Post-docs &
PhD Students
I supervise an enthusiastic team of post-docs and PhD students.
Are you fascinated by their topics? Do you dream of pursuing a PhD in Computer Science yourself? Don't hesitate to contact me!
Stijn obtained his PhD in 2011 on the topic of distributed reasoning for context-aware services. Since then he has been a post-doctoral researcher, performing research on semantic technologies applied in a variety of domains, such as eHealth, wireless senor networks and transportation as well as being involved in the follow-up of bachelor's and master's courses at the Faculty of Engineering and Architecture of Ghent University.
If you can't reach Stijn on his mobile, you might want to look for him in a basketball gym somewhere in Europe, satisfying his second love as FIBA table official, or even in his second (wannabe) home country - the UK.
Bram his research tries to improve anomaly detection, and root cause analysis for the eHealth and predictive maintenance domain, using (semantic) knowledge models in combination with both black and white-box machine learning techniques. He defended his PhD in 2023 on "knowledge graph creation and embedding to realize explainable hybrid AI applications", which can be found here. As a post-doc he continues this research, mainly applied to chronic disease management and predictive maintenance.
Besides his research, Bram likes to talk about sports as well. More specific his hobby: Volleyball and oh, he's a big Coldplay Fan.
Co-supervised by Sofie Van Hoecke.
Mathias performs research in the areas of stream reasoning & cascading reasoning. More specifically, he tries to make the semantic reasoning on data streams in complex IoT contexts more efficient by exploiting resources in the edge of IoT networks, and by adaptively deriving the necessary stream processing queries in a context-aware fashion. His research is mostly applied in the context of health care use cases, such as ambient-intelligent smart home monitoring. He defended his PhD in 2023 on "Adaptive semantic stream reasoning for healthcare". As a post-doc he performs research on semantic reasoning and project management on behavioral monitoring apps, cloud and dynamic dashboards for health.
If Mathias is not working, chances are high he'll be traveling around somewhere on our planet. If not, he might be found in his couch watching sports, or in his kitchen baking his newest cake creation.
Co-supervised by Sofie Van Hoecke.
Marija is a researcher in the field of Machine Learning. She works on Activity Recognition (AR) and Life Pattern Detection (LPD) on contextualized wearable and environmental sensor data. Her research includes techniques for personalizing AR and LPD models. This research finds applications in e-health with the goal of achieving personalized health management.
Marija has done sports for about all of her life. When in need of an exercising buddy, she is certainly a go-to person! Marija loves books, board games and dancing (the last one she practices at any given opportunity, meaning even in the office, supermarket and public transport)! And euhm yeah, she is a proud Hufflepuff.
Co-supervised by Sofie Van Hoecke.
Diego’s research focuses on bringing machine learning techniques to the vagus nerve stimulation (VNS) domain with the intention of better understanding its physiology and developing novel therapeutic techniques using neuromodulation. In addition, he works in predictive maintenance solutions (Industry 4.0) using matrix profile and deep learning to solve problems concerning fault classification, anomaly detection and remaining useful lifetime.
In his free time Diego enjoys running, reading (ergodic) books, and watercolor painting.
Co-supervised by Sofie Van Hoecke.
Michael's research interests include (i) Hybrid Artificial Intelligence and (ii) eXplainable AI (XAI) both with an emphasis on application domains within the Internet of Things (IoT). Specifically, his research is focused on the cross-fertilization between expert knowledge and machine learning, and ways to combine the transparency and expressivity of the prior with the predictive power of the latter. He currently focusses on researching optimized Knowledge Graph embedding techniques for IoT data.
In his free time, Michael can often be found buried in works of philosophy, trying to piece together the history of ideas, writing the odd poem, or playing video games with his friends.
Co-supervised by Filip De Turck.
Jarne's research main focus is developing and applying hybrid machine learning for the intensive care by closely working with medical professionals to present clinically relevant models for diagnostic and therapeutic purposes. This consists of incorporating medical expert knowledge in machine learning models while providing uncertainty and interpretability, specifically tailored to clinicians. In particular, he is focusing on optimizing antibiotic dosing in the intensive care unit.
Besides a large interest in medicine and machine learning, Jarne likes to dabble in different languages like Japanese and Chinese and get lost in the wondrous world of virtual reality, (tabletop) gaming and Live-Action Role-Playing (LARP).
Co-supervised by Sofie Van Hoecke.
Sandeep is researching knowledge graphs, explainable machine learning, and hybrid machine learning in the domain of healthcare. He is working on providing better care for patients by providing quality of life predictions from treatments to healthcare providers so they can better evaluate the available treatments.
Outside working hours, Sandeep likes to read fiction books (big fan of Stormlight Archive) and playing board games. On long weekends, he can be spotted roaming in hilly regions where he likes to hike.
Co-supervised by Sofie Van Hoecke.
Kush is a researcher in the field of Semantic Web and stream reasoning. He works on aggregators and reasoning over data streams. Kush's research focuses on developing scalable processing technologies in the Solid platform for personalized Linked Data which can find applications in healthcare use cases.
In his free time, Kush can be seen supporting Tottenham hotspurs in the premier league (the best league, btw) or trying to read philosophy or catching up with the recent pro-wrestling and mixed martial arts matches.
Co-supervised by Pieter Bonte.
Maarten's research is focused on reasoning over semantic data. He also researches and develops the use of aggregating technologies in the Solid platform to improve query response times.
In his free time, Maarten play's piano and annoys his neighbors with the sound of someone learning to play the saxophone. He also enjoys watching Formula 1 every other Sunday.
Co-supervised by Pieter Bonte.
Kyana’s research is centered around digital interventions for behavioral change. She develops a chatbot to act as a personalized coach to support behavior change. In her research, the focus is on smoking cessation.
