If you an MSc, BSc, PhD or postdoctoral student who would like to collaborate with our lab on your research project, you are on the right page! Depending on the depth vs duration of your project, the project focus will be adjusted. Feel free to check the proposed research topics below and contact us along with details at the end of this page.
Improve health for everyone, one thesis at a time!
A ticket to a journey of high-quality research and development, with a successful bachelor or master thesis in computer science or a related field on the way. Collaboration between Copenhagen (Denmark), Geneva (Switzerland) and Stanford (USA) teams, as well as collaboration with industry partners and international organizations, as well as other stakeholders in well-being, health and quality of life domain, are implied. An international visit abroad – to spend some time with the project collaborators is always an option. The potential projects are as follows.
The top killers worldwide are chronic diseases, such as cardiovascular disease, lung disease, and diabetes. Existing risk assessment models can calculate the risk of such diseases, yet some of the risk factors (age, gender, smoking status, presence of diabetes, body-mass index, blood pressure, glucose, and cholesterol) are hard to obtain.
Scope: Design, development, and deployment of a mobile platform that enables the data collection for these risk factors in a large population (n > 10.000), or analysis of the existing datasets we already have collected for tens of participants (age, gender, smoking status, presence of diabetes, body-mass index, glucose, additionally to: sleep, physical activity, heart rate).
Skills: Strong programming skills, knowledge of statistics (specifically for the second choice within the thesis scope), exposure to mobile platform development (preferably iOS), and an inclination towards applying these in the health sciences.
Team: The coordinators are Katarzyna Wac and Vlad Manea, leveraging their collaboration with the R&D consortium of the H2020 WellCo project and Mads Schnoor Hansen. Teams of students welcome.
More than 40% of the risk of chronic diseases is due to unhealthy behaviors, such as physical inactivity, bad nutrition, poor sleep, or smoking status. However, there is no precise cause-effect formula that translates all these behavioral “markers” to the risk factors used in the risk assessment models from the literature.
Scope: Research, development, and deployment of an interactive scenario generator based on the latest results in chronic disease risk assessment, longitudinal behavioral and health data from a large sample of the Danish population, and behavioral and health data from a participant.
Skills: For this thesis, it is important that the candidate has strong programming skills, knowledge of descriptive and inferential statistics, exposure to data visualization, and an inclination towards applying these in the health sciences.
Team: The coordinators for this thesis are Katarzyna Wac and Vlad Manea, leveraging the collaboration with the R&D consortium of the H2020 WellCo project (from Denmark, Spain, Italy, Slovenia, and the Netherlands). Teams of students welcome.
For many behaviors (e.g., physical activity, sleep, nutrition) and personal states (e.g., conditions such as depression and anxiety, level of social support), over time, researchers have calibrated and validated simple questionnaires “in the office”. However, few studies have tested the validity of such questionnaires when correlated with data collected from daily life, “in the wild”.
Scope: Research, design, development, deployment, analysis, and discussion of a comparative study between the outcomes obtained through the administration of a validated scale and those obtained through the collection of the analogous wearable data.
Skills: Programming skills, knowledge of statistics, exposure to participatory design, and an inclination towards applying these in the health sciences.
Team: The coordinators for this thesis is Katarzyna Wac and Vlad Manea, leveraging the collaboration with validated scale experts at the University of Copenhagen. Teams of students welcome.
Our living lab aims at leveraging smartphones and wearables towards collecting data from daily life, in time and context, over long periods of time. Such data does not only refer to the “usual” data an activity tracker may collect from the participant: number of steps (physical activity), hours slept (sleep), or heart rate (health state). Aspects of daily life can also be inferred from the proximity to the phone and the actual use of the phone.
Scope: Development, deployment, and discussion of deep device instrumentation feature for our incoming iOS mobile platform. The lab already uses a similar feature in our Android mobile platform, which can serve as a starting point.
Skills: Very strong programming skills, particularly in the Apple iOS universe, and interest in exploring iOS frameworks used in a mobile health context, such as HealthKit, ResearchKit, and ResearchKit EHR as well as external libraries and frameworks.
Team: The coordinator for this thesis is Katarzyna Wac, leveraging the collaboration within the Quality of Life technologies lab, in particular Alexandre DeMasi (University of Geneva, Android) and Vlad Manea (University of Copenhagen, iOS). Teams of students welcome.
Are you stressed? Besides being a relevant question to ask nowadays, this is a recent study in our lab. The study explores the relationships between the state of the art stress assessment by using validated scale-based questionnaires that are self-administered by the participant, and the stress assessment by using exploratory questionnaires that are administered to the participant’s peers (friends, family, co-workers) in situ.
Scope: Design and development of a mobile platform for peer stress assessment on the iOS platform, consisting of two applications: one for the individual and one for the peers.
Skills: Good programming skills, particularly in the Apple iOS universe, and an interest in exploring iOS frameworks used in a mobile health context, such as HealthKit and ResearchKit.
