Technical tools in data management and processing

• Head: Yang Bai, PhD, Docent, Assistant Professor (tenure-track)
• Faculty: Faculty of Information Technology and Electrical Engineering
• Group composition: One assistant professor, one postdoc, and four doctoral researchers; Research unit: Two full professors, two assistant professors, five senior researchers, 11 postdocs, and 16 doctoral researchers
• Website: https://www.oulu.fi/en/university/faculties-and-units/faculty-information-technology-and-electrical-engineering/microelectronics
• Contact: yang.bai (at) oulu.fi

Physical body signals such as conscious and unconscious muscle movement, including change of muscle status, reflect early signs of potential human health issues. For instance, cough is the most common symptom for which individuals seek medical advice. In ICU (intensive care unit), especially for patients who have gone through tracheostomy, weak swallowing strengths may cause saliva going to lungs and thus inducing pneumonia. For spine trauma patients, the strength of breathing or coughing may indicate recovery state. Such types of human body signals need to be monitored closely and preferably autonomously. Beside acute and clinical patients, daily health status of outpatients should also be monitored wirelessly so that the number of follow-up visits to doctors can be reduced and thus to save healthcare resources. For instance, the number of patients recovering from anaesthesia is incredibly large, but their recovery requires careful monitoring. To fulfil the demand of acquiring massive health data from body signals, this research will explore feasible methods for data collection as well as analysis to deliver relevant critical health information using energy harvesting systems as energy-efficient, intelligent, and autonomous sensing systems. Energy harvesters convert ambient energy in the working environment, including optical, kinetical, and thermal energy, into electrical signals. Therefore, energy harvesters are naturally smart sensors which can dynamically respond to ambient stimuli. In this research, the stimuli will be representative muscle movements triggered by coughing, breathing, swallowing, etc. The methodology of this research is to collaborate with clinics and/or medical researchers to figure out a series of appropriate body signals that can be reliably detected by energy harvesting systems and thus can be potentially used as health data which help to predict and diagnose early diseases and health status, respectively.

• Head: Lauri Eklund, Prof.
• Faculty: Biocenter Oulu (BCO) and Research Unit of Health Sciences and Technology, Faculty of Medicine
• Group composition: 2 Prof., 1 senior researcher, 4 post docs, 5 PhD students, 1 engineer, 2 technicians
• Websites: https://www.oulu.fi/en/university/faculties-and-units/biocenter-oulu and
• https://www.oulu.fi/en/university/faculties-and-units/faculty-medicine/research-unit-health-sciences-and-technology
• Contact: lauri.eklund (at) oulu.fi

We seek a post-doctoral fellow to participate in the project aiming to develop image analysis tools for quantitative and qualitative analysis of extracellular matrix (ECM) content and organization. Exploded data quantities have generated a bottleneck for scientific breakthroughs and innovations in biomedical imaging. There is an urgent need to develop automated image analysis tools to fuse multimodal microscopic data and provide quantitative outcomes with supporting visualization. Here, we are seeking a post-doctoral fellow to participate the development for harmonized data storage integrated with state-of-the-art image analysis including image segmentation with neural networks and complex morphometric analyses. The overall aim is to provide accurate information on collagen content and organization in the various sample materials. Quantification of image data will provide more information from qualitative image information gathered from e.g. various diseased tissues, and allow more accurate statistical analysis of changes in different fibrotic diseases and treatment responses.

ECM remodeling is essential for tissue homeostasis but when dysregulated, it leads to pathological processes including tissue fibrosis and cancer. Fibrosis and ECM research is one of the focus areas of the University of Oulu. FibroHealth is one of the UO interdisciplinary research fields comprised of several Faculties and covers two strategic research themes, fibrosis diseasome and fibrobesity, both supported by University proliferation funding from Research Council of Finland (11.5 M€).

Biocenter Oulu light and electron microscopy core facilities provide access to state-of-the-art confocal imaging techniques enabling real-time imaging of ECM in fixed and living cells, tissues and animals, and is selected as imaging service provider in European wide EuroBioimaging ERIC. 3D serial sectioning tomography of tissues, and light sheet and multiphoton microscopy of mesoscale optically cleared specimens provide additional imaging modalities to assess tissues and ECM macromolecular assemblies. In the project, a post-doctoral fellow will contribute for the development of image analysis tools for detecting abnormalities, changes in volume and quantity of ECM in various disease models including mouse and human samples.

The project is done in collaboration with Research unit of Health Science and Technology who are using x-ray microscopy, micro-computed tomography and spectroscopic imaging for in-depth understanding of ECM and fibrosis. Additionally, collaborations with various industry partners enhance our capabilities for research and development.

