SUURPh projects

SUURPh funds cohorts of 8 doctoral fellows, who work with advisors in Oslo and at UCSD on projects in key fields of computational biology and medicine. The project topics range from computational electrophysiology and neuroscience to mechanics and fluid dynamics, and involve leading experts in these fields. These projects are described below.

Current SUURPh projects:

Geometric Deep Learning for Brain Activity Classification

This PhD project will contribute to the field of Geometrical Deep Learning, which aims to extend the field of Deep Learning to non-Euclidean data. His work consists of both analytical mathematics and computational modeling using TensorFlow and Kereas. The goal is to develop new techniques and algorithms that allow for fast and efficient learning of non-euclidean data. A final application of the work will be to test the developed algorithms on fMRI and EEG data collected at the Virginia de Sa Lab and the lab of Dr. Anders Dale at the University of California San Diego.

PhD candidate: Andreas Oslandsbotn. Andreas has a background in applied physics and mathematics at the University of Science and Technology in Trondheim, Norway, and the University of California Berkeley, USA. Andreas has also performed experimental work at CERN, where he investigated the effect of proton beam impact on superconducting magnets. During his Masters, he investigated the multimesh formulation of the finite element method that could be used in combination with shape optimization. 

Supervisors: Nickolas Forsch (Simula Research Laboratory), and Alexander Cloninger (UCSD, Mathematics)

Computational models of growth and remodeling in Pulmonary Hypertension

This PhD project attempts to develop a framework for modelling the right ventricular hypertrophy and functional remodelling that contribute to compensated systolic and diastolic function, during pulmonary hypertension. His work mainly involves multi-scale computational modelling using data from murine experiments to validate the model-predicted myocardial growth and remodelling.

PhD Candidate: Oscar Odeigah. Oscar has a background in mechanical engineering and engineering design from Ahmadu Bello University, Nigeria, and University of Tromsø, Norway. For his master thesis, he used patient-specific bi-ventricular models to investigate the differences (and similarities) between the active stress and active strain methods of mathematically modelling the active contraction of cardiac muscle. His interest in cardiovascular biomechanics and patient-specific modelling, led him to join the SUURPh program, where he will have the opportunity to continue pursuing these interests, in a highly stimulating research environment.

Supervisors: Joakim Sundnes (Simula Research Laboratory), Samuel Wall (Simula Research Laboratory), Daniela Valdez-Jasso (UCSD, Bioengineering), Andrew McCulloch (UCSD, Bioengineering)

Using Personalized Cardiac Models to Predict Patient Risk for Arrhythmias

The goal of the PhD project is to extend the use of current personalized heart models to address the need for risk stratification and treatment planning in patients with congenital heart disease (CHD). To achieve this goal Lisa will utilize images and clinical data that were collected as part of a large longitudinal study of CHF patients. Lisa will use MRI imaging data to create structural models, personalize the electrophysiology using recorded ECGs, and then perform simulations to predict risk to arrhythmia.  The simulations can then be used to predict the optimal treatment for each patient (surgery, ablation, ICD implantation, etc.).

PhD Candidate: Lisa Pankewitz. Lisa received her M. Sc. Biochemistry with a focus on Biomedicine from Leipzig University in Germany. During her thesis she worked for 6 months in the laboratory of Jens Meiler  in the center for structural biology at Vanderbilt University on developing a novel approach for membrane protein structure determination. By joining the SUURPH program Lisa can use her interdisciplinary background in biophysics, biochemistry and computation to develop tools for biomedical applications.

Supervisors: Hermenegild Arevalo (Simula Research Laboratory), Andrew McCulloch (UCSD, Bioengineering)

BrainCircus: From Brain Circuit Models to Brain Insight

As part of an ongoing collaboration with the Allen Institute for Brain Science, Atle will work on validating and developing a biophysically detailed model of the mouse primary visual cortex. With this project, he will contribute both to a better understanding of visual processing in the brain and to improving our insight into the neuronal processing underlying the electrical signals measured with EEG, ECoG, and multielectrodes.

