notes in computer science · 2023-01-01 · 4 citations

  book-chapterOpen access1st authorCorresponding
  PublisherOA PDFDOI

• Automated Market Makers and the Value of Adaptive Fees

  SSRN Electronic Journal · 2023-01-01 · 6 citations

  articleOpen access
  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 asymptotically (in voter numbers), exponentially quickly, and under relatively weak informational requirements. Compared to popular stake-weighted lottery and single-vote protocols used in practice, we find that CBC, when paired with approval voting, can offer meaningful efficiency and robustness gains if enough voters are engaged. This paper was accepted by Will Cong, Special Section of Management Science: Blockchains and Crypto Economics. Funding: This work was funded by The Hogeg Blockchain Research Institute. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.03177 .

  PublisherDOI

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

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

  articleOpen access
  PublisherDOI

• Proactive Secret Sharing with Constant Communication

  Lecture notes in computer science · 2023-01-01 · 4 citations

  book-chapterOpen access1st authorCorresponding
  PublisherOA PDFDOI

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