• Head: Prof. Caglar Elbuken
• Faculty: Faculty of Biochemistry and Molecular Medicine
• Group composition: Prof. Ilkka Nissinen, Dr. Tatiana Avsievich
• Website: https://www.oulu.fi/en/research-groups/elbuken-lab-microfluidics-and-biosensor-research-group
• Contact: caglar.elbuken (at) oulu.fi

Flow cytometry is a tool used in microbiology, immunology, cancer biology for individual identification of non-homogenous cell populations. Its extension with cell sorting also makes it indispensable in any clinical analysis where postprocessing of clusters is essential. However, conventional flow cytometry relies on fluorescent labeling for phenotyping and is limited with the broad emission spectra of commonly used fluorophores. Raman flow cytometry has emerged as a technique to tackle these limitations. The slow response time of the Raman signal acquisition was addressed by coherent Raman scattering, However, this method has not found any clinical use due to the well-developed protocols that rely on existing labeling protocols. To address this problem, we propose a high-throughput time-gated Raman flow cytometry that has the capacity to probe both conventionally fluorescently tagged and non-labelled samples simultaneously. This technique combines the benefits of conventional flow cytometry and Raman flow cytometry, using time-gating to suppress the Raman suppressing fluorescence signal.

Advancements in single cell analysis techniques have already entered routine practice by integrating genomics and transcriptomics data. However, metabolomics is still lagging since a non-invasive high throughput metabolite detection technique is not available. The proposed flow-based system addresses this challenge and is a candidate for microbiology and lipid biology applications. The system will be combined with machine-learning tools for higher clustering precision. The availability of inherent fluorescent suppression ability provides a built-in system for obtaining high fidelity training sets. The complexity of Raman spectral data lies in its detailed nature which requires data analytics to distinguish specific molecular components. To overcome the challenges presented by the complexity of spectral data, the project utilizes data analytics. This approach significantly enhances the ability to recognize specific molecules, especially lipids due to their strong Raman signal, overcoming traditional limitations in spectral analysis. Verification of the technology will be performed using tumor samples. The system will utilize microfluidic integration and will be developed towards high throughput liquid biopsy.

This collaborative project unites the expertise of the Microfluidics and Biosensors (MB) research group from the Faculty of Biochemistry and Molecular Medicine and the Electronics Circuits and Systems (CAS) research unit from the Faculty of Information and Electrical Engineering. This setting provides a unique research training opportunity in a multidisciplinary project utilizing the infrastructure of both research units. MB research group specializes in the development lab-on-a-chip systems, combining microfabrication, chemistry, and optics, and focusing on innovative healthcare applications. The team is currently composed of ten researchers and focuses on artifical cells, cell separation and organoids. The CAS unit excels in the use of novel time-gated Raman spectroscopy, employing advanced CMOS sensor circuits for high-precision analysis overcoming conventional Raman spectroscopy. This technique allows for the elucidation of the composition, structure, and dynamics of metabolites, essential for determining their applicability in clinical contexts.

Merging microfluidics, microfabrication, and state-of-the-art time-gated Raman spectroscopy, enriched by the power of data analytics, this project aspires to provide a novel tool that will have a wide range of application for complex flow cytometry applications.

• Head: Simo Hosio, Associate Professor
• Faculty: Faculty of Information Technology and Electrical Engineering, Center for Ubiquitous Computing
• Group composition: 1 Assoc. Prof., 1 postdoc, 4 doctoral researchers, 6 research assistants
• Website: https://ubicomp.oulu.fi/research/cc
• Contact: simo.hosio (at) oulu.fi

The Center for Ubiquitous Computing (UBICOMP) in the Faculty of Information Technology and Electrical Engineering welcomes a Postdoctoral Researcher to join the Crowd Computing (CC) research group (https://ubicomp.oulu.fi/research/cc), to conduct research on merging health informatics and data science to revolutionise the understanding and improvement of sleep quality and overall wellbeing.

UBICOMP is a vibrant research center with 65 employees, of which 70% are international research and teaching staff. In the most recent international assessment of the research conducted in the University of Oulu, UBICOMP was awarded the highest rating across all evaluation criteria. UBICOMP comprises of five independent research groups, of which the Crowd Computing (CC) group led by Associate Professor Simo Hosio investigates distributed forms of work, including crowd-powered decision support through modelling large-scale subjective data.

In this scope, you will have a unique opportunity to contribute to healthcare by employing the latest digital sleep-tracking technologies and data analysis techniques. The focus of the project is two-fold: 1) Prototyping new technologies for end-users, and 2) Discovering patterns and correlations between various lifestyle factors and sleep health. The objective is to engage with end-users who can directly benefit from your work.

Prototyping involves the development of mobile and web tools that integrate commercial API services from wearable vendors and combine this data with mobile usage data through visualisations and data analysis tools. The information is provided to end-users who can manage their personal data on the web platform.

Discovering patterns and correlations involves analysing wearable health data and mobile usage data to discover actionable data insights, patterns, and correlations between lifestyle and well-being. The project will use the latest machine learning and AI-based tools to enable users to engage through actionable data-driven insights - lifestyle changes that could have a measurable positive effect on their wellbeing and sleep.

Ultimately, you will have the freedom to choose your focus according to your personal interests.

In summary, your work will involve the collection and analysis of sleep data using state-of-the-art data science methodologies. This position promises professional growth through research and publication opportunities but also a chance to participate in the supervision of junior scholars. The project is distinctive in its approach to utilising real-world, user-generated data to drive healthcare interventions.

