The Team

Dr. Nils Daniel Forkert is a Full Professor at the University of Calgary in the Departments of Radiology, Clinical Neurosciences, and Electrical and Software Engineering. He received his German diploma in Computer Science in 2009 from the University of Hamburg, his master’s degree in medical physics in 2012 from the Technical University of Kaiserslautern, his PhD in computer science in 2013 from the University of Hamburg, and completed a postdoctoral fellowship at Stanford University before joining the University of Calgary as an Assistant Professor in 2014. He is an imaging and machine learning scientist who develops new image processing methods, predictive algorithms, and software tools for the analysis of medical data. This includes the extraction of clinically relevant parameters and biomarkers from medical data describing the morphology and function of organs with the aim of supporting clinical studies and preclinical research as well as developing computer-aided diagnosis and patient-specific, precision-medicine, prediction models using machine learning based on multi-modal medical data. Dr. Forkert is a Canada Research Chair (Tier 2) in Medical Image Analysis, and Director of the Child Health Data Science Program of the Alberta Children's Hospital Research Institute as well as the Theme Lead for Machine Learning in Neuroscience of the Hotchkiss Brain Institute at the University of Calgary. He has published over 200 peer-reviewed manuscripts, over 90 full-length proceedings papers, over 150 conference abstracts, 1 book, and 2 book chapters. He has received major funding from the Canadian Institutes of Health Research (CIHR), Natural Sciences and Engineering Research Council (NSERC), the Heart and Stroke Foundation, Calgary Foundation, and the National Institutes of Health as a PI or co-PI.

Dr. Matthias Wilms is an Assistant Professor at the University of Calgary in the Departments of Paediatrics and Community Health Sciences. He received his BSc in Applied Computer Science from the Hamburg University of Applied Sciences in 2009, a MSc in Computer Science from the University of Hamburg in 2012, and his PhD in Computer Science from the University of Luebeck in 2018. Prior to joining the University of Calgary as a faculty member in 2022, he was a Postdoctoral Fellow in the Medical Image Processing and Machine Learning Lab at UCalgary. Dr. Wilms is an expert in machine learning-based medical image analysis. His research centers around the development of machine learning solutions for health data science and precision medicine problems where he is specifically interested in developing and advancing machine learning methods that accurately model the complex dynamics and variations of normal or pathological processes in the human body by integrating and combining diverse, large-scale medical data (e.g., images, clinical data, text reports). These models can then serve as clinical computer-aided diagnosis support tools or as tools for systematic data exploration in research scenarios. While the sensitivity and specificity of the models are of paramount importance in healthcare, his work also explicitly focuses on the explainability and interpretability of their decisions to enhance acceptability and trust by clinicians and patients. Finally, he is also interested in developing methods that achieve good results even if trained with limited data, which is a major problem in pediatric applications and/or rare diseases where data is especially challenging to collect. Examples of his work include the modeling of brain aging using neuroimaging data and the detection of genetic syndromes from 3D facial scans.

Anthony Winder graduated from the University of Calgary in 2018 with a BSc Neuroscience. After working summer studentships in the Medical Image Processing and Machine Learning Lab, he completed a Master's degree in Neuroscience with a specialization in Medical Imaging in the lab. Since completing his Master's degree, he is working as a Research Associate in the Medical Image Processing and Machine Learning lab. His research focuses on optimizing machine learning for tissue outcome prediction in acute ischemic stroke patients. Currently, he is working on a deep learning project to model stroke evolution using CT perfusion data.

I obtained my Ph.D. in Applied Mathematics from Washington State University in 2023 under the supervision of Dr. Nikolaos K. Voulgarakis. Upon completion of the degree, I joined MIPLAB under the mentorship of Dr. Nils Forkert, initially as an Eyes High Postdoctoral Scholar and am currently serving as an Alberta Innovates Postdoctoral Fellow. My research interests encompass Boolean networks, the critical dynamics of complex systems, and the modeling of associative memory using the Hopfield network.

Kimberly graduated from the University of Guadalajara, Mexico, in 2018 with a BSc in Biomedical Engineering. She is currently pursuing a PhD degree in Biomedical Engineering with a Medical Imaging Specialization at the University of Calgary under the supervision of Dr. Forkert. Mainly, she is interested in establishing innovative solutions for current clinical problems. Her current research project focuses on utilizing deep learning models to predict stroke tissue outcomes, aiming to improve the clinical decision-making process. 

Sara graduated from the University of Waterloo in 2022 with a BSc in Chemical Physics. She is now pursuing her Masters in Biomedical Engineering with a specialization in Medical Imaging at the University of Calgary under Dr. Forkert. Her computational science background has led her to become passionate about the utilization of artificial intelligence in Medical Imaging. Sara's project will focus on the application of machine learning to neuroimaging for computer aided diagnosis of early-stage neurological diseases.

