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.
Consultants:
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.
Daniel Marelius Bjørnstad
Daniel is a seasoned software developer with a PhD in neuroscience, specializing in data analysis and cloud software development. With expertise in multiple stages of project development, Daniel’s work spans from the initial definition of project goals and value propositions to the deployment of final products, including services, dashboards, reports, production models, and APIs. His background in neuroscience provides a strong foundation in complex data processing and analysis, allowing him to tackle challenging problems in diverse domains. He started working professionally with software development in 2016.
Felix Kohler
Felix is a research scientist with experience in data analysis and scientific programming. He holds a PhD in physics and worked in various interdisciplinary projects both as a Postdoc and Researcher in an academic setting as well as a senior researcher in industry. Felix has a background in both experimental studies as well as mathematical and numerical modelling. His creativity, analytical mindset and scientific experience provide him with a solid foundation for finding solutions to complex problems.
Kine Onsum Moseid
Kine is an organized scientific programmer that likes to solve problems from a big picture approach. She submitted her PhD thesis in Climate Science in December of 2021, and during her doctorate she analyzed radiation and air pollution data from a multitude of climate model simulations and compared them with observations. This work gave insight in big data analytics and visualisation, especially regarding time series and how to avoid common pitfalls like comparing apples and oranges. She enjoys working with large datasets, learning new things, and communicating/teaching science.
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.
Sebastian Franco Ulloa
Sebastian is a curious person always looking to learn about new topics and combining them into novel solutions to complex problems. He received his PhD in biocomputional sciences in March 2021 from the Italian Institute of Technology/University of Bologna, where he studied how nanomaterials interact with physiological environments. He is experienced in molecular simulations, data visualization, and scientific writing. In more recent years, Sebastian has been applying bioinformatic methods to genomics, proteomics, and transcriptomics data.
Therese Renstrøm
Therese holds a Ph.D. in Experimental Nuclear Physics from the University of Oslo. Her thesis was awarded the YARA Birkeland Prize for outstanding contributions to innovation and industry. Her main fields of expertise are developing real-time analytics tools and implementing machine learning and statistical models. The past years, she has developed prototypes and set into production software tools for the petroleum-, processing- and medical industry.