• Head: Jianan Huang, Assistant Professor
• Faculty: Faculty of Medicine, Faculty of Biochemistry and Molecular Medicine
• Group composition: Jianan's group has 1 visiting professor, 2 postdocs, 4 doctoral researchers, 2 research assistants
• Website: https://www.oulu.fi/en/research-groups/jianan-lab-nanophotonic-biosensors
• Contact: jianan.huang (at) oulu.fi

Proteins are the workhorses in biological systems that can be used to characterize cellular functions and progress of diseases as biomarkers. However, the fact that proteins cannot be amplified results in a serious lag of proteomics behind genomics and transcriptomics. Conventional protein detection and sequencing rely on mass spectroscopy whose low sensitivity is challenging for not only clinical applications but also mechanistic studies such as the single-cell proteomics. While portable single-molecule DNA sequencers have been brought to African and Brazil villages for sequencing Ebola and Zika virus, no corresponding technologies exist for protein sequencing.

The big gap could be filled by the emerging single-molecule protein sequencing technology that combines plasmonic nanopores with surface enhanced Raman spectroscopy. As one of the pioneering groups, the PI's group has developed novel plasmonic nanopores to demonstrate label-free Raman spectroscopic detection of single amino acid residues in single peptide. In strong contrast to the averaged Raman spectra with fixed peak positions of molecule solutions or solids, the single-molecule Raman data exhibit peak blinking and shifting due to the fact that the molecule diffuses in the plasmonic nanopore. Therefore, the fragments of individual protein can be monitored through the dynamic single-protein Raman data, which can be used to reconstruct the sequence of the whole protein. Because existing algorithms of protein sequencing are for fluorescence and mass spectroscopy, new algorithms of data analysis and protein sequencing based on the single-molecule Raman spectroscopy need to be developed.

In this research topic, we aim to develop new machine learning algorithms to analyze the dynamic single-molecule Raman data from the plasmonic nanopore to firstly identify the amino acid residues, then reconstruct the sequence of the peptide or protein fragment, and eventually find the parent protein in a proteome database. It is synergetic with and supported by the PI's Academy Research Fellow project (TwoPoreProSeq), BCO emerging project (DigiRaman) and BCO project (LiveProSeq) that together include 1

postdoc fellow and 4 doctoral researchers for sensor fabrication and measurement, 2 doctoral researchers for detection automation and 2 doctoral researchers for data analysis. As a starting point, we have used the plasmonic nanopore biosensors to collect large amount of unprecedented single-molecule Raman spectra of 20 proteogenic amino acids, peptides and their post-translational modifications. The data-analysis team of 2 doctoral researchers is developing deep learning models to classify them.

We are now looking for a postdoctoral researcher to join the data-analysis team and lead this research topic. The topic belongs to the Data4Healthcare Theme 3) Technical tools in data management and processing. It will be co-supervised by the machine learning expert, Prof. Simo Saarakkala, from Faculty of Medicine and the bioinformatics expert, Associate Prof. Valerio Izzi, from Faculty of Biochemistry and Molecular Medicine in University of Oulu. This topic will contribute to the PI's research cluster on single-protein Raman sequencing technologies that could open a new era for single-cell proteomics and diagnostic devices able to identify low-abundance protein, protein modifications and new biomarkers, benefiting early diagnosis of diseases, personalized healthcare, and precision medicine.

• Head: Adjunct Professor Miia Jansson
• Faculty: Faculty of Medicine
• Group composition: Three postdocs (starting), six doctoral researchers, and two project researchers
• Website: https://www.oulu.fi/en/research-groups/digiimpact
• Contact: miia.jansson (at) oulu.fi

Research group is researching the impact of digital solutions in acute and critical care. Our goal is to improve short- and long-term outcomes of critical illness by developing a new real-time dynamic prediction models to tackle the timeliness and accuracy of the current static and intermittent risk assessment in a sustainable manner.

Intensive care (ICU) is a high-tech environment for intensive and invasive monitoring. ICU clinical information systems collect, store, process, and present high-volume, high-velocity, and high-variety information (e.g., structured and unstructured data, waveforms) from multiple systems (e.g., laboratory, pathology, and radiology systems) and solutions (e.g., monitors, ventilators, infusion pumps) simultaneously, continuously, and synchronously to support clinical decision-making. Predictive analytics can be used to estimate the probability of either presenting (diagnostic models) or developing a particular disease or outcome (prognostic models) whereas the best prediction performance can be detected by combining multimodal data (e.g., clinical, laboratory, and radiological variables, physiological signals). There is, however, a complete lack of time-dependent predictors to predict patient outcomes continuously.

The DigiImpact is an interdisciplinary research group that brings together a diverse team of experts (e.g., clinicians, epidemiologists, technology specialists, data scientists), who work in Finnish and international companies, hospitals, universities and research organizations to reduce the global burden of critical illness. We are also active in regional, cross-border and -boundary collaboration to scale our innovative solutions.

Our mission is to improve risk-based prevention, early detection and personalized care by utilizing AI, big data and machine learning. We use multimodal data from various sources to develop new machine learning-based prediction models to identify an individual who would benefit from a certain type of treatment based on the predicted disease course.