PhD Candidate: Atle Rimehaug. With a B.Sc. in cognitive psychology from the University of Oslo and an M.Sc. in applied physics from NTNU Atle was searching for a field where he could apply all his skills and scientific knowledge. He found this in computational neuroscience at CINPLA and the SUURPh-program, where strong multidisciplinarity is combined with an excellent international research environment.

Supervisors: Gaute Einevoll (UiO, Physics), Marianne Fyhn (UiO, Biosciences), Anders M. Dale (UCSD, Radiology and Neurosciences), Anna Devor (UCSD, Neurosciences)

Behavior Dependent Neural Stimulation in Freely Moving Animals

The objective of the project is to combine computational modelling with animal experiments to reveal new insight about memory processing in the hippocampal-entorhinal circuit. Maria will use closed-loop stimulation of neurons in the hippocampal-entorhinal circuit involved in various memory tasks, such as spatial navigation in freely moving animals. While experiments with this system can examine the changes in the stimulus responses of target neurons, many questions about the underlying mechanism of those changes can only be answered in computational models. In particular,  artificial intelligence (AI) approaches will be used to study the hippocampus-entorhinal circuit function, which has recently opened new perspectives to understand cognitive behaviors.

PhD Candidate: Maria Perona Fjeldstad. Maria holds a B.Sc. in Biomedical Sciences with Pharmacology and a M.Sc. in Integrative Neuroscience from the University of Edinburgh. She is interested in the nervous system, and how different types of neuronal and glial cells communicate in an immensely complicated way to give rise to complex behaviour in living animals. Her Masters degree research involved experimentally analysing sciatic nerve conduction properties.

Supervisors: Torkel Hafting (UiO, IMB), Phillip Häfliger (UiO, IFI), Gert Cauwenberghs (UCSD, Bioengineering)

Non-Invasive Microwave BOdy-Sensing (NIMBO)

The main objective of NIMBO (Non-Invasive Microwave BOdy sensing) project is to develop an innovative, new sensor technology for in-vivo, transcutaneous, non-invasive sensing of blood glucose based on microwaves. This technique might potentially be applied to other body substances monitoring. NIMBO includes the study and characterization of microwave sensors and the design and implementation of an integrated circuit for signal processing.

PhD Candidate: Adrian Llop Recha. Adrián has a background in physics and electronics from the University of Zaragoza, Spain, and the University of Oslo, Norway. His interests include integrated circuits and systems and biomedical sensors. His master thesis focused on designing a front-end for conducting 4-electrodes bioimpedance monitoring of cell cultures. By embarking on SUURPh, he will explore the blend of electronics with the human body while working in an international and outstanding research environment.

Supervisors: Dag T. Wisland (UiO, IFI), Kristian Gjertsen Kjelgård (UiO, IFI), Tor Sverre Lande (UiO, IFI), Gert Cauwenberghs (UCSD, Bioengineering)

Quantifying the Impact of Germline Gene Variation on Immune Repertoire Architecture

The main goal of this PhD project is to quantify the impact of germline gene variation on the immune receptor repertoire architecture. Lonneke will develop representations in which immune repertoire variability is decomposed into genotype-derived and phenotype/environment-derived components. Subsequently, these representations will be used for machine learning approaches to capture disease-associated biomarkers. 

PhD Candidate: Lonneke Scheffer. Lonneke completed a bachelor in bioinformatics at Hanze University of Applied Sciences, Groningen. During the final year of her bachelor's, she completed two internships in Oslo, after which she went on to pursue a master’s degree in informatics at the University of Oslo. During her master’s thesis, she analyzed and simulated the frequency distributions of immune receptor repertoires. During her SUURPh program, she will continue to work on immune repertoire analysis. 

Supervisors: Geir Kjetil Sandve (UiO, IFI), Victor Greiff (UiO, Immunology), Pavel Pevzner (UCSD, Computer Science)

Contact:

Andy Edwards

Chief Research Scientist

Email Andy

Kimberly McCabe

Research Scientist

Email Kimberly

Nickolas Forsch

Research Engineer

Email Nickolas