Prof Juho Kanniainen's pages

Dr Juho Kanniainen is Professor in Computing Sciences at Tampere University (TAU), Finland, where he serves as the Head of the Data Science Sub-Unit, which includes a total of ~70 members, 11 of whom are professors. He also leads the research group focused on Financial Computing and Data Analytics. With a wealth of international leadership experience, Dr. Kanniainen has undertaken pivotal roles, notably as the director and coordinator of two large EU projects: HPCFinance and BigDataFinance . In his capacity as the head of multi-member international consortia, he has successfully secured a total of 7.5 million euros in EU funding for these pioneering initiatives. Additionally, Dr. Kanniainen has directed the international MSc program in Computing Sciences at TAU.

Juho's research agenda is focused on statistical computing, mathematical modelling, and data science in finance and risk management. He has published on financial markets, high-frequency finance, financial networks, derivative pricing, and financial econometrics. Currently, he is running a project "Tip Chains: Modeling and Analyzing Information Flows in Insider Networks", which aims to identify how private information spreads from insiders to other investors (see our recent publication and a newspaper article).   

In his research, he is not only using traditional stochastic calculus, but also modern data science approaches, namely network science and machine learning methods with rich data sets. His papers have been published in top-tier journals, including IEEE Transactions on Neural Networks and Learning Systems (IF 14.255), Pattern Recognition (IF 8.518), and the Review of Finance (IF 5.059). He has organized several conferences and served as a co-editor for the book entitled High-Performance Computing in Finance: Problems, Methods, and Solutions, Chapman and Hall/CRC Financial Mathematics Series. He has supervised and co-supervised 10 PhD students.

Google scholar: 

BigDataFinance EU project: New quantitative and econometric methods for empirical finance and risk management with large and complex datasets by exploiting big data techniques

HPCFinance EU project: At the fertile crossroads of Financial Engineering and High Performance Computing providing robust solutions to managing financial risks