• Head: Miguel Bordallo López
• Faculty: Faculty of ITEE (Research group: Multimodal Sensing Lab, Research Unit: Center for Machine Vision and Signal Analysis - CMVS)
• Group composition: At the Multimodal Sensing Lab research group: 1 professor, 4 post-docs, 4 phD students, 4 Master Students. At the Center for Machine Vision and Signal Analysis research unit: 6 Professors, 6 Senior Scientists, 25 post docs, 30 doctoral students, 10 Master Students, 10 visiting researchers, 2 support personnel.
• Websites: CMVS: https://www.oulu.fi/cmvs
• Multimodal Sensing Lab: https://sites.google.com/view/miguelbordallo/team
• Collaborator NE Device SW & Vitacam: https://www.nedevicesw.com/
• https://vitacam.health/
• Contact: miguel.bordallo (at) oulu.fi

The SViSenS research project, seeks to revolutionize healthcare through advanced technology. The project identifies a critical gap in current healthcare diagnostics, where physical examinations and doctors' oversights can lead to significant misdiagnoses. To address this, SViSenS proposes the innovative use of real-time video analysis, integrated with Artificial Intelligence (AI) and Computer Vision, to monitor human behavior and detect early signs of diseases. This approach aims to assist healthcare professionals in making more accurate diagnoses.

SViSenS intends to overcome the limitations of existing healthcare technologies that currently relies heavily on storing and processing raw image and video data on remote servers. This conventional method raises concerns about the security, safety, and privacy of sensitive healthcare information. The project's solution, to be developed in collaboration with the local industrial partner NE Device SW and its IIA Medical Device Vitacam, is to develop sensorized cameras and imaging devices that discard raw image and video footage, focusing instead on extracting and streaming only quantifiable, medically relevant features. This ensures data privacy and security while maintaining patient anonymity. The approach is grounded in four principles: real-time analysis for immediate information extraction, data minimization, privacy preservation, and scalable data storage and management.

The focuses on three interlinked research questions: 1) Explore the most effective methods for real-time extraction of medically relevant features from video data, 2) find ways to sensorize cameras to capture only essential information, and 3) applying the developed algorithms specific healthcare use cases with large amounts of available data, such as pediatric respiratory infections, stroke, Parkinson's disease, and psychiatric conditions related to depression. Additionally, the project investigates the potential challenges and limitations associated with sensorized camera systems in terms of data quality, feature selection, and algorithm performance, aiming to address these through iterative development, testing, and optimization.

The expected outcomes of the SViSenS project are groundbreaking in terms of methodological implementation and sensing capabilities. The project anticipates developing real-time feature extraction algorithms and sensorized camera systems that preserve patient anonymity. By applying these technologies to four use cases and addressing their inherent challenges, SViSenS aims to establish a new paradigm in healthcare data management. The project's potential for scientific breakthroughs lies in its approach to healthcare data acquisition and analysis, which places a strong emphasis on privacy, security, and quality healthcare outcomes.

SViSenS stands at the intersection of healthcare systems, computer vision, and embedded computing, aiming to provide a comprehensive understanding for future privacy-preserving healthcare applications, considering the constraints of real-world scenarios as underscored by the collaboration with NE Device SW Ltd. and the use of practical, data-driven health solutions. The project echoes Data4Health's emphasis on harnessing data for practical health solutions (Theme 1), the improvement of health data management practices (Theme 2), and the development of advanced technical tools that include predictive analytics, novel computational methods and AI (Theme 3).

The SviSenS project is led by Associate Professor Miguel Bordallo López at the Center for Machine Vision and Signal Analysis. SviSenS aligns closely with the Data4Health programme at the University of Oulu.

• Head: Jouko Miettunen, professor
• Faculty: Faculty of Medicine
• Group composition: 1 professor, 6 postdocs, 5 doctoral researchers, 2 statisticians
• Website: https://www.oulu.fi/en/research-groups/psychiatric-epidemiology-northern-finland-birth-cohort-studies-1966-and-1986
• Contact: jouko.miettunen (at) oulu.fi

The research group investigates different topics relating to psychiatric epidemiology. The research topics include for instance risk and outtcome research on psychosis, depression, bipolar disorder and alcohol use. Schizophrenia and other psychotic disorders are a major public health problem and leading unsolved disease afflicting about 3-4 % of the population. In Finland, the prevalence of any psychotic disorder is even higher; 4.5 %. Schizophrenia is a complex genetic disorder manifesting combined environmental and genetic causation. To date, the candidate risk factors include e.g. advanced paternal age, obstetric complications, cannabis use, motor dysfunction, urbanicity, and low IQ. Data from Northern Finland birth cohorts have underpinned many of these key findings. Depression is major public health concern and affect about 10% of population. Rick factors include various biological and social factors, personality and life events. The team investigates also psychosocial factors on cancer and effect of temperament on physical and mental health. The team has long experience in epidemiology and access to unique data sets, the findings are likely to have both clinical and public health influence.

• Head: Maisa Niemelä, Postdoctoral researcher
• Faculty: Faculty of Medicine
• Group composition: 2 professors, 4 postdoctoral researchers, 6 doctoral researcher
• Website: www.oulu.fi/en/research-groups/wearables-for-population-health
• Contact: maisa.niemela (at) oulu.fi

Wearables for population health research group focuses on development of novel algorithms for wearable activity monitors tailored to assess 24-hour movement behaviors. The group is dedicated in revealing how the use of time and patterns of movement behaviors are related to several health outcomes. The research group utilizes data from large population-based cohort studies, integrating data from wearables, health records, and behavioral factors.

