Data Science and Machine Learning

Data Science is the science (and art) of extracting knowledge or insight from raw data, whether that is applied to research or to industry. Extracting meaning from and interpreting data requires tools and methods of multiple disciplines such as mathematics, statistics, physics and computer science.

In Expert Analytics we apply the scientific method as a pillar to obtain valuable knowledge out of data. This usually includes a combination of designing experiments, collecting data from various sources, processing and cleaning the data to be analyzed, making hypothesis, analyzing and interpreting the data, drawing conclusions and presenting them in an understandable manner, all with the aim of supporting decision-making in the industry. In today's business world, data analysis plays a fundamental role in helping businesses operate more effectively.

Examples of data science applications useful in an industrial environment are the detection of anomalies in the normal functioning of expensive heavy machinery, where data from sensors are used to predict maintenance operations or schedule stops in production. Other examples are optimisation problems where production (and thus benefits) is maximized, maintenance operations minimized within safety regulations, and optimization of work schedules in terms of the allocation of manpower relative to demand.

Our data scientists have an academic background in the natural sciences, where analysis of complex datasets and critical thinking are keys to advance knowledge and publish results in scientific journals. Our expertise spans from physics, to mathematics, to statistics and computer science, and we have experience in presenting difficult-to-understand concepts in a comprehensible language.

Contact Point: vinzenz@xal.no
Phone: +47 451 24 193

Consultants:

Ada Ortiz-Carbonell

Astrophysicist researcher turned into data scientist and having fun with it. After 20 years in academia I decided to take on a new challenge and apply my skills in the industry.

Anis Ayati

Anis holds a PhD in Fluid Mechanics from the University of Oslo. Before joining us, he completed a three-year postdoctoral fellowship, which included research stays at PUC-Rio, Brazil and Princeton University, USA.

Ata Karakci

Ata holds a PhD in physics with specialisation in astrophysics which he obtained from Brown University in 2014. Before joining the Expert Analytics team, he has worked as a postdoctoral researcher at the University of Oslo and Universite Paris VII. His expertise includes statistical analysis of large data sets, numerical modelling, signal processing, imaging, and programming.

Jonathan Feinberg

Jonathan is a specialist within machine learning, stochastic modelling, statistics, and numerical programming. He has an eye for seeing possible solutions to the “hard” problems, and being able to quickly convert those into functioning prototypes.

Pia Zacharias

Pia is a physicist with more than ten years of experience in scientific programming and data analysis. She holds a PhD in physics, which she obtained from the University of Freiburg, Germany in 2010. She has been working as a researcher, mentor and lecturer in astrophysics at different research institutions across Europe before she joined Expert Analytics in 2018. Her areas of expertise include numerical simulations of complex physical systems, statistical data analysis and signal processing, as well machine learning applications and visualization and handling of large datasets.

Robert Hagala

Robert is finishing a PhD in astrophysics at the University of Oslo, with specialisation in cosmological simulations. He is experienced with numerical modelling of physical systems, statistical analysis of large data sets, and solving highly non-linear systems of differential equations with parallel computing.

Robert Solli

Robert Solli is a specialist in scientific programming, with expertise in mathematical optimisation, statistical analysis and machine learning. He has solved a varied set of problems with this skillset, from understanding complex physical systems to increasing student volunteer participation.