Invited speakers to submit a biographical sketch of ~250 words. Include a color photo in the top left corner.

Submission deadline: 20 September 2024

Alan Godfrey

Dr Godfrey major research is in algorithms for data science and analytics in healthcare. This includes areas of artificial intelligence, machine learning, data mining and multidimensional signal processing. He has published over 100 papers on those topics in various engineering and medical journals from a portfolio of translational-based research. He serves as Deputy Editor for npj/Nature Digital Medicine, Editor for Maturitas and Associate Editor for Journal of NeuroEngineering & Rehabilitation. He is an International Advisory board member for Physiological Measurement. Dr Godfrey is a Member of the Institution Engineering Technology (MIET) and Senior Member of the Institution of Electrical and Electronic Engineering (SMIEEE).

Chris Richter

Dr. Chris Richter is a distinguished expert in academia, science, and industry, with a career spanning healthcare, professional sports, life sciences, and the digitalization of the German government. With over 70 peer-reviewed papers, reviewing activities for more than 15 scientific journals, and 15+ keynotes on movement and AI, Dr. Richter is a leading voice in his field.

Dr. Richter has served as the Head of Data Analytics and Innovation at the Sports Surgery Clinic in Dublin, pioneering advancements in healthcare. He has also consulted for professional sports, including roles with the English Premier League and the NBA as a Biomechanical Consultant at TotalPerformance. In the life sciences sector, he excelled as a Senior Applied Researcher at Kaia Health and currently leads the Data and Technology Team at Byte.

Driven by a passion for bridging the gap between advanced movement capture technologies and the delivery of automated, meaningful insights, Dr. Richter is dedicated to solving complex problems, enhancing processes, and exploring uncharted territories. His fascination with machine learning has made him an innovative force in sports and medical science, continually pushing the boundaries of what is possible.

Join Dr. Richter as he shares his insights on AI and injury prediction, and discover how his work is shaping the future of these fields.

Claudine Lamoth

Claudine Lamoth is a Professor of Movement Analysis and Smart Technology for Healthy Ageing at the University Medical Center Groningen / University of Groningen, Netherlands. Her research focuses on understanding gait and postural control mechanisms in age- and lifestyle-related disorders.

Lamoth's interdisciplinary approach combines dynamical systems theory, biomechanics, behavioral sciences, and data sciences. She collaborates with medical departments, biomedical technology experts, data scientists, artists, and industry partners on projects aimed at prevention, personalized interventions, and early risk detection. Her work is characterized by the innovative use of sensor technology and AI/data science methods. Part of this research involves developing, testing, and validating sensing and monitoring technology that can assess, remotely monitor, and provide personalized services to individuals in their natural (home) environment

Throughout her career,  Lamoth has secured approximately €10 million in research grants and has mentored 15 PhD students and over 100 undergraduates. She currently leads a research group of 7 researchers and 12 PhD students.

Dzmitry Kaliukhovich

Dzmitry Kaliukhovich serves as a Senior Principal Data Scientist in the R&D Data Science & Digital Health division at Johnson & Johnson Innovative Medicine. He specializes in identification and evaluation of digital health technologies and tools for drug development in the pharmaceutical industry. In his role, Dzmitry supports activities related to patient enrichment, phenotyping, and monitoring of patient’s well-being in clinical trials as well as the development and validation of novel biomarkers and endpoints for treatment efficacy. His primary areas of interest cover several disease areas, including neuropsychiatric, neurodegenerative, and pulmonary diseases.

Dzmitry has a proven track of record in analyzing actigraphy, speech, active and passive sensing behavior data generated by wearable devices as well as electrophysiological and eye movement signals in primates and rodents. Dzmitry holds a master’s degree in Computer Science from Brest State Technical University, Belarus, and a Ph.D. in Cognitive Neuroscience from KU Leuven, Belgium. He is based in Antwerpen, Belgium.

Gergő Bolla

I’m a passionate Researcher with a robust background in biomedical engineering and clinical neuroscience. Currently pursuing a PhD in Clinical Neuroscience at Semmelweis University, I’m driven by the application of data science and machine learning in healthcare.