I completed a BSc in Physics from the University of British Columbia in 2016 and am now pursuing my PhD in Biomedical Engineering with a specialization in Medical Imaging from the University of Calgary. My project revolves around computationally modeling neurological conditions using deep learning architectures. I have always been interested in the vast applications of artificial intelligence and statistics and believe my project will meaningfully contribute to a more comprehensive understanding of neurological conditions. 

Garazi completed her bachelor's degree in Biomedical Engineering at Mondragon Unibertsitatea (Basque Country, Spain) (2019-2023). She completed her thesis at the BioMobility department of the Università Degli Studi di Padova (Italy) while studying the gait alterations in children with Fragile X Syndrome. She is a fall 2023 thesis-based MSc student in Neuroscience at the University of Calgary. She is interested in understanding how the brain works on its most complex way, and trying to improve the quality of life of human beings by means of the interaction of medicine and technology is an objective for her. Her research project consists of investigating the structural and functional properties of the brain in children with severe behaviors, with the aim of developing a protocol for imaging children with neurodevelopmental disorders who have behaviors of concern.

Raissa graduated from Sao Paulo State University, Brazil, in 2017 with a BSc in Computer Science. She also studied abroad at University of California, San Diego and got passionate about applying computer science to improve medical care as a medical trainee at University of California, Los Angeles, in 2015. She is currently pursuing a Ph.D. degree in Biomedical Engineering with a Medical Imaging Specialization. Mainly, she is interested in developing distributed learning methods capable of training with small sample sizes, offering new opportunities to apply machine learning models in rare diseases, pediatric research, and small hospitals.

Emma Stanley completed her undergraduate degree in Chemical and Biological Engineering at the University of British Columbia, where she was involved in bioengineering, synthetic biology, and image analysis research. She is currently pursuing a PhD in Biomedical Engineering with Medical Imaging Specialization. Her research focuses on improving understanding of bias and fairness in AI for medical image analysis. More specifically, she aims to develop methods to systematically study how biases in medical images impact deep learning pipelines and how medical imaging AI affects health equity from a sociotechnical perspective.

Gabrielle graduated from Thompson Rivers University, Kamloops, BC in 2021 with a BSc in Mathematics. She is now pursuing a PhD in Biomedical Engineering with a specialization in Medical Imaging at the University of Calgary under Dr. Forkert. Building upon prior research utilizing genotypic data, her research project will focus on integrating genotypic and phenotypic data in the form of medical images for analysis.

Chris Nielsen is currently pursuing a PhD in Biomedical Engineering, Medical Imaging specialization, at the University of Calgary under the supervision of Dr. Nils Forkert. At the University of Calgary, he previously earned an MSc in Electrical Engineering in 2019 and a BSc in Applied Mathematics in 2016. His MSc thesis focused on the challenge of training machine learning models for medical image classification where there is a limited volume of data. From 2017 until joining the MIPLAB in 2021, Chris worked as an industrial data scientist at Getty Images, applying machine learning to improve image search. As a PhD student, his research interests involve developing machine learning tools for ophthalmology. Chris aspires to become a clinician-scientist specializing in ophthalmology and balancing direct surgical intervention with research and policy at the intersection of artificial intelligence and medicine.

I graduated from the University of Moratuwa, Sri Lanka, with a BSc (Hons) in Electronic and Telecommunications engineering. My honours project focused on applying deep-learning techniques to identify patterns in brain waves. Due to my interest in medical imaging, I pursued a PhD at the University of Auckland, New Zealand, where I developed image-processing techniques to analyse large-scale coronary microscopy images. As a postdoctoral fellow at the MIP lab, I am researching causal models to explain, interpret, and generalize medical data.

After obtaining BSc and MSc degrees in Biomedical Engineering from Universitat Politècnica de Catalunya and Universidad de Navarra, Spain, I did a PhD at the Basque Center on Cognition, Brain and Language. For my PhD, I developed novel hemodynamic deconvolution algorithms to blindly estimate neuronal-related events from functional MRI data without any information of the timing of these events. I joined the University of Calgary as a postdoctoral researcher in 2024 to work on innovative deep learning methods to predict tissue outcomes in acute ischemic stroke patients, with the goal of to improving our understanding of the temporal evolution of acute ischemic stroke and identify new treatment targets. I am mainly interested in studying causality with cutting-edge deep learning techniques like graph neural networks, as well as employing federated learning to ensure confidentiality of patient data and enrich the model’s robustness and applicability to diverse patient populations.

Beth graduated from Queen's University in 2022 with a BASC in Electrical Engineering. She is currently pursuing a Master's degree in Biomedical Engineering with a Medical Imaging Specialization at the University of Calgary under the supervision of Dr. Forkert. She is currently working on a project for brain age prediction using machine learning with the application of how the brain prematurely ages in different diseases.