Position: Clinical Imaging Scientist at Genentech - USA.

Bio: Dr. Sandra M. Sanabria Bohórquez is a Clinical Imaging Scientist at Genentech, a member of the Roche group. She holds a Bachelor's in Physics and a Master's in Biomedical Engineering from the University of los Andes (Bogota, Colombia). She earned her PhD in Biomedical Sciences from the Catholic University of Louvain (Brussels, Belgium). With over 25 years of experience, she is a recognized expert in PET imaging for Neurosciences and Oncology.

Talk: PET Imaging in Alzheimer's Disease: Understanding Progression and Supporting the Development of New Therapies

Position: Associate Professor with Tenure at the University of Colorado Anschutz Medical Campus - USA.

Bio: Dr. Antonio R. Porras is an Associate Professor with Tenure at the University of Colorado Anschutz Medical Campus, and Director of the Medical Imaging and Machine Intelligence initiative. He also directs the research program of Pediatric Plastic and Reconstructive Surgery at Children’s Hospital Colorado. Dr. Porras is an expert in computer science, biomedical engineering, medical image computing and machine learning with diverse medical applications and domains including radiology, cardiology, surgery, pediatrics and medical genetics.

Talk: A data-driven approach to the management of craniosynostosis

Position: Co-Director of the Craniofacial Surgery Program at Children’s Hospital Colorado - USA.

Bio: Dr. Brooke French, MD FACS is a pediatric plastic and reconstructive surgeon at Children’s Hospital Colorado where she serves as the Co-Director of the Craniofacial Surgery Program, which is a high-volume multidisciplinary endeavor with Pediatric Neurosurgery caring for highly complex patients with diverse diagnoses of congenital and acquired craniofacial differences. She is also the Director of the Cosmetic Program at the University of Colorado School of Medicine. Her research focuses on cleft and craniofacial differences, and she leads resident and medical student education in craniofacial anomalies and cosmetic surgery.

Talk: A data-driven approach to the management of craniosynostosis

Position: Full Professor at Centrale Nantes and the LS2N Lab - USA.

Bio: Dr. Diana Mateus is Full Professor at Centrale Nantes and the LS2N Lab. Her research focuses on designing hybrid methods combining learning approaches with domain knowledge and/or model-based methods to improve the acquisition, reconstruction and analysis of medical images. She currently contributes to the SIRIC Iliad and DHOLMEN Labex projects with the CHU Nantes on the PET image reconstruction and survival analysis of hematological cancers from multi-modal data. She also collaborates with biology/optics (Stevens University) and sports/medical partners (CHU Nantes/MIP Lab) on the reconstruction and analysis of ultrasound and OCT images. She was holder of the MILCOM Chair (Multimodal Image analysis and Learning for COmputational based Medicine) awarded by the region Pays de la Loire within the Connect Talent program and co-financed by the FEDER EU funds and Nantes city. She previously worked as research scientist in Germany at the CAMP team from the Technical University of Munich (2009-2017), and Helmholtz Zentrum (2011-2015). She obtained her PhD in Mathematics and Computer Science from INRIA Rhône Alpes/INP Grenoble in 2009 (France), a MSc on automation systems from LAAS/Univ Toulouse (France), and a BSc in Electronics Engineering from the Javeriana University (Colombia).

Talk: Implicit Neural Models for Representing Medical Images from Reconstruction to Shape Analysis.

Position: Professor of Neurology and Biomedical Engineering at the Mark and Mary Stevens Neuroimaging and Informatics Institute at the University of Southern California

Bio: Dr. Neda Jahanshad is a Professor of Neurology and Biomedical Engineering at the Mark and Mary Stevens Neuroimaging and Informatics Institute at the University of Southern California. Her lab, the Laboratory of Brain eScience, conducts research focusing on developing and applying advanced computational neuroimaging and data analytics to study the brain across the lifespan. She is associate director of the ENIGMA consortium, an international collaboration that harmonizes brain imaging data from tens of thousands of individuals worldwide to uncover risk factors and biological markers of neurological and psychiatric disorders. Dr. Jahanshad’s work integrates imaging, genetics, and AI and data science techniques to better understand brain structure, variability, and decline, with the goal of advancing precision medicine for brain health.

Talk: Integrating neuroimaging data from around the world for large-scale global collaborative studies of brain health and disease

Position: Full Professor at Universidad Nacional de Colombia - Colombia. Director of MindLab Research Group

Short course: "AI and Health", The talk will provide an overview of the development of artificial intelligence from its beginnings to the present day, and how it has been applied to solving problems in the medical context.

Bio: Fabio A. González is a full professor in the Department of Systems and Industrial Engineering at the National University of Colombia, Bogotá, where he leads the Machine Learning, Perception and Discovery Lab (MindLab). He holds a degree in Computing Systems Engineering and an MSc in Mathematics from the National University of Colombia, as well as an MSc and a PhD in Computer Science from the University of Memphis, USA. His research focuses on artificial intelligence, machine learning, quantum computing, and their applications in various fields. He has been a visiting professor at multiple universities in the U.S. and Europe, and has received awards such as the Latin American Research Award from Google Research and the Angela Restrepo Academic Excellence Award for Best Researcher in the Converging Technologies focus area from MinCiencias in 2022. The research.com portal recognizes him as the leading researcher in Computer Science in Colombia.

Position: Full Professor at Universidad de los Andes - Colombia. Director of Center for Research and Formation in Artificial Intelligence

Short course: "AI for Social Good"

Abstract: The main objective of this activity is to show participants how to implement artificial intelligence algorithms for medical image segmentation in 3D Slicer and how to use pre-trained models through available extensions. In addition, a theoretical introduction to some of the most relevant algorithms and platforms in the medical imaging field—such as nnU-Net and TotalSegmentator—will be provided. Participants may bring their own laptops to apply the algorithms in real time during the activity, although this is not required to follow along. By the end of the session, attendees will be able to implement automatic segmentation solutions in their own clinical or research workflows.

Level: Intermediate (basic knowledge of Python recommended).

Position: Clinical Imaging IT Specialist IV at UCLA Health

Short course: "3D Visualization and Printing Primer"