Movement behaviors occurring in 24-hour days are associated with many major health outcomes and risk of non-communicable diseases such as cardiometabolic diseases. Previous research has relied on self-reported measures of physical activity or measured waking hour activity behaviors with accelerometers enabling more precise estimates on sedentary time, light intensity activity and moderate-to-vigorous intensity physical activity. However, recently the codependent relationship within sleep and waking behaviors and the association of this composition to health has been recognized as well the versatile health effects of sleep duration and quality. Yet, accelerometer-based activity monitoring has failed to precisely measure sleep because accelerometers measure movements and their intensity and lack of motions makes it difficult to distinguish between sedentary time on waking hours and sleep. With multimodal wearable measurement setups, recognizing sleep periods from the 24-hour activity patterns has shown promising results compared to purely self-reporting times in bed or accelerometer only measurements. When continuous measurements of heart rate, heart rate variability, respiratory rate, temperature, and movements are combined, other metrics than just sleep duration like sleep efficiency, quality, restorativeness, and sleep stages can be assessed. These metrics are of importance in recognizing healthy sleep patterns and determining how interrelationship between sleep and waking hours activity behaviors affect these behaviors and their health effects.

In the Northern Finland Birth cohorts (https://www.oulu.fi/en/university/faculties-and-units/faculty-medicine/…), our research group has measured from 2012 onwards from more than 7000 individuals habitual 24-hour movement and sleep behaviors with different wearable sensors. Up to 14 days measurement duration during daily living has enabled recognition of various movement patterns and profiles predicting future disease risk and healthy aging. We have also measured sleep patterns with Oura rings from more than 1600 cohort members. Our group focuses on innovative analytical methods to reveal the associations between different movement patterns and life-long health outcomes. The aim of this project is recognizing novel patterns and features from the 24-hour movement behavior data especially focusing on sleep and recovery. The knowledge produced enables us to target evidence-based, effective preventive interventions to those at risk in future.

• Head: Ilkka Nissinen
• Factulty: Faculty of Biochemistry and Molecular Medicine
• Group composition: Profs. 4 and PhDs 2
• Website: https://www.oulu.fi/fi/yliopisto/tiedekunnat-ja-yksikot/infotech-oulu
• Contact: ilkka.nissinen (at) oulu.fi

To create optimal medical diagnostic value the analyte measures should allow identification of its detailed molecular compositions in association to a disease. Such measures can be provided by the electromagnetic spectrum (EMS) based applications. Use of the EMS diagnostics offers molecular classification on the grounds of emission and absorption relationships at certain frequencies serving to biochemically characterize the samples. The more in depth spectroscopic analytic results can be obtained if machine learning strategies will be integrated. The other line of diagnostic research relies on specific molecular depicting reagents. These are offered by recombinant and synthetic antigens and antibodies against them, nanobodies, aptamers and DNA probes and molecularly imprinted polymers. The reagents were used to enable massive need for citizen science-based SARS-CoV2 diagnostics in COVID-19.

However, there is now a great need to be able to diagnose single cells, single molecules and single cell secreted extracellular vesicles (EV) with its multiple molecular analytes. EVs represent a rather recently identified complex and a robust secretory information system. In EVs are assembled in cells in their multivesicular bodies as nano- and micro size secreted droplets. Human health status involves in theory millions of specifically molecularly assembled EVs whose composition depends on exposomic/environmental conditions.

This EU CODUND Data4Healthcare post-doctoral project focuses to advance technology to enable EVs molecular composition at single and multiple, an EV array level. Moreover, given the large degree of EV molecular variation a challenge is to translate EV analysis to the real world, situation. Such capacities can be offered by the citizen science concept that represents a co-creation strategy where citizens help to collect the data. The aim here is in this Data4Health project to advance the capacities to diagnose the excreted EV based analytes in sweat, saliva or urine. For this purpose, we propose to take use of the Time-Gated/Raman technologies and machine learning to obtain measures for specific biological sport parameters. Better analytic powers to target sports medicine related needs such as aerobic/anaerobic exercise load, recovery from intense exercise period and strategies to offer more precision sports values and efficient training. These proposed measures will be conducted in the ODL sports clinic and VTT in collaborating sports network partner premises. We will capitalize to take use of our novel biological wearable solutions and aim to advance establishment of a bio- and databank based on sweat excreted EV analytes derived from wearable blasters. Putative secondments: VTT, ODL Sports Clinic/KIHU and Time Gated Instruments Oy.

• Head: Research Director, Adj. Prof. Susanna Pirttikangas
• Faculty: Faculty of Information Technology and Electrical Engineering, Center for Ubiquitous Computing
• Group composition: 3 Professors, 1 University Teacher, 4 Postdoctoral researchers,7 Doctoral researchers, 1 Project Engineer, 1 Project researcher
• Website: https://ubicomp.oulu.fi/research/iedge
• Contact: susanna.pirttikangas (at) oulu.fi

Citizens are the most valuable assets of the cities. Therefore, the quality of life of citizens, their health, and general well-being play the most important role in the prosperity of the city and the society overall. Understanding citizens’ quality of life and well-being in the city is challenging, as these constructs are composed of diverse subjective and objective parameters.

