On May 11, 2023, our colleague Gideon Mekonnen Jonathan, PhD student in IT Management and Governance group at DSV/Stockholm University has defended successfully his PhD thesis entitled: “Information Technology Alignment: Towards Successful Digital Transformation”. His PhD thesis can be downloaded from the following link in DiVA: https://www.diva-portal.org/smash/get/diva2:1743531/FULLTEXT06.pdf. On behalf of research group in IT Management and Governance, I would like to congratulate Gideon Mekonnen Jonathan for this great achievement.
Rebecka Weegar’s defence of her PhD-thesis “Mining Clinical Text in Cancer Care”
Rebecka Weegar defended today January 27, 2020, successfully her PhD thesis , with the title Mining Clinical Text in Cancer Care. Opponent were Associate professor Lilja Øvrelid at Department of Informatics University of Oslo.
Nailing of Rebecka Weegar’s thesis
Rebecka Weegar nailed today her PhD-thesis with the title Mining clinical text in cancer care.
Jury member of the PhD defence of Tim Huygh at University of Antwerp, Belgium
On December 5, 2019 I have been a jury member in the PhD defence of Tim Huygh at University of Antwerp, Belgium. His PhD thesis is entitled “Understanding IT Governance: Conceptualization and Enabling IT Business Value”. The abstract of this PhD thesis is included below together with a picture after this PhD defence with Tim Huygh, Steven De Haes (as Tim Huygh’s PhD supervisor) and the jury members.
Understanding IT Governance: Conceptualization and Enabling IT Business Value
Date: 5 December 2019
Venue: Master Aula André Leysen 001.B – Antwerp Management School, Boogkeers 5 – 2000 Antwerp
Time: 4:00 PM
PhD candidate: Tim Huygh
Principal investigator: Prof Steven De Haes
Short description: PhD defence Tim Huygh – Faculty of Business and Economics
Abstract
An increasing (operational and strategic) dependence on IT for organizations results in a growing need for effective IT governance. Effective IT governance effectuates appropriate control over an organization’s current and future IT use, as to enable the creation and protection of IT business value. However, the research domain of IT governance is somewhat hampered by a lack of theoretical underpinnings for IT governance, which makes it difficult to explain from a theoretical perspective how effective IT governance should be organized and why.
In response, this PhD thesis investigates the concept of IT governance through Beer’s Viable System Model (VSM). In that context, the research that was conducted serves multiple purposes. First, an organizing logic for IT governance is articulated that is grounded in the VSM and informed by extant IT governance research. Second, extending the discussion beyond the conceptual level, two in-depth case studies are presented that demonstrate how the VSM can be used as a lens for describing and diagnosing IT governance in practice. Third and finally, four empirical IT governance-related research studies are presented that are inspired by the VSM-based organizing logic for IT governance, which demonstrates that this logic can provide structure and theoretical underpinnings to IT governance-related empirical research.
For academics, this thesis contributes to the theoretical discourse on IT governance. It answers the question why IT governance can continue to fulfil its general purpose of creating and protecting IT business value. Furthermore, it provides strong theoretical underpinnings for how to organize effective IT governance. For practitioners, this thesis shows how the VSM can be used as a lens to describe and diagnose IT governance in practice. Furthermore, it provides insights on the IT governance mechanisms and practices that may be used to instantiate the required functions of an IT governance arrangement.
Link: https://www.uantwerpen.be/en/research-groups/management-information-systems/
Andrea Andrenuccis Dissertation successfully defended on Oct 29, 2018
Andrea Andrenucci defended successfully his dissertation with the title Using Language Technology to Mediate Medical Information on Health Portals – User Studies and Experiments on Monday Oct 29 2018. Opponent was Dr. Diego Molla-Aliod, from Macquarie University, Sydney Australia.
Parisa Aasi’s PhD defense
On June 4, 2018 our colleague Parisa Aasi, PhD student in IT Management and Governance group at DSV/Stockholm University has defended successfully her doctoral thesis entitled: “Information Technology Governance: The Role of Organizational Culture and Structure”. Her PhD thesis is available to be downloaded at the following link: http://su.diva-portal.org/smash/get/diva2:1195170/FULLTEXT01.pdf
On behalf of research group in IT Management and Governance I would like to congratulate Parisa Aasi for this great achievement. Some pictures from this event are included below.
Parisa Aasi has nailed her PhD thesis at DSV
On May 14, 2018, Parisa Aasi has nailed her PhD thesis at DSV. Parisa Aasi is a PhD student in IT Management and Governance group at DSV and her PhD defence will take place on June 4, 2018 at 13.00 in L50 at DSV. A copy of her PhD thesis entitled “Information Technology Governance: The Role of Organizational Culture and Structure” is available to be downloaded at the following link http://su.diva-portal.org/smash/get/diva2:1195170/FULLTEXT01.pdf
Job: PhD student in Computer and Systems Sciences with focus on Deep Learning for Natural Language Processing of Healthcare Data
Deep learning based on various neural network architectures has seen tremendous success in recent years, substantially outperforming alternative learning approaches in fields such as natural language processing. By learning abstract representations through multiple processing layers, the learning task can be simplified while removing the need for carefully handcrafted features. Deep learning hence provides an effective paradigm for obtaining end-to-end learning models from complex data, such as the vast amounts of longitudinal and heterogeneous data that are stored in electronic health records. Learning general-purpose representations of patients can be useful for modeling patient trajectories and disease progression, supporting early prediction and detection of adverse events, such as healthcare-associated infections or adverse drug effects. There are numerous open research questions w.r.t. deep learning from healthcare data, including (i) effectively learning from small amounts of (labeled) data through, e.g., unsupervised pre-training, (ii) modeling the temporality of clinical events, and (iii) creating interpretable models that can be understood by clinical decision makers.
The PhD project involves designing novel deep learning architectures that address these challenges in order to make better use of heterogeneous healthcare data, in particular free-text clinical notes, for ultimately supporting healthcare by improving patient safety and reducing healthcare costs. In HEALTH BANK, we have access to eight years of specialized healthcare data from Karolinska University Hospital and are currently in the process of also obtaining primary care data, thereby allowing patients to be followed throughout the healthcare system.