Aside from developing behind a computer, Kyana likes to run three times a week. Hiking in the mountains and woods is the ultimate travel experience, but cultural places fascinate her too. Before she even realizes it herself, she gets lost in a fantasy world, through any media whatsoever: books, manhwa, manga, anime, donghua, movies, kdramas, and tv shows. As long as it takes you on a trip, count her in!
Co-supervised by Sofie Van Hoecke.
Mathijs’ research tackles the logical foundations of decentralized stream reasoning. He mainly focuses on formal, logic-based frameworks that incorporate both the temporal aspects of streaming data and the decentralized approaches to data storage, such as the Solid platform.
Aside from his research, Mathijs can be found debating in several university committees, practicing judo in a local dojo or enjoying a ride on his motorcycle.
Co-supervised by Pieter Bonte.
Tom Windels
PhD Student
PhD Student
Tom's research involves combining data sets found on personal solid pods through Semantic Web streams with tried and tested healthcare related models. The predictions made from these models and data streams are then given back to the user's pod, serving as extra data for both the end user as well as medical professionals.
Besides his research, Tom typically spends his free time either playing video games or working on personal hobby projects. The latter ranges from silly apps to computer graphics projects.
Co-supervised by Sofie Van Hoecke.
Arne is a researcher dedicated to decoding neurocommunication by employing hybrid machine learning techniques. His work revolves around the intersection of artificial intelligence and neuroscience, trying to bridge both realms.
In his free time, Arne likes to go running, get immersed in the world of a captivating book or engage in chess. Further, he enjoys travelling, exploring new places and cultures as well as going for hikes in the mountains or woods.
Co-supervised by Sofie Van Hoecke.
Cédric's research aims to leverage the growing prevalence of wearables in daily life to gain insights into and model user behaviour. The research's goal is to use the collected data for predictive healthcare via various machine learning techniques, with the ultimate intention of supporting users in maintaining or improving their health.
In his spare time, Cédric can be found on his couch watching either cycling or football, attempting to read a book, playing the odd video game, or out and about with his camera - probably doing street photography.
Co-supervised by Sofie Van Hoecke.
Pol's research focuses on enabling scalable analytics and querying across distributed Solid pods containing healthcare data from bariatric patients. His work aims to explore how incremental aggregators can be used to pre-compute computations and allow federated data analytics to enable near real-time query responses over continuously evolving treatment data. Given the sensitive nature of healthcare information, Pol is committed to ensuring privacy and security by employing semantic ontologies to define how patient data is handled and protected throughout the query process.
Outside of work, Pol enjoys spending time with his friends from the scouts. He also loves immersing himself in video games, fantasy novels, and films—whether indie hits or blockbusters. In his free time, you'll often find him at a local pub quiz or gathered with friends for a board game night.
Co-supervised by Ben De Meester.
Ayko is currently pursuing a PhD focused on sustainable and health AI, leveraging knowledge graphs and hybrid AI techniques. His research explores innovative solutions for working with heterogeneous data to improve personalized therapy and develop efficient systems, with a strong emphasis on sustainability and real-world applications across multiple domains.
Outside of research, Ayko enjoys activities like basketball, traveling, spending time with friends, and reading. He appreciates both trying new things and returning to old interests, finding enjoyment in a mix of experiences.
Co-supervised by Sofie Van Hoecke.
Warre's research focuses on personalized healthcare across various data modalities. He works with image data of wounds, developing machine learning models to streamline the evaluation of wound progression, helping nurses and doctors reduce their workload. Additionally, he applies machine learning to optimize treatment pathways for dermatologic conditions like psoriasis, enabling personalized dosing therapy to improve patient outcomes.
In his free time, Warre is always searching for the best surf — whether in Belgium or, even better, in France, Portugal, or Morocco. He loves playing chess and other board games, cooking, and running. During downtime, he enjoys reading the occasional book and watching Netflix.
Co-supervised by Sofie Van Hoecke.
Emma's research focuses on enhancing building management through the integration of semantics and machine learning. She explores various aspects of the built environment, ranging from suggestions for renovation strategies to improving dynamic building management systems.
In her free time, Emma enjoys crocheting clothes, attending spinning classes, and catching up with friends. She loves to explore new hobbies, whether that means learning a new language or signing up for a dance class.
Co-supervised by Sofie Van Hoecke.
Kim is a researcher in the field of Physical activity and Health. She takes a holistic approach by simultaneously studying physical activity, sedentary behavior, dietary intake, and sleep in adults aged 55 to 75. Her research focuses on exploring the temporal interrelationships between these behaviours, examining how they influence each other over time. Additionally, she will evaluate the impact of a personalized mHealth intervention, tailored to be delivered at the right moment and context, to optimize health outcomes.
In her free time, Kim enjoys running, swimming, and playing badminton. She also values spending quality time with family and friends.
Co-supervised by Delfien Van Dyck & Femke De Backere.
Alumni
David performed research on realizing new machine learning architectures and algorithms towards the realization of context-aware machine learning that is able to separate the data from the context in which it was gathered. As such, the algorithm is more transferable between different contexts (or people) without requiring (much) retraining.
Pieter was active in the Stream Reasoning research area, an intersection between Stream Processing and the Semantic Web. He focused mainly on complex query answering and the efficient evaluation of reasoning algorithms over high volatile data streams. He obtained his PhD in 2019 on "Efficient Processing of Heterogeneous IoT Data through Expressive Reasoning Techniques", which can be found here.
Pieter is now a Professor at KU Leuven.
Vic researched novel graph-based machine learning architectures to improve performance and explainability for tasks where data is interconnected, mainly applied to eHealth use cases.
He co-founded Optioryx.
