Better decisions, fewer costs. Industrial data acquisition & AI products for your business

Board observer in Avo Consulting via Bergen Næringsråd's (Bergen's Chamber of Commerce) programme Styrekandidaten

Asistobe uses Big Data and AI to optimize, predict, and redesign public transportation networks in small/medium cities around the globe. Asistobe ensures that a city's transport system is operating to maximum capacity within their budget.

I have helped Sonat establish a foothold as the leader for Machine Learning in the west coast of Norway. As a Senior consultant I have helped with sales and facilitated courses in Machine Learning, Python, and Big Data architectures. A few relevant roles:- Data Science and Machine Learning lead at a major international marine engineering company. I have architected the platform for sensor data acquisition and predictive maintenance. I have gathered internal support for the continued project funding, and assembled a team that successfully delivered a pilot after 6 months, and a finished product after 2 years. - Data Science and Machine Learning lead for TV2, delivering a personalization and recommendation systems platform that is now live on sumo.tv2.no. I advised on the choice of algorithms, data preparation and handling, and setting machine learning models in production.

DharmicData is a company specializing in optimization of media platforms and delivering personalized user experiences. DharmicData has competence in a number of areas related to media, including experimentation and testing, text analytics, predictive analysis, personalization, and visualization.As CTO of DharmicData i helped the team grow to deliver a big data platform capable of supporting the consultancy business.

I worked as a management consultant at Capgemini Bergen, with roles in Information Security, Big Data, and Analytics.

Unsilo is an high-tech Venture Capital-backed start-up company offering artificial intelligence tools and solutions for publishers to grow new business opportunities and improve customer experience and publishing workflows. The technology is based on a sophisticated semantic-based search engine, making it easy and fast to find relevant knowledge across different content sources hidden in domain-specific terminology. The underlying data sources are over 12 million scientific articles, 4 million patents, and 1 million Wikipedia articles. Unsilo has a SaaS offering and is partnering with major scientific publishers such as Springer Nature, Wiley, and BMJ.I was Unsilo's co-founder and CTO and one of the main drivers in technological strategy, algorithmic development, and business and system architecture. I was responsible for managing and expanding the technical team, and have lead 10 employees to successfully delivering a product.My background in Text and Data mining, Natural Language Processing, and Information Retrieving, enabled me to work on the product from prototype to delivery, building its components in Python, Java, and C.The entire computational infrastructure is Linux based and deployed on the Amazon Web Services (AWS) Cloud. We made extensive use of AWS' services such as EC2, S3, DynamoDB, and SWF.

Uni Research is a research company with more than 460 highly-qualified employees from 50 different nations. The company has an annual turnover of NOK 400 million and consists of seven departments. Uni Research carries out research and development in the fields of health, modelling, marine molecular biology, environment, climate, energy and social sciences. Uni Research is a strategic research partner of the University of Bergen (UIB).I have participated in several projects within Uni Research as a Postdoctoral Fellow, in the areas of knowledge discovery and data integration, Big Data, and Data Mining. He was also a key player in the eSysbio project, a PaaS offering for bioinformatics analysis based on SOAP web services consumption. Main technologies, tools, and programming languages used were Python, Linux, Java, SOAP, REST, R, bash, Git, and SVN.

The Haukeland University Hospital is the largest hospital in Norway in terms of number of patients. Every year nearly 600 000 patients are treated at its facilities. Budget as per 2013 is of 10 billion kroner. The hospital is staffed by around 11 800 people divided over 8250 FTEs. They have a research and teaching cooperation with the University of Bergen. Around 2000 students have part of their education at the hospital.I have worked on a project to construct a fully automated pipeline for the analysis of Next Generation Sequencing Data (Human Exome) for the Department of Clinical Medicine. The goal behind the project was to provide additional diagnostic potential from human sequenced blood samples.The pipeline was assembled together using Python, joining a collection of disparate scripts and tools. There were special requirements, such as the handling of large amounts of data concurrently and in parallel. For a common run, the pipeline can work with 30TB of data input, working on a large UNIX shared memory machine with 24 cores and 128GB ram. Technologies used : Python, Java, bash, R, Graphviz.

PhD title: Integrating medical databases, the interactome, and the environment.The overall goal of my project was to create an environment for systems biology of growth and reproduction, with the specific aim of discovering new disease genes, and in particular revealing molecular mechanisms that also involve changes in the chemical environment. The computational strategy involved data integration (for example protein-protein interaction data, gene expression data, chIP-on-chip data, arrayCGH data), text mining (phenotype association based on medical texts), and the construction of machine learning techniques for prediction of molecular properties not yet measured, and discover novel correlations between disease and phenotype.The PhD project was at the intersection point between molecular bioinformatics and systems biology, overlapping with the data produced in biobanks, public registries, and patient journals. High quality biobanks in Denmark, with large cohorts collected over of longs periods of time including disease records linked to the personal identification number, were the starting point for the analysis.Main activities included understanding and mapping fields from Electronic Health Record (EHR) systems, information extraction, combining structured data with knowledge mined from free text data. In addition to EHR system data, I have combined data from human Protein-Protein interaction databases, chemical databases, and human phenotypic databases.The insights from my PhD thesis have paved way to a better understanding of the data trove contained in EHR systems, and how it can be combined with other types of data in order to get a better overview of the patient and of the disease.

As part of my Ph.D. education, I have stayed in Dr Patricia Donahoe's group, at the Center for Pediatric Research. My main responsibilities were to integrate different types of biological data and knowledge,such as text mining literature and protein-protein interaction databases, constructing an inference layer forsupporting genomic studies' results. MGH is the teaching hospital of Harvard Medical School and is affiliated with the BROAD Institute of Harvard and MIT.

System administrator and bioinformatics support for the Department of Systems Biology. Responsible for daily management of the internal MacOSX servers, and 30 laptops. Design and implementation of the Department's SOAP Web Services, using Perl, Python and Ruby.