Workshop 16 January 2025

List of people who will participate at workshop for convergence environments. Will be updated.

Neuroscience

Name	Research topic(s) of interest and/or competence	Title or short description (2-3 sentences) of initial project idea
Charlotte Boccara	Sleep Systems, neuroscience, cognitive development	We combine high density electrophysiology, optogenetics and computational analyses to investigate how sleep contributes to healthy development.
Ulysse C?t��-Allard	Machine learning, wearable, human-computer interface, digital biomarkers	Health state recognition and trajectory prediction.
Camila Esguerra	Disease models, Drug discovery, Tissue xenografting, Toxicology
Steffen Maude Fagerland	fNIRS, fMRI, brain training
Helene Kn?velsrud	Autophagy, kidney cancer, drosophila	Autophagy in sleep and restoration
Soili Marianne Lehto	Depression, stress, psychobiology, psychoneuroimmunology, psychosomatics	Several options possible based on potential collaborators' orientation and interests. I have ongoing projects (with focus on above topics of interest/competence) with multiple existing datasets, both clinical and population-based, ranging from 100 to ca. 500,000 individuals. Most samples include biomaterial banks (serum, plasma, whole blood, placental tissue, hair).
Siri Leknes	Applying psychology to understand health consequences of clinical decisions and clinical encounters, Opioid effects; pain; addiction; anaestesia, Affective neuroscience, social neuroscience	Applying psychology to understand consequences of clinical decisions
Niamh MacSweeney	Hormonal contraceptive use, adolescent brain development, mental health risk, women's health, puberty, cognitive neuroscience
Anders Malthe-S?rensen	Neuroscience, gene therapy, de novo protein design, machine learning, docking simulations	Our goal is to develop a platform technology for gene therapies for central nervous system diseases combining generative AI models for construct and capsid development, in vitro and in vivo experimental pipelines, and an integral RRI approach to address fundamental relevant ethical issues.
Cecilie Morland	Stroke	Preclinical development of new stroke treatment
Tuula Nyman	clinical proteomics, mass spectrometry, spatial proteomics, personalised medicine	Proteomics is the ��large scale study of proteins��, and proteome level information is critically important in biomedical research. Recent advances in mass spectrometry (MS)-based proteomics have opened the way to widespread use of proteomics in clinical applications and personalized medicine. Also, new MS-based approaches for profiling the spatial proteome/lipidome/metabolome in tissues are important for understanding biological complexity in health and disease. Here, I propose an interdisciplinary project utilizing novel proteomics techniques to exploit the power of proteomics to address challenges in personalized medicine.
John N. Parker	I am a sociologist of science with expertise in how to best create the kinds of organizations, groups, and social interactions that best facilitate creative interdisciplinary research. My interest is in joining a team to help them plan how they might best organize themselves for interdisciplinary success.
Jonathan Polfus	Artificial synapse, physical neural networks	Development of solid-state electrochemical synapses based on resistive switches. These can be modulated by incorporation of protons, which emulates the strengthening and weakening of synapses in the brain.
Johanne Rinholm	Cellular mechanisms in stroke	Preclinical development of new stroke treatment
J?rgen Sugar	Neuroscience, Memory	Attention is important for memory. How is attention formed in the brain and how is it used to successfully form memories? My idea is to record neural data in humans while they perform a memory task that is highly dependent on attention.
Christian K. Tamnes	Brain development, adolescents, mental health, MRI, environmental factors