Nowadays, technology has become ubiquitous. Cities embed ICT to get insights and enhance their processes, citizens use connected devices to monitor their habits and health. By collecting measured data, available quantitative and qualitative information from Smart Cities and citizens, we assume that we should be able to provide insights into city performance and citizens’ life.

However, there are several challenges, ranging from data quality and availability, management and analysis, to providing actual insights and services for the users. Robust and efficient architectures are necessary for such data intensive systems, ensuring privacy and QoS. Proper analysis methods are essential to accommodate the variability in data availability and quality, privacy, and processing demands. Efficient solutions are required to communicate findings and link insights to actions for both decision-makers and citizens.

Edge computing distributes applications between the cloud, data producing devices, and devices at the edges of wireless and fixed networks. Edge computing brings AI methods closer to the physical environment. This enables deep integration of AI with applications supporting humans in their activities and with machines to realise autonomous systems. AI is integrated with the edge computing platform as well, for example, to decide optimal locations for computations.

We study situation-aware, self-organizing and self-managing software platforms that use efficiently edge computing infrastructures for 5G and beyond. We enable trust with distributed ledger technologies and privacy-by-design methods. We push edge computing to the limits by embedding computations and AI into physical objects that are digitally fabricated to fit to their purpose. We use distributed artificial intelligence, big data technologies, and machine learning methods to explore situation awareness for both end-user applications and the edge computing platforms. We develop architectures and analysis for data-intensive systems. For example, in one of our current research projects, most closely related to the Data4Healthcare programme, we want to understand how available data from the city can be connected to citizens’ health and quality of urban life.

We implement AI methods in ethical manner and emphasise testing in real environments. We expect our research to shed light on how humans can benefit from edge computing, and distributed solutions in general, and how it can be used to implement resource-efficient systems.

We invite highly skilled and motivated postdoctoral candidates with an interest in smart cities, citizens' wellbeing, and expertise in AI, data management, and distributed architectures.

• Head: Professor Juha Röning
• Faculty: Faculty of Information technology and electrical engineering
• Group composition: Seppo Vainio, Mikko Sams, Steven LaValle
• Website: https://www.oulu.fi/fi/yliopisto/tiedekunnat-ja-yksikot/infotech-oulu
• Contact: juha.roning (at) oulu.fi

Physiological control of human body functions involve cells secreted in and out of body, secretome, loaded with huge amounts of regulatory information. We aim to combine this relative new knowledge with classical measures of human functions, such as measuring EKG, body and facial motions, breathing and gaze patterns and EEG. Importantly, planned measurements will be performed in life-like conditions in virtual/mixed reality, rather than impoverished laboratory conditions. Such approaches offer novel strategies to be translated to the precision medicine, remote medicine and associated diagnostics.

We aim in this EU COFUND Data4Healthcare project to combine expertise on analysing secretome in University of Oulu, creating virtual realities in University of Oulu and Aalto University, and measurement of human behavior and experiences under naturalistic conditions in Aalto University. This makes it possible to offer novel knowledge of bodily functions underlying behavior for basic research, but also for applications in, e.g., education, diagnostics and therapeutic interventions. This also requires skillful use of powerful AI-based computational methods.

The project will take use of the MAGICS network that is an infrastructure of three universities, Aalto University, University of Tampere, and the University of the Arts Helsinki. MAGICS encompasses two principal interrelated objectives: 1) the provision of instrumentation for the 4D digitization and virtualization of objects, spaces, and actions to facilitate scientific and artistic research as well as their practical applications, and 2) the deployment of instrumentation for the measurement of human behavior and experiences under real-life or closely simulated conditions. The effective realization of MAGICS' multifaceted functionality hinges upon a meticulously coordinated collaboration among three universities, which also makes it versatile in its knowledge profile.

This suggested post-doctoral project is collaboration between the Oulu University and MAGICS – Aalto with the aim to couple, e.g., motion capture, physiological recordings and automatic recognition of facial expressions to those biomolecular elements that become represented in such embodiment process. Specifically, we aim to identify the bionanocosmos of selected human emotional experiences, and find how it correlates with more traditional measures of bodily basis of experiences. In practice we will correlate the noninvasive measurements of OMICS data that and the ones measured with MAGICS facilities, measurements will be done in Aalto University. Collected data will be analyzed with powerful computational methods, a strong expertise in Aalto university. We can collect millions of brains synaptic vesicle live cell secreted molecularly loaded nanobodies via clinical grade collectors. The mining of the molecular representations of the body while performing experimental tasks is expected to enlarge tremendously of the powers of the digital recording of human body actions. When such approaches will be subjected to medically relevant setting and coupled with top specific genetic and physiological contains, such BioMedICT analytics can be expected to provide novel therapeutic openings.

• Head: Simo Saarakkala, Professor / Head of Research Unit
• Faculty: Faculty of Medicine
• Group composition: The research team in this proposal includes the following persons: University of Oulu: 1 x Professor (Simo Saarakkala), 1 x Assistant Professor (Aleksei Tiulpin), 1 x Doctoral Researcher (Leon Robertz) Polar Electro: 1 x Senior Researcher (Lassi Rieppo), 1 x Principal Researcher (Topi Korhonen)
• Website: https://www.oulu.fi/en/research-groups/diagnostics-osteoarthritis-dios
• Contact: simo.saarakkala (at) oulu.fi

This research topic is carried out as a joint effort with our industrial partner, Polar Electro, which has a strong research heritage and is a world-wide known manufacturer of wearable devices for health and wellbeing technology and guidance. We are looking for candidates who are interested in wearable technology and are skilled in programming (Python) and machine learning.

