Henrik Ohlsson was born in Sweden in 1981. He received the M.Sc. degree in Applied Physics and Electrical Engineering in October 2006 and his Ph.D. degree in Automatic Control in November 2010, all from Linköping University, Sweden. He has held visiting positions at the University of Cambridge (UK) and at the University of Massachusetts (USA). Henrik is currently an Assistant Professor at Linköping University and a visiting professor at University of California, Berkeley. He is supported by a grant from the Sweden-America foundation and a grant from the Swedish Research Council (VR).
Henrik's research is in the area of system identification, machine learning and compressive sensing. Henrik is involved in a number of research projects, some which are described below.
Nonlinear Compressive Sensing
While compressive sensing has been one of the most vibrant research fields in the past few years, most development only applies to linear models. This limits its application and excludes many areas where compressive sensing could make a difference. One such area is phase retrieval. In this research project we aim to extend compressive sensing to the nonlinear phase retrieval problem.
In many applications it is costly or impossible to measure the sources one-by-one and all that is available is some aggregated measurements of their contributions. Disaggregation is the task of separating a signal into its sources. It is a fairly new area and with applications in fields such as chemistry, biology, etc.
Hybrid System Identification
Hybrid system identification is an area within system identification which deals with the identification of models of hybrid systems. Hybrid system identification is an extremely complex task and as today most research has been dealing with the most basic type of hybrid model -- the piecewise affine model.