Team: The coordinator for this thesis is Katarzyna Wac, leveraging the collaboration within the Quality of Life technologies lab, in particular Allan Berrocal (stress study researcher, University of Geneva, Android) and Vlad Manea (University of Copenhagen, iOS). Currently the study is replicated at Stanford (USA). Teams of students welcome.
Migraine is a debilitating disease, and migraine headaches decrease the quality of life of individuals suffering from it, as well as those around them. Is there an influence of behaviours on the migraine headache attack? Can we predict the next attack given enough data about the past behaviours of the individual? Can we model and predict the behaviours, well-being and quality of life of migraine sufferers longitudinally? We identify a set of new research questions in our lab, which we hope to answer in collaboration with MigraineBuddy team – developing and continuously improving their award-winning app-based migraine headache diary and tracking platform. The app helps users record and identify triggers of migraines, symptoms, medication, migraine frequency and duration, pain intensity and location, and other lifestyle factors to help users improve their migraine condition.
Scope: The first stage implies an analysis of already collected datasets. The second stage implies an analysis of data for selected cohorts of individuals – being already MigraineBuddy users in Denmark/Switzerland/USA.
Skills: Good statistical background and programming skills for large datasets analysis (Python, R, Matlab).
Team: The coordinator for this thesis is Katarzyna Wac, leveraging the collaboration within the MigraineBuddy team (Singapore and France). Teams of students welcome.
How does an average individual perceive own behaviours, health, well-being and quality of life? What matters to him or her? What they define as ‘good’ behaviours and ‘bad’ behaviours and why? Do they use technologies like wearables and apps for self-management of health? Given an increased human lifespan, along with an increased burden of chronic illness, we ask these questions to ourselves and populations at large. We identify a set of new research questions in our lab, which we hope to answer in collaboration with scientists from Stanford Prevention Center (USA), specifically focusing on data collection via WELLforLife platform.
Scope: QoL-lab specific expansion of the existing self-report-only based WELLforLife platform; especially incorporating the wearables and smartphone mhealth apps-enabled data collection for individuals. The first stage implies an analysis of already collected datasets. The second stage implies an analysis of datasets collected from selected cohorts of individuals in Denmark/Switzerland/USA.
Skills: Good statistical background and programming skills for large datasets analysis (Python, R, Matlab).
Team: The coordinator for this thesis is Katarzyna Wac, leveraging the collaboration within the Stanford Prevention Center (USA). Teams of students welcome.
What social structures in which we live and operate daily guard or enable our momentary ‘good’ behaviours and ‘bad’ behaviours? Who are the changemakers enabling better individual behaviours and hence a better world? Who do the individuals listen to, when not in touch with the healthcare system, because they are not really sick? Given the burden of chronic illness, ageing, sustainability, the world now requires everyone to be a changemaker. Along with that statement, we identify a set of new research questions in our lab, which we hope to answer in collaborators and from Ashoka and LinkedIN Elevate teams (both in USA). The larger context of this project relates to the IEEE Standardization activity on Social Impact Measurement (SIM), to which QoL lab contributes since August 2019.
Scope: QoL-lab specific expansion of the existing Ashoka/LinkedIN approach for human networks analysis and “changemaker score” metrics; especially incorporating the QoL-lab specific mobile app (Android/iOS) for self-assessment of behaviours and social context. The first stage implies an analysis of already collected datasets by LinkedIN/Ashoka. The second stage implies an analysis of datasets collected from selected cohorts of individuals in Denmark/Switzerland/USA.
Skills: Good statistical background and programming skills for large datasets analysis (Python, R, Matlab).
Team: The coordinator for this thesis is Katarzyna Wac, leveraging the collaboration within the Ashoka and LinkedIN Elevate teams (USA). Teams of students welcome.
From the Organization for Economic Cooperation and Development (OECD) report on Social Impact Investment January 2019 “Social impact investment is the provision of finance to organizations addressing social needs with the explicit expectation of a measurable social, as well as financial, return. Social impact investment seeks to leverage innovation and apply measurement rigor to achieve social outcomes.” The challenge is that the ‘measurable social return’ is hardly measurable, as the reliable and reproducible set of impact measures, formulae, logic models, and processes for assessing and calculating social impact hardly exist today. For example, there are 150 social impact platforms uncovered by the Bertelsmann Foundation 2018 report, each having a different measurement framework. Then there are set of other metrics proposed by the Open SDG Data Hub aiming to facilitate reaching of the The UN Sustainable Development Goals (SDGs) by 2030. Our lab has a high ambition to achieve a substantial social impact in the future, and we are interested in contributing to existing reserach and development in the domain of social impact measurement. Along with that statement, we identify a set of new research questions in our lab, which we hope to answer in collaborators and from Ashoka (USA). The larger context of this project relates to the IEEE Standardization activity on Social Impact Measurement (SIM), to which QoL lab contributes since August 2019.