Wearable technology has gained popularity among large populations for its ability to continuously track multiple health-related parameters, such as sleep, physical activity, stress, physical motion, and cardiac function. Wearable devices produce datasets that characterize human health in a longitudinal setting and can be used to monitor long-term changes in health.

Data modelling methods enable the development of a Digital Twin, a digital representation of a person, based on the wearable data. The Digital Twin can be used to understand the health status of the person and to make predictive simulations of how the person’s health would change in different interventions. Consequently, the Digital Twin modelling and associated predictions can be used to design interventions that would lead to desired outcomes in health. In this research proposal, the primary interest is to study and develop Digital Twin modelling technologies and methodologies based on wearable data in a health context.

The specific research topic can relate to, for example:

a) Developing Digital Twin methodologies that employ large datasets of wearable measurements and computational physiology,

b) Computational model-based design of interventions to improve health and well-being, or

c) Digital Twin models integrating wearable data to other data sources such as environmental data, clinical data, behavioral data, etc.

• Head: Dr Pekka Siirtola
• Faculty: Faculty of Information Technology and Electrical Engineering
• Group composition: 2 professors (Seppo Vainio & Juha Röning); Secondment contacts: Titti Kallio, Terhi Paananen, Kristiina Fekete
• Website: https://www.oulu.fi/fi/yliopisto/tiedekunnat-ja-yksikot/infotech-oulu
• Contact: pekka.siirtola (at) oulu.fi

Traditionally human relations were based on direct interactions between individuals and nature. Today social media offers alternatives for this and impacts our actions. Digital communication offers benefits such as non-locality and easy engaging with people. Online communities provide a space for individuals with shared interests, emotional support and personal well-being. Digitalization brings together individuals who might never have crossed otherwise. In case of forming partnership, digital tools offer a spectrum of potential partners. Digitalization shapes the traditional rituals, the cornerstone of our cultural identity. A supportive social network offers psychological comfort, promotes mental well-being and deeper meanings reinforcing of belonging. Irrespective of the depicted values of the social media feeling of loneness has increased. Moreover, mental diseases and psychiatric medication prescriptions have increases especially in the younger citizens.

We hypothesize that the digital communication lacks the critical transmitted true embodiment biosignalling that are transmitted when physically present. This in turn may contribute to sensory imbalance reflected at one end as sensation of lack of meaning among the younger generations.

In relevance to this problematic is the discovered mechanism that humans are more directly connected to others and nature as considered. This notion is based on the findings that all living organisms use the same mechanism as their overall systemic communication mechanism. Such communication is composed of the cell secreted molecularly loaded extracellular nanosized lipid membrane encircled vesicles (EVs). EVs are similar to those of the synaptic vesicles in the brain. In addition, most of the brain cells communicate with EVs. Importantly EVs can cross the biological barriers and may be well presented in excreted body fluids such as sweat. Transmitted individual EV molecular constituents can be analyzed to date via clinical grade skin plasters and offers effective bio- and data banking from cohorts.

In this project we will collaborate the Evangelical Lutheran Church (ELC) of Finland Evangelical Lutheran, the largest church in Finland with 3,6 million members. The Christian dating App VALO will participate in the research by activating users to provide their expertise by experience on searching for a biologically and mentally compatible partner. The App will offer eventually the knowledge that will be derived from the nano level bioparticles to be integrated into the App to better the performance of its social algorithm. VALO has around 250 000 registered users. We aim to couple digitally collected data sets from the connected community to those of the molecular genetic ones. This may provide value and motivation that may reinforce social gathering and mental wellbeing promotion via such a novel citizen science program. It is speculated that EV exchanges during social interactions provide a novel physical sociobiological control mechanism and reasoning of our social and self-assembly. Realization of the hypothesis is based on analysis of questionnaires, wearable data and social narratives from an app, EV OMICS and spectral data derived from targeted cohorts. The results are expected to provide novelties towards science depicted value of social bonding and mental wellbeing. Putative Secondments: Valo and ELC collaborators.

• Head: Jaakko Suutala, Asst. Prof
• Factulty: Faculty of Information Technology and Electrical Engineering (ITEE)
• Group composition: 1 Professor, 1 Adjunct professor, 1 senior researcher, 2 postdocs, 3 doctoral researchers, 4 research assistant
• Website: https://www.oulu.fi/en/research-groups/data-analysis-and-inference-group
• Contact: jaakko.suutala (at) oulu.fi

As a part of Biomimetics and Intelligent Systems research unit at ITEE, Data Analysis and Inference Group (DataAI) conducts high-quality research in the field of computational data analysis and develops methods for diverse real-world modeling problems. We are experts in applied AI and ML, especially in the areas of health and wellbeing and sustainability in industry and environment. Our long-term goal is to enable Human-AI collaboration in next generation decision support to make your work and everyday life easier.

