Mar 6 – 8, 2017
Europe/Zurich timezone
There is a live webcast for this event.

Speaker biographies

Georgios Bitzes  |  CERN, Switzerland

I am currently a Fellow working on data storage at CERN. My main project right now is building a highly-available distributed database for the EOS namespace, built upon rocksdb and the raft consensus algorithm.

I received my BSc in Computer Science from the Department of Informatics and Telecommunications, University of Athens. My main interests include performance optimization, distributed computing, and algorithmic problems.


Daniel Hugo Campora Perez
Universidad de Sevilla, Spain
CERN, Switzerland

Coming from Sevilla with a Computer Engineering degree and having spent some five years at CERN with various contracts, I'm currently a Doctoral Student, optimizing reconstruction algorithms in LHCb with an Artificial Intelligence twist.

I have many good questions, but I'm still searching for good answers!


Dr. Michael Davis  |  CERN, Switzerland

Michael is a software engineer at CERN. He has spent the last three years developing part of the power control system for the LHC.

Michael holds a BSc. in Computer Science from Brunel University, London, a CERN Tier-2 institute. He received his MSc. in Computer and Electronic Security from Queen’s University, Belfast.

In 2010, he joined the Knowledge and Data Engineering research group at Queen’s, where he obtained a Ph.D. for his research into algorithms to discover patterns and anomalies in graphs. He has also worked as an extra in Game of Thrones seasons 2, 3 and 4.

Michael is looking forward to joining the IT Department (Storage Group) in spring 2017.


Daniel Lanza  |  CERN, Switzerland

At CERN, an organisation that Daniel joined more than 2 years ago, he is working on developing and providing Big Data solutions. During his two Degrees and two Masters, where he studied computer science, telecommunications and Big Data, he has been specially interested in Evolutionary Computation, field of knowledge where he has several publications.

His attention has been mainly in joining the two fields he loves, Big Data and Evolutionary Computation, hence he has worked on integrating them to speed up evolutionary processes by running them on distributed environments like Hadoop. However, he has also worked in a bloat control mechanism for genetic programming algorithms to avoid the grow in size and computational cost of individuals (solutions).


Lorena Lobato Pardavila
University of Oviedo, Spain
CERN, Switzerland

Lorena works as a DevOps Engineer in the Computing and Monitoring group of the IT department, having worked previously on Oracle replication technologies within the Data Bases group and as a software developer for the GLIB project in the Electronic Systems group in the PH department .

After receiving her MSc in Computer Science at the University of Vigo, she subsequently worked for different companies as a System Analyst and started working for CERN in 2011. She is a positive-thinker, and an enthusiastic and dynamic person with strong interests in medical applications and cognitive systems, technological development and engineering systems, and how they can be applied to solving IT challenges. She is also a passionate about music, travels, literature and sport in general.


Eamonn Maguire  |  Pictet Asset Management, Geneva, Switzerland

Eamonn completed his DPhil (PhD) at the University of Oxford in computer science, focused on data visualization, in particular the systematisation of glyph design.

His research interests are in the merging of machine learning and visual analytics, where he currently plies his trade as a Data Scientist at Pictet Asset Management in Geneva.

Until November 2016 he was a Senior Marie Curie COFUND Fellow at CERN where he led development of the new platform and contributed to numerous other visualization and data projects at CERN.

Before that, he was the lead software engineer at the Oxford University e-Research Centre, where he led development of bioinformatics tools and a visual analytics platform for corporate insider threat detection.