Personalised medicine

Name	Research topic(s) of interest and/or competence	Title or short description (2-3 sentences) of initial
Ulysse C?t��-Allard	Machine learning, wearable, human-computer interface, digital biomarkers	Health state recognition and trajectory prediction.
Nina Edin	Radiation physics, radiobiology	Convergence environment on Radiation and Health
Jorrit Enserink	Immunotherapy; prediction of outcome; cancer heterogeneity; patient stratification; drug screening; leukemia	Immunotherapy can provide remarkable clinical benefit to cancer patients; however, only a subset of patients responds to immunotherapeutic treatment. Being able to differentiate between responders and non-responders will have a major impact on the accuracy of patient stratification and thereby on treatment outcome. The goal of our environment will be to bring together experts in the field of cancer biology, mathematical modeling, and law/ethics to develop novel personalised medicine methods for immunotherapeutic treatment of a variety of cancer types.
Camila Esguerra	Disease models, Drug discovery, Tissue xenografting, Toxicology
Steffen Maude Fagerland	fNIRS, fMRI, brain training
Yael Friedman	Philosophy of medicine; Philosophy of science in practice.	Knowledge gaps and epistemic injustice in medical research.
Osman Gani	Computer-aided drug design (CADD). Molecular Modeling
Helene Kn?velsrud	Autophagy, kidney cancer, drosophila	Autophagy in sleep and restoration
Alvaro K?hn-Luque	Mathematical modeling, knowledge-driven machine learning, digital twins, personalized cancer therapies, biomarkers, genomics, medical imaging.	Personalized cancer therapy integrating multi-type clinical data, biological experimentation, mechanistic mathematical models and artificial intelligence.
Soili Marianne Lehto	Depression, stress, psychobiology, psychoneuroimmunology, psychosomatics	Several options possible based on potential collaborators' orientation and interests. I have ongoing projects (with focus on above topics of interest/competence) with multiple existing datasets, both clinical and population-based, ranging from 100 to ca. 500,000 individuals. Most samples include biomaterial banks (serum, plasma, whole blood, placental tissue, hair).
Siri Leknes	Applying psychology to understand health consequences of clinical decisions and clinical encounters, Opioid effects; pain; addiction; anaestesia, Affective neuroscience, social neuroscience	Applying psychology to understand consequences of clinical decisions
Anthony Mathelier	computational biology, gene expression regulation, multi-omics, machine learning, AI, transcription factors, cis-regulatory variations
Hilde Loge Nilsen	Ageing, Machine learnring, AI, Multi-omics data, Longitudinal, Age-related diseases	Age is the primary cause and risk factor for the majority of diseases, slowing down aging has huge personal and societal benefits by minimizing the disease burden that comes with aging process. We aim to develop a framework for personalised healthy aging interventions using machine-learrning to intergrate longitudinal clinical data and multiomic data.
Tuula Nyman	clinical proteomics, mass spectrometry, spatial proteomics, personalised medicine	Proteomics is the ��large scale study of proteins��, and proteome level information is critically important in biomedical research. Recent advances in mass spectrometry (MS)-based proteomics have opened the way to widespread use of proteomics in clinical applications and personalized medicine. Also, new MS-based approaches for profiling the spatial proteome/lipidome/metabolome in tissues are important for understanding biological complexity in health and disease. Here, I propose an interdisciplinary project utilizing novel proteomics techniques to exploit the power of proteomics to address challenges in personalized medicine.
John N. Parker	I am a sociologist of science with expertise in how to best create the kinds of organizations, groups, and social interactions that best facilitate creative interdisciplinary research. My interest is in joining a team to help them plan how they might best organize themselves for interdisciplinary success.
Biswa Sahu	Cancer epigenetics, gene expression control, enhancer malfunction and epigenome reprogramming by transcription factors, next-generation sequencing and single cell multomics	Understand the role of non-genetic mechanisms such as enhancer and epigenome reprogramming in cancer initiation, progression, metastasis and treatment resistance. Using high resolution enhancer landscapes in accessible chromatin and gene expression data from single cell analysis.
Dipak Sapkota	Biomarkers in white and red lesions in oral cavity and oral cancer; quality of life in patients with white and red lesions in oral cavity and oral cancer, development of in vitro models of oral mucosa/oral cancer for functional studies, drug testing
Chloe Steen	Computational biology, cancer biology, data science
Joanna Sulkowska	Precision cardiology
Henrik Vogt	Personalized/precision medicine, Systems medicine, Digital health, Health technology assessment, Research ethics, Precision public health, General practice
Manuela Zucknick	Statistical learning/ machine learning (ML) and AI for translational and clinical cancer research, in particular for personalised cancer therapies. Integration of data from diverse sources (multi-omics, drug screens, clinical, imaging, genomics) for prediction of drug response and synergies in pharmacogenomic screens and of prognosis and treatment response in patients. Machine learning for small and noisy (biomedical) data, e.g. through integration of knowledge and structure in ML.	AI and robotics for personalised cancer medicine: Next-generation pipelines and tools for overcoming drug resistance in cancer by studying the full dynamics and evolution of the tumor and its environment in large-scale screens, in vitro, in vivo and in silico.

