Available thesis proposals:
- Medical image processing
- Supply Chain Management (SCM) Optimization and Resilience to Disasters and Disruptions
- Misinformation and disinformation through the lens of data analytics
- Multilayer networks to better understand Multiple Sclerosis
- Predict disability in multiple sclerosis using synthetic data and federated learning
- HealthGuardians: Empowering with AI and Citizen Science
- AI and psychology: towards a fruitful synergy
| Thesis proposals | Researchers | Research Group |
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Medical image processing is a key step in the diagnosis of a large number of diseases. Nowadays, we can acquire images of the inside and outside of our bodies using a large variety of devices (ultrasound, magnetic resonance, optic tomography, computed tomography, etc.). Afterward, the acquired images usually need to be denoised, corrected for inhomogeneities, segmented, registered, etc. in order to be able to get relevant information to aid the clinical decision using image-based biomarkers.
On this research line, we would like to explore the latest image processing challenges and develop new image-based biomarkers that aid clinicians in their daily work. This work will be done in collaboration with world-wide recognised clinical institutions in Barcelona.
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Mail: fpradosc@uoc.edu |
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Supply Chain Management (SCM) Optimization and Resilience to Disasters and Disruptions Disasters have a significant and increasing impact all over the world. There is a growing concern about them, so Disaster Risk Reduction (DRR) is increasingly in international agenda. This thesis proposal sets up the scientific and technical basis for a significantly improved resilience to hazards (such as climate related hazards, earthquakes, pandemics, etc.) and their human and socioeconomic impacts related to Suppply Chain Management (SCM).
The proposal is based on three principles, inspired by UN Sendai Framework and related to UN 2030 Agenda for Sustainable Development:
1) Focus on prevention and resilience building oriented. 2) Inclusive “whole-of-society” approach, to involve non-traditional stakeholders not usually involved in DRR planning and decision making (such as households, SMEs, NGOs, etc.). 3) Data-driven approach, to integrate in DRR planning and decision-making diverse types of data (including small data, thick data, and big data) from a wide range of sources, and including reuse of data.
This thesis proposal will conduct research about data-based instruments for SCM's optimization and resilience related to disasters and disruptions.
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Mail: jcobarsi@uoc.edu |
ICSO |
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This thesis proposal focuses on cases studies about misinformation or disinformation, through the application of quantitative data analytics methods to amounts of digital content such as: social media, mainstream media news and reports, Wikipedia entries, literature about historical events, open data and/or other open or public domain sources. This digital content may be created, updated, influenced and/or used by a wide range of actors: citizens, anonymous agents or activists, governments and public agencies, companies, international organizations, political parties, social organizations, etc.
Research methodologies for these case studies will usually include the advanced conceptualization of misinformation and disinformation events, so to enhance the intensive application of quantitative methods to trace and analyse them through amounts of digital content and logs. These quantitative methods may be combined when suitable with qualitative methods.
Cardoso, G.; Sepúlveda, R.; Narciso, I. (2022). Whatsapp and audio misinformation during Covid-19 pandemic. El Profesional de la Información https://doi.org/10.3145/epi.2022.may.21
Cobarsí-Morales, J. (2022). Controversial ‘Black Kegend’ concept as misinformation or disinformation related to history: where do we go from here in the 21st century information field?. In: Smits, M. Proceedings iConference 2022 – Information for a Better World: Shaping the Global Future.
Salaverría, R., & León, B. (2022). Misinformation beyond the media: ‘fake news’ in the big data ecosystem. In: Vázquez-Herrero J., Silva-Rodríguez A., Negreira-Rey M.C., Toural-Bran C., López-García X. (2022). Total Journalism. Models, Techniques and Challenges (pp. 109-121. Studies in Big Data, 97. Springer Nature. Cham: Springer. DOI:10.1007/978-3-030-88028-6_9
Meel, P.; Vishwakarma, D.K. (2019). Fake news, rumor, information pollution in social media and web: A contemporary survey of state of the arts, challenges and opportunities. Expert Systems With Applications https://doi.org/10.1016/j.eswa.2019.112986
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Mail: jcobarsi@uoc.edu |
ICSO |
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Neuroaxonal anatomy and function are affected by multiple sclerosis (MS) disease which, in turn, impacts the brain structure, organization, and function. In general, networks representing particular brain aspects (morphology, structure, or dynamics) are studied independently to understand and predict individual brain damage effects [1]. Designing a single unified model to jointly study these multiple aspects is necessary to understand neurological diseases.
