Foundations for Blockchain Data Analytics: October 6-7, 2019


This event was held at SAMSI, Research Triangle Park, NC.


This decade has been marked with the rise of Blockchain based technologies. At its core, Blockchain is a distributed public ledger that stores transactions between two parties without requiring a trusted central authority. On a Blockchain, two unacquainted parties can create an unmodifiable transaction that is permanently recorded on the ledger to be seen by the public. Many applications, ranging from cryptocurrencies to food supply chain management, which demand provenance and decentralization, are already having a positive impact on society and allowing poorer countries to partake in the global economy. As these applications proliferate, so does the complexity and volume of data stored by Blockchains. Analyzing this data has emerged as an important research topic, already leading to methodological advancements in statistics, computer and information sciences.

The modeling of Blockchain data is a new area with little to no precompetitive research and working groups. There are many areas of mathematics and statistics which have great potential in advancing Blockchain applications, ranging from graph theory, topological data analysis, random matrix theory and probabilistic graphical modeling. Equally, there are several open problems which must be addressed before Blockchain becomes more mainstream and gains favorability with governmental regulations. Modeling the effect of transaction costs on the long-term stability of Bitcoin adoption, identifying more energy efficient and scalable mining mechanisms which avoid excessive computation, and determining how to identify criminal activity are just a few of these problems. Their solution requires an interdisciplinary approach and the value of this workshop is in bringing relevant experts together to solve many of these challenge with novel mathematical and statistical modeling approaches.

The SAMSI Blockchain Analytics workshop brought researchers together from statistics, data and computer science with experts in economics, social science, finance and operational research, with an ultimate goal of advancing mathematical and algorithmic methodology for blockchain analytics. This three-day workshop started with a half-day tutorial providing an introduction to the main concepts behind blockchain analytics including distributed ledger technology, graph analysis, fintech, supply chain analysis and environmental management. The end of the first day and second day featured talks that highlighted recent research in this area including monetary circuit theory, fraud detection, transaction cost analysis and market-microstructure. The workshop aimed to bring a diverse body of attendees and to attract students, postdoctoral fellows and researchers from traditionally underrepresented groups in STEM to the field of blockchain data analytics.

Confirmed speakers for this event were:

  • Peter Adriaens, Dept. of Civil and Environmental Engineering (University of Michigan)
  • Nino Antulov-Fantulin, Dept. of Humanities, Social and Political Science (ETH, Switzerland)
  • Rod Garratt, Economics (University of California-Santa Barbara)
  • Zhiguo He, Booth School of Business (University of Chicago)
  • Danny Huang (Princeton University)
  • Steven Kou, Questrom School of Business (Boston University)
  • Dimitrios Koutmos, Foisie Business School (Worcester Polytechnique Institute)
  • Alex Lipton (EPFL, Switzerland)
  • Sarit Markovich, Kellogg School of Management (Northwestern University)
  • Ciamac Moallemi, Graduate School of Business (Columbia University)
  • Anita Nikolich, Department of Computer Science (Illinois Institute of Technology)
  • Vadim Sokolov, Operations Research (George Mason University)
  • Dawn Song, Dept. of Computer Science (University of California-Berkeley)
  • Hong Wan (Purdue University)
  • Andy Wu, Stephen M. Ross School of Business (University of Michigan)

Schedule and Supporting Media

Printed Schedule
Speaker Titles and Abstracts
Poster Titles

Sunday, October 6, 2019
SAMSI, Research Triangle Park, NC


Description Speaker Slides
Opening Remarks Matthew Dixon, llinois Institute of Technology
Yulia Gel, University of Texas, Dallas
Cuneyt Akcora, University of Manitoba-Canada
Murat Kantarcioglu, University of Texas, Dallas
An Economic Analysis of the Bitcoin Payment System Ciamac Moallemi, Columbia University
A Theory of Fintech Steve Kou, Boston University
Towards a New Financial System Alex Lipton, MIT
Disclosure in the world of cryptocurrency Sarit Markovich, Northwestern University
REMOTE PRESENTATION: Cryptocurrency and blockchain analysis — Complexity and Data Science perspective Nino Antulov-Fantulin, ETH Zurich
ChainNet: Learning on Blockchain Graphs with Topological Features Cuneyt Akcora, University of Manitoba-Canada
Topological Recognition of Critical Transitions in Time Series of Cryptocurrencies Pablo Roldan Gonzalez, Yeshiva University
Dissecting Blockchain Price Analytics: What We Learn from the Geometry of Ethereum Umar Islambekov, Bowling Green State University
Real World Adventures at a Cryptocurrency Exchange Anita Nikolich, Illinois Institute of Technology
InfraTech: Blockchain-Based Financing Solutions for Cyber-Physical Infrastructure Systems Peter Adriaens, University of Michigan
Poster Session and Reception

Monday, October 7, 2019
SAMSI, Research Triangle Park, NC

Description Speaker Slides
BitcoinHeist: Topological Data Analysis for Ransomware Detection on the Bitcoin Blockchain Murat Kantarcioglu, University of Texas, Dallas
REMOTE PRESENTATION: Threat Detection on Blockchain Danny Huang, Princeton University
Panel Discussion: Blockchain Data Analytics: Research Horizons Alex Lipton, MIT
Murat Kantarcioglu, University of Texas, Dallas
Andy Wu, University of Michigan
Hong Wan, N.C. A&T State University
Entrepreneurial Incentives and the Role of Initial Coin Offerings Rod Garratt, University of California, Santa Barbara
Modeling Cryptocurrency Markets with Transaction Aware Agents Matthew Dixon, llinois Institute of Technology
Predicting Blockchain Platform dynamics with Deep learning Vadim Sokolov, George Mason University
QuTrack: Model Life Cycle Management for AI and ML models using a Blockchain approach Srikanth Krishnamurthy, Northeastern University
Machine Learning in/for Blockchain: Future and Challenges Hong Wan, N.C. State University
The Future of Cryptocurrencies & Blockchain Research Dimitrios Koutmos, Worcester Polytechnic Institute
Closing Remarks and Adjourn

Questions: email