At the Neurocognitive Research Center, I have developed machine learning algorithms for early dementia detection using MRI data, contributing significantly to ongoing research efforts. This work involved collaborating closely with medical experts, ensuring that the models' outputs were both accurate and clinically relevant. Additionally, I played a key role in automating MRI data analysis and developing databases and KPI-s that improved the efficiency of anomaly detection.

Previously, I have contributed to the development and monitoring of machine learning models for pricing and revenue forecasting at Egis Pharmaceuticals. These models were integral to strategic decisions with substantial financial implications. I also designed interactive dashboards, enabling real-time insights into market trends.

Adept in Python, MATLAB, and a range of AI/ML libraries, proficient in data manipulation, visualization, and database management. Outside of work, I enjoy hiking, cycling, reading, and engaging in continuous learning.

Dedicated to leveraging data analytics/data science to drive innovation in both research and industry, aspiring to make impactful contributions to both healthcare and other industrial applications.

Luis Sigcha

Ph.D. in Mechanical Engineering by the Universidad Politécnica de Madrid (UPM), Master in Big Data Analytics from the European University of Madrid, and Master in Acoustic Engineering (UPM). Luis did his doctoral thesis at the Instrumentation and Applied Acoustics Research Group (I2A2-UPM) in collaboration with the Ergonomics & Human Factors Group (EHF) at the Universidade do Minho (Guimares-Portugal). His research involves the automatic detection of human movement using wearable sensors (wearables) and artificial intelligence with application in physical activity monitoring, Parkinson's disease assessment, and occupational risk prevention.

Luis is passionate in the application of cutting-edge technologies to solve real problems. He has participated in 8 research projects funded by competitive grants, authored 9 indexed scientific articles, 4 book chapters, and 9 presentations at international congresses. Additionally, his served as the director of 8 final degree projects (Bachelor and Master) and 1 Ph.D. student.

Currently he is working as a postdoctoral researcher in the cross-European Project: WEALTH (Wearable sensors for the assessment of physical and eating behaviours) and the Spain project: Biomarcadores digitales para la evaluación del estado motor de pacientes con Enfermedad de Párkinson para su aplicación Clínica y Terapéutica (BIOCLITE). Also, he is part of the Data-Driven Computer Engineering Research Group (D2ICE), in the Department of Electronic and Computer Engineering, and the Health Research Institute (HRI) at the University of Limerick.

Neil Cronin

I am a Professor of Exercise Biology at the University of Jyväskylä. My research spans human locomotion, imaging techniques, and more recently, applications of artificial intelligence in the sport and health domains. In particular, in recent years I have explored the use of markerless pose estimation, generative AI techniques for audio and video synthesis, and various medical imaging analysis techniques. At the Budapest Symposium, I will give an overview of recent developments in AI, specifically related to biomechanics and medical image analysis.

Peter van Ooijen

Prof. Dr. Ir. P.M.A. (Peter) van Ooijen is a distinguished professor in at the University Medical Center Groningen, specializing in Medical Informatics, Computer Science, and Artificial Intelligence. With over 25 years of experience in medical imaging informatics, he has made significant contributions to the field, particularly in the application of machine learning and deep learning into medical imaging.

Prof. van Ooijen's research at the department of Radiotherapy focuses on the detection, segmentation, diagnosis, treatment, and prediction of cancer. His work is highly regarded in the scientific community, and he continues to push the boundaries of AI in healthcare.

In addition to his research, Prof. van Ooijen holds several key positions. He is the theme coordinator for "Digital Healthcare" at Jantina Tammes School, coordinator of the Machine Learning Lab at the Data Science Center in Health (DASH), and the president of the European Society of Medical Imaging Informatics (EuSoMII). He also serves on the editorial board of several prestigious journals.

Prof. van Ooijen's prolific academic output is evident in his publication record. He has co-authored more than 200 PubMed enlisted papers and contributed to numerous book as editor and author, contributing significantly to the body of knowledge in his field.