Innovative health technologies and drug development

Name	Research topic(s) of interest and/or competence	Title or short description (2-3 sentences) of initial
Melinka Butenko	Signalling systems, toxins, structural biology, peptide activity, plant pathogen interactions.
Ulysse C?t��-Allard	Machine learning, wearable, human-computer interface, digital biomarkers	Health state recognition and trajectory prediction.
Nina Edin	Radiation physics, radiobiology	Convergence environment on Radiation and Health
Jorrit Enserink	Immunotherapy; prediction of outcome; cancer heterogeneity; patient stratification; drug screening; leukemia	Immunotherapy can provide remarkable clinical benefit to cancer patients; however, only a subset of patients responds to immunotherapeutic treatment. Being able to differentiate between responders and non-responders will have a major impact on the accuracy of patient stratification and thereby on treatment outcome. The goal of our environment will be to bring together experts in the field of cancer biology, mathematical modeling, and law/ethics to develop novel personalised medicine methods for immunotherapeutic treatment of a variety of cancer types.
Camila Esguerra	Disease models, Drug discovery, Tissue xenografting, Toxicology
Steffen Maude Fagerland	fNIRS, fMRI, brain training
Yael Friedman	Philosophy of medicine; Philosophy of science in practice.	Knowledge gaps and epistemic injustice in medical research.
Osman Gani	Computer-aided drug design (CADD). Molecular Modeling
Stefan Krauss	organoids, organ models	stem cell derived embryo models
Alvaro K?hn-Luque	Mathematical modeling, knowledge-driven machine learning, digital twins, personalized cancer therapies, biomarkers, genomics, medical imaging.	Personalized cancer therapy integrating multi-type clinical data, biological experimentation, mechanistic mathematical models and artificial intelligence.
Anders Malthe-S?rensen	Neuroscience, gene therapy, de novo protein design, machine learning, docking simulations	Our goal is to develop a platform technology for gene therapies for central nervous system diseases combining generative AI models for construct and capsid development, in vitro and in vivo experimental pipelines, and an integral RRI approach to address fundamental relevant ethical issues.
Cecilie Morland	Stroke	Preclinical development of new stroke treatment
Tuula Nyman	clinical proteomics, mass spectrometry, spatial proteomics, personalised medicine	Proteomics is the ��large scale study of proteins��, and proteome level information is critically important in biomedical research. Recent advances in mass spectrometry (MS)-based proteomics have opened the way to widespread use of proteomics in clinical applications and personalized medicine. Also, new MS-based approaches for profiling the spatial proteome/lipidome/metabolome in tissues are important for understanding biological complexity in health and disease. Here, I propose an interdisciplinary project utilizing novel proteomics techniques to exploit the power of proteomics to address challenges in personalized medicine.
John N. Parker	I am a sociologist of science with expertise in how to best create the kinds of organizations, groups, and social interactions that best facilitate creative interdisciplinary research. My interest is in joining a team to help them plan how they might best organize themselves for interdisciplinary success.
L��on Reubsaet	microsampling, dried blood spots, advanced protein determination (using LC-MS) from biological samples, innovative sampling, complex sample preparation integrated in paper (smart sampling), targeted protein determination, analytical chemistry	Our research is focused on revolutionizing the workflow for LC-MS-based protein determination by integrating traditionally tedious sample preparation steps directly into the paper used for collecting dried blood samples or other microsampling devices (smart sampling). This innovative method leverages the significant advantages of dried blood samples, which can be collected in virtually any environment without the need for trained personnel. Upon receipt of the dried samples in the laboratory, we streamline and enhance protein analysis using advanced LC-MS techniques. By embedding preparation steps into the sampling paper itself, we significantly reduce both time (up to several days) and labor, while seamlessly maintaining the existing workflow from sample collection to analysis. We are seeking to collaborate with groups that recognize the potential of this approach and are interested in implementing smart sampling in their workflow.
Johanne Rinholm	Cellular mechanisms in stroke	Preclinical development of new stroke treatment
Biswa Sahu	Cancer epigenetics, gene expression control, enhancer malfunction and epigenome reprogramming by transcription factors, next-generation sequencing and single cell multomics	Understand the role of non-genetic mechanisms such as enhancer and epigenome reprogramming in cancer initiation, progression, metastasis and treatment resistance. Using high resolution enhancer landscapes in accessible chromatin and gene expression data from single cell analysis.
Dipak Sapkota	Biomarkers in white and red lesions in oral cavity and oral cancer; quality of life in patients with white and red lesions in oral cavity and oral cancer, development of in vitro models of oral mucosa/oral cancer for functional studies, drug testing
Nikolina Sekulic	chromatin, structural biology, cryoEM, protein engineering, drug discovery, mapping Ab-Ag interactions	We have experience in the purification and structural characterisation of proteins. We have technology for mapping protein-drug interactions and can assist in the development of protein targets and/or biologics with increased potency or binding affinity.