In this project, we are developing novel machine learning approaches that are able to perform robust predictive uncertainty quantification and explaining the model's decisions. Furthermore, data-efficient ML models that are capable of learning from sparse data in small data regimes and adapt to distributional shifts as a part of big multimodal health datasets, are considered with the recent advances of deep learning, non-parametric Bayesian methods, and ensemble learning. Developed methodologies are applied and their performance are evaluated on large-scale medical records and wearable sensor data with the goal of building personalized models to help clinical decision making and health monitoring systems.

• Head: Assistant Professor Aleksei Tiulpin, PhD
• Faculty: Faculty of Medicine
• Group composition: 7 professors, 1 senior researcher, 4 postdocs, 6 PhD students
• Website: https://aleksei.tiulpin.ai
• Contact: aleksei.tiulpin (at) oulu.fi

The consortium led by the Intelligent Medical Systems research group of the University of Oulu offers projects, which are done in collaboration with Oulu University Hospital. We are looking candidates interested in different subfields of AI, namely medical imaging and computer vision, natural language processing, and machine learning in general.

The consortium is co-led by clinical collaborators Prof. Tero Rautio, MD PhD, Prof. Juha Saarnio, MD PhD, Prof. Nina Hautala, MD PhD, Prof. Mika Martikainen, MD PhD, Adj. Prof Niina Salokorpi, MD PhD, and Adj. Prof Mika Nevalainen, MD PhD. The team is strongly reinforced by several senior researchers and existing PhD students.

Our group is focused on the development of new methods, and we are looking for candidates with excellent skills in mathematics, medical image analysis, and programming. Please apply if you are looking to work on: Reinforcement Learning, Bayesian Optimization, Uncertainty, Computer Vision, and Foundation Models. The offered projects are mainly under the themes (1) Data as a new resource in the society and (3) Technical tools in data management and processing.

-Sub-theme: Machine Learning:

(1) Differentially-private disease trajectory modelling

(2) Hospital-scale foundation models for radiology and ophthalmology

(3) Medical foundation models for time-varying multi-modal data

-Sub-theme: Medical imaging:

(1) AI-driven care pathways in glioma

(2) Pre-operative prognostic biomarkers of colorectal cancer

(3) Personalized diabetic retinopathy screening

(4) Population-scale body composition analysis with Deep Learning.

-Sub-theme: Natural language processing:

(1) Care need assessment in neurology

(2) Pseudonymization of medical text

(3) Medical data structuring

-Sub-theme: Surgical data science:

(1) Automatic analytics of surgical processes, and AI-driven procedure scheduling

(2) Surgical room monitoring with computer-vision

(3) Virtual reality for surgical training

In your application, specify which theme and which project within it you would like to work on. We also have a very extensive collaboration network beyond the partner organizations in the program. Contact us in advance to discuss your interests and the research plan.      

• Head: Guoying Zhao, Academy Professor
• Faculty: Faculty of Information Technology and Electrical Engineering
• Group composition: 2 professors, 7 postdocs, 23 researchers
• Website: https://gyzhao-nm.github.io/Guoying/
• Contact: guoying.zhao (at) oulu.fi

The group led by Academy Professor Guoying Zhao is affiliated with the Center for Machine Vision and Signal Analysis (CMVS), Faculty of Information Technology and Electrical Engineering. It is a creative, open, and internationally attractive research center, combining the expertise of University of Oulu computer vision, machine learning, biosignal analysis and biomedical image analysis scientists to explore novel computer vision methods and harness the power of big data to improve the healthcare services.

The group is committed to revolutionazing healthcare through mining the knowledge embedding in the big data produced by both the sensors in our daily life such as normal cameras, and the medical imaging equipments such as fundus cameras. Our mission is to improving healthcare for every patients particularly those who have difficulty to correctly express their feelings and enabling remote healthcare.

The research “Computer vision-based and big data-driven healthcare and wellbeing” aims to develop novel learning methods, particularly multi-modal learning, domain adaption, domain generalisation, deep generative models etc. to analyze the large-scale multi-modal health data, and apply them to the tasks including but not limited to:

1) Multi-modal pain analysis: aiming to collect and integrate large-scale and diverse multi-modal datasets, including the videos/audios captured by daily life camera and wearable devices information, and explore multi-modal learning methods to estimate the pain grade;

2) Non-contact remote heart rate for emotional wellbeing and heart diseases: aiming to develop novel learning methods to predict the heart rate with facial videos and images, assess the emotional wellbeing and detection heart diseases;

3) Multi-center multi-modal ophthalmical data analysis for multiple eye disease detection: aiming to develop novel multi-modal learning methods to learn with multi-modal ophthalmical data captured from multiple medical centers for disease detection.

4) Doctor and patients interaction: aiming to develop novel paradigm of doctor-patient interaction such as learning-based interactive behavior analysis, visualization methods to better visualize the big health data, and make the medical expertise be easily and well understood by patients, further facilitate the interaction of doctor and patients.

5) Cancer image analysis: novel deep learning techniques are developed for cancer image analysis for computer-aided diagnosis in digital pathology.

The co-supervisors are Professor Tapio Seppänen from Biomedical Engineering group and Academy Fellow Qing Liu.