KTU Big Data School 2020

KTU Big Data School 2020 is both theoretical and practical scientific event aiming at updating participants about the most recent advances in the critical and fast developing area of big data, which covers topics such as big data and financial analytics, machine learning, big data processing, etc.
The event is concentrated towards deep theoretical background and improving skills during practical workshops.
The event will take place at Kaunas University of technology and is organized by the Faculty of Mathematics and Natural Sciences. The sessions will be held at KTU Technology and Business Centre Santaka Valley.
There will be three sessions given by different experienced speakers involving lectures and practical tasks.
The event is addressed to Industry practitioners, Scientists, PhD students, postdocs and Master students.

KTU Big Data School 2020 has received partial funding from the European Union’s Horizon 2020 research and innovation program “FIN-TECH: A Financial supervision and Technology compliance training programme” under the grant agreement No 825215 (Topic: ICT-35-2018, Type of action: CSA)

Lecturers:

Dr. Michael Fairbank (University of Essex, UK)
Short-bio: Dr Michael Fairbank is a Computer Science lecturer at the University of Essex, UK. He is an active machine-learning researcher, with publications in reinforcement learning, deep learning and neural networks. In his previous careers he worked as a computer consultant and as a mathematics teacher. He has a passion for all things related to computing, mathematics and AI.

Dr. Wannes Meert (KU Leuven, Belgium)
Short Bio: Wannes Meert received his degrees of Master of Electrotechnical Engineering, Micro-electronics (2005), Master of Artificial Intelligence (2006) and Ph.D. in Computer Science (2011) from KU Leuven. He is currently research manager in the DTAI research group at KU Leuven. His work is focused on applying machine learning, artificial intelligence and anomaly.

Dr. Ronald Hochreiter (WU Vienna University of Economics and Business, Austria)
Short Bio:  Algorithmic Finance. Computational Finance. Quantitative Finance. Analytics in Banking and Finance. Risk Management. Optimization under Uncertainty and Decision Science. Data Science and Machine Intelligence (Machine Learning and Deep Learning). AI.