Health and society

Name	Research topic(s) of interest and/or competence	Title or short description (2-3 sentences) of initial
Diana Domanska	Ageing, Machine learning, AI, multi omics, longitudinal data	Healthy ageing �C Prevention is better than cure. Using clinical data and biomaterial we want to define signatures in the general population that characterises ageing driven DNA damage.
Nina Edin	Radiation physics, radiobiology	Convergence environment on Radiation and Health
Camila Esguerra	Disease models, Drug discovery, Tissue xenografting, Toxicology
Yael Friedman	Philosophy of medicine; Philosophy of science in practice.	Knowledge gaps and epistemic injustice in medical research.
Soili Marianne Lehto	Depression, stress, psychobiology, psychoneuroimmunology, psychosomatics	Several options possible based on potential collaborators' orientation and interests. I have ongoing projects (with focus on above topics of interest/competence) with multiple existing datasets, both clinical and population-based, ranging from 100 to ca. 500,000 individuals. Most samples include biomaterial banks (serum, plasma, whole blood, placental tissue, hair).
Siri Leknes	Applying psychology to understand health consequences of clinical decisions and clinical encounters, Opioid effects; pain; addiction; anaestesia, Affective neuroscience, social neuroscience	Applying psychology to understand consequences of clinical decisions
Anthony Mathelier	computational biology, gene expression regulation, multi-omics, machine learning, AI, transcription factors, cis-regulatory variations
Julien Mayor	Language acquisition, cognitive development, eye-tracking, language assessments, online data collection	How does variability in the learning environment impact language acquisition? We plan on looking into sources of variability in the learning environment of young children (dialects, languages, contexts, etc) and their impact on early language development, as well as on their potential effect on language delays and impairments.
Hilde Loge Nilsen	Ageing, Machine learnring, AI, Multi-omics data, Longitudinal, Age-related diseases	Age is the primary cause and risk factor for the majority of diseases, slowing down aging has huge personal and societal benefits by minimizing the disease burden that comes with aging process. We aim to develop a framework for personalised healthy aging interventions using machine-learrning to intergrate longitudinal clinical data and multiomic data.
John N. Parker	I am a sociologist of science with expertise in how to best create the kinds of organizations, groups, and social interactions that best facilitate creative interdisciplinary research. My interest is in joining a team to help them plan how they might best organize themselves for interdisciplinary success.
Astrid Marie Jorde Sands?r	Genetic research, education, mental and physical health, register data, causal inference, inclusion and diversity, sociogenomics.	How to achieve equality and inclusion in the face of growing societal challenges? Interdisciplinary project (economics of education and health, sociology, psychology and genetic epidemiology) combining methodological approaches (experimental, quasi-experimental, machine learning and genetically sensitive designs) and unparalleled data linkage (register data and detailed survey and genomic family data).
Dipak Sapkota	Biomarkers in white and red lesions in oral cavity and oral cancer; quality of life in patients with white and red lesions in oral cavity and oral cancer, development of in vitro models of oral mucosa/oral cancer for functional studies, drug testing
Christian K. Tamnes	Brain development, adolescents, mental health, MRI, environmental factors
Henrik Vogt	Personalized/precision medicine, Systems medicine, Digital health, Health technology assessment, Research ethics, Precision public health, General practice

No specified topic

Name	Research topic(s) of interest and/or competence	Title or short description (2-3 sentences) of initial