Cuneyt Akcora

Postdoctoral Fellow | Departments of Statistics and Computer Science at the University of Texas at Dallas

Title: Blockchain Data Analytics: Building Predictive Machine Learning Models with Topological Data Features


Over the last couple of years, Bitcoin cryptocurrency and the Blockchain technology that forms the basis of Bitcoin have witnessed an unprecedented attention. Designed to facilitate a secure distributed platform without central regulation, Blockchain is heralded as a novel paradigm that will be as powerful as Big Data, Cloud Computing, and Machine Learning. Blockchain continues to evolve, but its applications have already matured to rival, and already in some cases, replace more traditional institutions as avenues of global activity.

A unique Blockchain feature is that in contrast to fiat currencies, transactions of cryptocurrencies are permanently recorded on distributed ledgers to be seen by the public. As a result, public availability of all cryptocurrency transactions creates a complex network of public financial interactions that can be used to study not only the blockchain graph, but the relationship between various blockchain network features and their impact on risk investment, price dynamics, and assessment of market activity, in general.

We will offer a holistic view on Blockchain Data Analytics. Starting with the core components of Blockchain, we will detail the state of art in Blockchain data analytics for graph, security and finance domains. Beyond the cryptocurrency aspects of Blockchain, we will outline the frontier research approaches for data analyses from Blockchain platforms, such as Ethereum, Waves and Omni.

Furthermore, we will discuss how the adoption of Blockchain will impact the future of data analytics.


Cuneyt Gurcan Akcora is a Postdoctoral Fellow in the Departments of Statistics and Computer Science at the University of Texas at Dallas. He received his Ph.D. from University of Insubria, Italy and his M.S. from SUNY Buffalo, USA. His research interests are Data Science on complex networks and large scale graph analysis, with applications in social, biological, IoT, WWW and Blockchain networks. He is a Fulbright Scholarship recipient, and his research works have been published in leading conferences and journals including VLDB, ICDM and ICDE.