Concise and informative visualisation is the key for comprehension and communication of data and analysis results. Especially these days where the amount of data is often huge and traditional visualisation forms may not be suitable. Treatments like data reduction or aggregation are then needed to visualise the data efficiently.
Visualisation of data plays an important role in data science, machine learning and analysis platforms. Raw data needs to be inspected, results of computational simulations and algorithms need to verified, and the end-users need insight into the data too. Given the large amounts of data available in today’s analyses, implementing efficient and responsive visualization software is far from trivial.
Our consultants have expertise in standard and advanced data visualisation techniques. This includes pretreatments such as reduction, aggregation and contextualising of data. For more complex visualization of larger data sets, a combination of different techniques is often required. For instance, deriving some statistical measures across spatial, temporal and parametric dimensions can be combined with blending techniques to provide novel visual insight.
Diako holds a Ph.D. in Computational Mathematics from the University of Oslo. As a student at both Mathematics and Physics departments at the University of Oslo, he has acquired a broad knowledge in various physical and mathematical theories, and numerical methods.
Guttorm is a curious person with an interest and enthusiasm for technology and problem solving. His academic background is focused around applied mathematics, programming, and physics. He submitted his master’s thesis the summer 2017 in the field of Computational Science and Engineering at the University of Oslo / Simula.
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.
Naeeme is an enthusiastic software developer with many years of experience as a project engineer. Before joining our team, she worked in the field of 3D modelling and CFD simulation of reactive and non-reactive flows, employing high-performance computing and immersive virtual reality environments to optimize the performance of industrial units. This allowed her to gain experience in solving complex problems using her expertise in software development combined with her knowledge in fluid dynamics, chemical thermodynamics, mass and heat transfer.
Robert has a PhD in astrophysics, with a specialisation in cosmological simulations. He is experienced with numerical modelling of physical systems, statistical analysis of large data sets, high-performance computing, and cloud based applications.
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 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.
Vinzenz is a curious, pragmatic, creative problem solver. He holds a PhD in biomechanical engineering, focusing on stochastic simulations of the blood flow in the human arterial system. This interdisciplinary work included working within engineering, medicine, biology, software development and statistics.