Scope: A reserach on existing set of impact measures, formulae, logic models, and processes for assessing and calculating social impact, starting from the Bertelsmann Foundation 2018 report and the Open SDG Data Hub and continuing with analysis of other datasets documenting past and existing projects worldwide. The datasets are provided by our collaborating teams.
Skills: Good qualitative reserach skills especially applied for large datasets coding/analysis.
Team: The coordinator for this thesis is Katarzyna Wac, leveraging the collaboration within the Ashoka and IEEE Standardization activity on Social Impact Measurement (SIM) (USA). Teams of students welcome.
Our lab is implementing a chatbot to make participation in scientific studies fun! The chatbot is in minimum viable product stage: it can sign up people for studies, follow-up with research questions and connect with the participants’ wearable for health data sharing, all using the Facebook Messenger platform. It contains a few explorations: Explore Your Physical Activity, Explore Your Sleep, Explore Your Stress, and Explore Your Quality of Life.
Scope: participatory design sessions of the chatbot with users (researchers and participants) and concrete user interface design; exposure to user data-driven (analytics) research; psychological aspects of chatbot engagement and human study participation.
Skills: excellent design skills, particularly in user experience (UX) and user interface (UI) design, and an interest in taking a research-driven approach to co-designing / participatory-designing (PD) a chatbot.
Results: Preliminary research work on the chatbot has been accepted at the HealthMedia 2019 workshop in conjunction with the ACM MultiMedia 2019 conference (conference rank: excellent) to be presented in October 2019. The research and development of the chatbot is the subject of a successful master thesis to be defended in September 2019.
Team: The coordinators for this thesis are Katarzyna Wac and Vlad Manea. Collaborators: Ece Elbeyi (Department of Arts and Culture) and Mads Schnoor Hansen (Department of Computer Science). Teams of students welcome. Priority will be given to design students.
* Instructions (totally inspired by Prof. S. Lehman, DTU): Please answer all questions below. Use as much or little detail as you find appropriate. The file should be returned in plain text format and via email to our lab leader Katarzyna Wac. We reserve the right to be unimpressed if you’re unable follow these simple instructions. We encourage theses in teams, so if you have a friend or two who are interested, you can write a thesis together and join forces to improve health for everyone!
* Questions:
Q1: What is your study line (if this question apply to you)?
Q2: List your programming experience (e.g., standard machine learning libraries (e.g., sk-learn, pandas, PyTorch, and TensorFlow/Keras) and/or data visualisation software (e.g., Plotly, Seaborn, Matplotlib))
Q3: List your math courses & skills
Q4: List other skills that you consider relevant
Q5: What is the best class you have taken at DIKU/CUI/Your University* and why?
Q6: What is the worst class you have taken at DIKU/CUI/Your University* and why?
Q7: What project from the above list interests you the most and why?
Q8: What grade do you aim to receive for your project?
*depending where you are based: UCPH/DIKU or UNIGE/CUI or Other University.
Email: wac@di.ku.dk or katarzyna.wac@unige.ch
Department of Computer Science, Faculty of Science, University of Copenhagen, Denmark
Department of Media, Cognition and Communication, University of Copenhagen, Denmark
Department of Media, Cognition and Communication, University of Copenhagen, Denmark
Department of Computer Science, Faculty of Science, University of Copenhagen, Denmark
Department of Computer Science, Faculty of Science, University of Copenhagen, Denmark
Department of Media, Cognition and Communication, University of Copenhagen, Denmark
Department of Computer Science, Faculty of Science, University of Copenhagen, Denmark
Department of Computer Science, Faculty of Science, University of Copenhagen, Denmark
Department of Computer Science, Faculty of Science, University of Copenhagen, Denmark
Department of Media, Cognition and Communication, University of Copenhagen, Denmark
Department of Computer Science, Faculty of Science, University of Copenhagen, Denmark
Department of Health Informatics, University of Copenhagen, Denmark
Department of Media, Cognition and Communication, University of Copenhagen, Denmark
Department of Bioinformatics, Faculty of Science, University of Copenhagen, Denmark
Department of Computer Science, Faculty of Science, University of Copenhagen, Denmark
Department of Computer Science, Faculty of Science, University of Copenhagen, Denmark | The Mobile App: Student Version Teacher Version
Faculty of Humanities and Faculty of Science, University of Copenhagen, Denmark
Department of Computer Science, Faculty of Science, University of Copenhagen, Denmark
University of Geneva, Switzerland
University of Geneva, Switzerland
University of Twente, the Netherlands
University of Geneva, Switzerland
University of Geneva, Switzerland
University of Geneva, Switzerland
Vrije University, the Netherlands (stay at the Blekinge Institute of Technology, Sweden)
Vrije University, the Netherlands (stay at the Blekinge Institute of Technology, Sweden)
University of Geneva, Switzerland (stay at the University of Twente)
University of Geneva, Switzerland
University of Geneva, Switzerland