Visitors

Andrija Petrovic is a highly accomplished professional with a diverse academic and industrial background. He holds two PhDs from the University of Belgrade, one in Process/Chemical Engineering and the other in Machine Learning.

Andrija has extensive industrial experience in a variety of areas, including NLP, computer vision, and machine learning. He is an expert in chemical engineering and has specialized in the design of heat exchangers and ejectors.

Currently, Andrija is a Graduate Researcher and Teaching Assistant with PhD at the University of Belgrade and an AI Architect at Intellya company. His research interests include Variational Inference and Probabilistic Graphical Modelling, which showcase his commitment to advancing the field of machine learning. 

Antoine is interested in Artificial Intelligence and medicine. During the Covid-19 crisis, together with a friend, he created the Corovisa application which uses Machine Learning and data analysis to secure certificates of non-contamination. During this citizen initiative, he worked with the cabinet of the Ministry of Health and the largest French pharmaceutical laboratories.

He further specialized with master courses in Machine Learning and Bayesian Learning. In collaboration with the hospitals of Paris, he carried out and published an AI project on improving the understanding of Priority Neighbourhoods using bank data, under the supervision of the senior advisor of the French Minister of Industry.

He is working with us on issues related to discovered ODE.

Elisabeth is a visiting student at the van der Schaar Lab. Her research interests lie in machine learning, neurosciences and everything in between. Elisabeth is currently doing a PhD at KU Leuven university under the supervision of Prof. Maarten De Vos. Her PhD is funded by the Research Foundation – Flanders through the competitive strategic basic research fellowship. The project revolves around automated sleep analysis from brain signals recorded with wearable monitoring devices. Prior to this, Elisabeth graduated summa cum laude from KU Leuven for the MSc in Biomedical Engineering. During her bachelor’s and master’s studies, she was involved in multiple research projects at KU Leuven and Johns Hopkins University, resulting in three scientific publications. After obtaining her master’s degree, she did a 6-month research internship at the Neuroengineering Lab in EPFL.

In her research stay at the van der Schaar Lab, Elisabeth is focusing on interpretability and expert feedback for next-generation automated sleep analysis models. As such, she aims to build better machine learning models for sleep staging, and to empower sleep clinicians in their daily duties through insights generated from these models.

Prof Woillard has been a Doctor of Pharmacy since 2008. He obtained a Master in Pharmacology in 2007 followed by a PhD in Pharmacogenetics and Pharmacokinetics in 2011. He is Head of the “TDM and pharmacokinetics” unit in the Department of Pharmacology, Toxicology and Pharmacovigilance (managed by Prof. Pierre Marquet) at Limoges University Hospital. He is a member of the International Association of Therapeutic Drug Monitoring and Clinical Toxicology (IATDMCT) and vice chair of the Pharmacometrics Committee, the European Association for Clinical Pharmacology and Therapeutics (EACPT) and of the French Society of Pharmacology and Therapeutics (SFPT).

Dr Woillard conducts his research in the INSERM U1248 unit (INSERM is the French National Institute of Medical and Health Research) on treatment personalization, mainly concerning immunosuppressants (IS) in organ transplantation, which covers: pharmacogenetic and pharmacodynamic studies of IS; development of original models in pharmacokinetics; statistical modeling; and application to routine treatment personalization in transplant recipients. He is also interested in antibiotics modeling and their dose individualization. His new research focused on the application of machine learning methods to therapeutic drug monitoring and pharmacometrics.  He has 81 publications in peer-reviewed, international journals and has done over 25 presentations in national and international congresses. 

Johanna is a visiting student at the Van der Schaar Lab and the Cambridge Center for AI in Medicine. Johanna is currently studying medicine at Heidelberg University in Germany and is doing her MD thesis in the research group “Artificial Intelligence and Cognitive Robotics” within the division for minimally invasive and robot-assisted surgery. 

Her research interests lie especially in oncological surgery, computer vision and workflow modelling. 

Funded by the Helmholtz International Graduate School for Cancer Research as part of the projects “Data Science Driven Surgical Oncology” and “Surgomics”, she aims to contribute to predicting life-threatening complications in esophageal surgery via machine learning methods on multimodal data. Her vision is by performing process modelling and analysis to develop an intra-operative context-aware decision support system in oncological visceral surgery. The project involves intensive collaboration with physicians, computer scientists and interaction/product designers.

Within her internship at the Van der Schaar Lab, Johanna wants to broaden her knowledge of machine learning methods and their applications in medicine with a special focus on the project “Precision Medicine”. 

Lasse Hansen is a PhD student at Aarhus University/Aarhus University Hospital. His research centers on developing implementable machine-learning models within psychiatry, focusing on natural language processing of clinical notes.  He holds a MSc in Cognitive Science from Aarhus University.

In his research stay at the van der Schaar lab, Lasse works on combining data-centric methods with synthetic data generation to improve data access and improve augmentation strategies for data from electronic health records.

Onintze is a PhD student in the Machine Learning group at the Basque Center for Applied Mathematics. She previously obtained a BSc in Mathematics at the University of the Basque Country and holds a MSc in Biostatistics from the Complutense University of Madrid. 

Onintze’s current research is focused on developing probabilistic generative models for healthcare. In particular, she has developed methods to infer the latent progression of diseases from Electronic Health Records, addressing common challenges in medical data such as heterogeneity, missing data and interpretability.

Onintze also contributed her mathematical modelling skills to the multidisciplinary task force that assisted the Basque health managers and Government during the COVID-19 pandemic.

Sandro Radovanović is an assistant professor at the University of Belgrade, Faculty of Organizational Sciences. His research builds on designing and developing machine learning models and decision support systems, with a focus on fairness and equality in algorithmic decision-making. He finished his PhD and MSc at the University of Belgrade in the Machine Learning area.

In his research stay at the CCAIM, Sandro focuses on designing a variational autoencoder for the generation of counterfactual data. He aims at modeling disentangled representation of hidden factors and the sensitive attribute (control variable) in the latent space. The method Sandro is developing can generate counterfactual synthetic data that can enable data sharing and other downstream tasks in algorithmic fairness (as well as in other areas).