Within this PhD we would like to introduce an interconnected multi-layer framework for the joint analysis of morphological, structural, and functional networks. Therefore, we aim to define a multi-layer scheme that allows us to combine the information of morphological, structural, and functional networks into a single scheme in order to better assess brain damage effects and evolution of MS patients. Then, it is very relevant to define or adapt graph-mining metrics to evaluate and quantify the deterioration of the connectivity of the brain using the new multi-layer scheme.
This work will be done in close collaboration with the Multiple Sclerosis group led by Dr. Sara Llufriu at the IDIBAPS-Hospital Clinic, a world-wide recognized clinical institution.
Ref: Jordi Casas-Roma, Eloy Martinez-Heras, Albert Solé-Ribalta, Elisabeth Solana, Elisabet Lopez-Soley, Francesc Vivó, Marcos Diaz-Hurtado, Salut Alba-Arbalat, Maria Sepulveda, Yolanda Blanco, Albert Saiz, Javier Borge-Holthoefer, Sara Llufriu, Ferran Prados; Applying multilayer analysis to morphological, structural, and functional brain networks to identify relevant dysfunction patterns. Network Neuroscience 2022; 6 (3): 916–933. doi: https://doi.org/10.1162/netn_a_00258
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Mail: fpradosc@uoc.edu
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NeuroADaS Lab |
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Predict disability in multiple sclerosis using synthetic data and federated learning
An integrative approach to predict disability in multiple sclerosis through image analysis, synthetic data generation and the provision of a federated learning platform. This PhD is going to be in collaboration with the research group ImaginEM at Hospital Clinic BCN.
The primary clinical objective of this PhD project is to develop robust predictive models for disability progression in individuals diagnosed with multiple sclerosis [1]. This clinical objective involves leveraging advanced imaging techniques, clinical data, and machine learning algorithms to identify early indicators of disability. The successful candidate will collect and analyse multimodal imaging data, including but not limited to MRI, CT scans, and patient clinical records. The aim is to enhance our understanding of disease progression patterns, ultimately contributing to developing personalised treatment plans.
In parallel with the clinical objectives, the PhD candidate will be responsible for developing an open federated learning system platform tailored to analyse medical images [2,3]. This platform will facilitate collaboration and data sharing across healthcare institutions while ensuring data privacy and security. The candidate will design and implement federated learning algorithms, enabling the aggregation of insights from diverse datasets without centralised data storage. Developing a synthetic data generation module [4] will also be crucial, providing a more extensive and diverse dataset for training machine learning models.
[1] https://jnnp.bmj.com/content/early/2023/07/19/jnnp-2022-330203.abstract
[2] https://www.worldscientific.com/doi/abs/10.1142/S0129065722500496
[3] https://link.springer.com/chapter/10.1007/978-3-031-22356-3_12
[4] https://www.nature.com/articles/s41598-023-40364-6
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Mail: fpradosc@uoc.edu
Mail: lsubirats@uoc.edu
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NeuroADaS Lab |
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HealthGuardians: Empowering with AI and Citizen Science
This PhD project seeks to revolutionize medical imaging by combining the power of citizen science with artificial intelligence (AI). By involving citizens in the annotation of medical datasets, this approach breaks away from traditional limitations, speeding up the annotation process and enriching the data with diverse perspectives. This project not only drives innovation in AI but also empowers individuals to contribute to healthcare advancements, creating a valuable dataset that supports more adaptable AI models. Beyond the technical outcomes, it fosters a more informed and engaged public in the realm of healthcare innovation. In essence, this PhD represents a transformative fusion of citizen science and AI, addressing pressing societal challenges and driving collective progress in healthcare.
The project is structured into three main phases. First, it will engage citizens from diverse backgrounds to identify patterns that may be overlooked by clinical experts alone. Through this collaboration, a large, annotated dataset will be created, addressing the common challenge of limited labeled medical images and enabling more robust AI development. The second phase focuses on algorithm generalization, where insights from citizen annotations help develop more inclusive and generalized AI models that work effectively across varied demographics and medical conditions. Finally, the project will promote public participation in healthcare innovation, raising awareness about AI in healthcare and ensuring ethical standards are met throughout.
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NeuroADaS Lab |
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AI and psychology: towards a fruitful synergy
This project focuses on studying possible links between psychology and artificial intelligence (AI), with a particular focus on large language models (LLMs). We will explore ways in which these research areas can connect and benefit from each other.
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Mail: jamidei@uoc.edu Mail: rnietol@uoc.edu |