Data and algorithms will be key drivers to gain competitive advantages in the coming decades. Our mission is to bring cutting edge research in machine learning from academia to strategic industry applications. We bring world class and comprehensive expertise in machine learning into our innovations. Some of which are turned into products or services and some are spun off as new companies.
Have a look at our projects to learn more about what we currently work on. Please, don't hesitate to contact us for more information. We are based out of Gothenburg, but travel regularly within Sweden and internationally.
NO. Small data can be very valuable as well. Big and small data come with different challenges and often require different methods and tools. The three things that matter in the end are the business potential, the information in the data and the methods used to extract that information to solve your business problem. For those interesting in learning more, we can recommend OECD's report on data-driven innovation for growth and well-being.
Feel free to email us at firstname.lastname@example.org.
Professor Devdatt Dubhashi leads the machine learning and algorithms group at the Department of Computer Science and Engineering at Chalmers. He was an expert consultant for the OECD for their report on "Data Driven Innovation" (2016). He is centrally involved in machine learning and Data Science courses at Chalmers and Gothenburg University. He leads the project "Data Driven Secure Business Intelligence" funded by SSF, and is also a PI in a EU Center of Excellence in High Performance Computing, a VR Framework and a Vinnova FFI project. These projects are all centered around machine learning techniques and most recently the focus in the group has been in Deep Learning and Reinforcement Learning.
Hans Salomonsson has during his career in industry and academia implemented, applied and improved many types of machine learning algorithms, e.g. deep learning (LSTM, CNN, Deep Reinforcement Learning), decision tree based models (RF, GBM), SVM, etc., in many different programming languages, e.g. C, C++, C#, Python, R, Java, Scala, Objective-C, MATLAB and Julia. He enjoys taking state of the art machine learning research and adapting it to solve particular research and business problems. Alongside his engineering physics studies he also studied industrial and financial management and is passionate about the opportunities data-driven algorithms have on business processes and strategy.