LL-COVID19

Maria Gabrani is a Research Staff Member at IBM Research Zurich since 2001. Her research has focused on image processing and pattern recognition techniques, used to extract meaningful information from data in different application areas, from medical imaging to computational pathology. She is currently a manager of the Cognitive Healthcare and Lifesciences group that focuses on ingesting, analyzing and integrating textual, imaging and molecular data for building health knowledge and disease understanding and supporting decision making in numerous applications, from medical triage, to diagnosis, prognosis and treatment selection and treatment monitoring. She has also global IBM Research roles, as one of the strategists in Future of Health and specifically in the areas of oncology and knowledge representation, as well as a member of the Exploratory Lifesciences Council of IBM Research. Before joining IBM, from 1999 until 2001, Maria worked for Philips Research, in Eindhoven, The Netherlands. She holds a Ph.D and MS degree in Electrical and Computer Engineering from Drexel University, Philadelphia, PA, USA, and a Diploma in Electrical Engineering from Aristotle University, Thessaloniki Greece. She has received numerous awards including several Outstanding technical Achievement awards, from IBM Corporation, and Technical Achievement Award, from IBM Research Division and numerous best paper awards, from NASA/Goddard Space Flight Center, Image Registration Workshop (1997), to GRAIL best paper award, MICCAI (2020), and more than 40 patents.

Jannis is a PhD student jointly between IBM Research Europe and the D-BSSE at ETH Zurich. His current research is on machine learning for healthcare applications, especially deep molecular generative models and medical image analysis. Jannis received a BSc. in Cognitive Science and a M.Sc. in Computational Neuroscience, both with highest distinction. He has worked and studied in Germany, Singapore, the UK and Switzerland and received the FXH Scientific Excellence Award from Roche for his work on cancer drug modelling in 2019.

Dr.Yunfei Long is an algorithm engineer of Huawei. He obtained his PhD from Peking University. His research is at the intersection of medical imaging, fluorescence image and artificial intelligence aiming to build computational tools for improving the efficiency of the big health industry.

Dr. Anirban Mukhopadhyay leads a research group in TU Darmstadt, Germany that focuses on bringing imaging AI to benefit healthcare. Healthcare applications in the group includes diagnostic and interventional radiology, Pathology, image-guided surgery and robotic surgery. To bring in the view of different stakeholders within the healthcare AI domain, Dr. Mukhopadhyay hosts a Podcast called "AI-ready Healthcare." He tweets often about healthcare AI @anirbanakash.

Colin Compas leads the Healthcare Solutions Architect team for NVIDIA in the US. This team focuses on bringing deep learning and accelerated computing solutions to healthcare. His background is in medical image analysis where he has worked to develop automated methods for anomaly detection in clinical decision support. Prior to joining NVIDIA, Colin worked to bring cognitive solutions to radiologists and cardiologists as a part of IBM Research. Colin received his Ph.D. in biomedical engineering from Yale University focusing on quantification of cardiac function in echocardiography. His work has resulted in over 25 journal and conference publications.

Ruud JG van Sloun (Member, IEEE) received the B.Sc. and M.Sc. degrees (cum laude) in electrical engineering and the Ph.D. degree (cum laude) from the Eindhoven University of Technology, Eindhoven, The Netherlands, in 2012, 2014, and 2018, respectively. Since then, he has been an Assistant Professor with the Department of Electrical Engineering at the Eindhoven University of Technology and since January 2020 a Kickstart-AI fellow at Philips Research, Eindhoven. From 2019-2020 he was also a Visiting Professor with the Department of Mathematics and Computer Science at the Weizmann Institute of Science, Rehovot, Israel. He is an NWO Rubicon laureate and received a Google Faculty Research Award in 2020. His current research interests include artificial intelligence and deep learning for front-end (ultrasound) signal processing, model-based deep learning, compressed sensing, ultrasound imaging, and probabilistic signal and image analysis.

Gautam Rajendrakumar Gare is a first-year Ph.D. student at the Robotics Institute of Carnegie Mellon University (CMU), where his research area is Computer Vision and Deep Learning with a focus on AI-based video understanding targeting both natural and medical image domains. He received his Master's in Electrical and Computer Engineering from CMU in 2020 and completed his B.E. in Electronics and Communication Engineering from BMS College of Engineering, India.
(Personal Website: https://ggare-cmu.github.io)

Holger Roth is a Sr. Applied Research Scientist at NVIDIA focusing on deep learning for medical imaging. He has been working closely with clinicians and academics over the past several years to develop deep learning based medical image computing and computer-aided detection models for radiological applications. He is an Associate Editor for IEEE Transactions of Medical Imaging and holds a Ph.D. from University College London, UK. In 2018, he was awarded the MICCAI Young Scientist Publication Impact Award. (Website: https://research.nvidia.com/person/holger-roth)

Bram van Ginneken is Professor of Medical Image Analysis at Radboud University Medical Center and chairs the Diagnostic Image Analysis Group. He also works for Fraunhofer MEVIS in Bremen, Germany, and is a founder of Thirona, a company that develops software and provides services for medical image analysis. He studied Physics at Eindhoven University of Technology and Utrecht University. In 2001, he obtained his PhD at the Image Sciences Institute on Computer-Aided Diagnosis in Chest Radiography. He has (co-)authored over 250 publications in international journals. He is member of the Editorial Board of Medical Image Analysis. He pioneered the concept of challenges in medical image analysis.

Prof. Dorith Shaham is the Director of CT and Cardiothoracic Imaging Unit at the Department of Medical Imaging, Hadassah Ein Kerem, Chair of the Clinical Teaching Committee at the Hebrew University-Hadassah School of Medicine and Deputy Editor of Journal of Thoracic Imaging.
Prof. Shaham earned her MD from the Hebrew University-Hadassah School of Medicine in 1988 and completed her residency in Diagnostic Radiology at Hadassah in 1995. In 1997, Prof. Shaham completed a Clinical Fellowship in the Division of Thoracic Radiology at the New York Hospital, Cornell Medical Center, and in 2003 she returned for a research fellowship at the same institution.
Upon her return to Israel and Hadassah in 1998, Prof. Shaham established the first Lung Cancer Screening Program in Israel at Hadassah, as a part of the International Early Lung Cancer Screening Program (I-ELCAP). She has studied the cost-effectiveness of low-dose CT-based lung cancer screening in Israel and participated in multiple I-ELCAP studies, investigating various aspects related to lung cancer screening.
Prof. Shaham is a member of the steering committee appointed by the Israeli Ministry of Health in 2020, to establish a National Pilot Program for the Implementation of Lung Cancer Screening in Israel. As director of the Cardiothoracic Imaging Unit, she has been extensively involved with interpretation of images of COVID-19 patients since the beginning of the pandemic.

Dr. Rosen-Zvi is a Director of health informatics at IBM Research and a visiting professor at the Faculty of Medicine, The Hebrew University. At IBM Research she co-leads the research strategy of a worldwide team who are experts in AI applied to health data and she is the local senior manager of the IBM Research Haifa department who focuses on deep learning, machine learning and casual inference technologies applied to patients data. Michal holds a PhD in computational physics and completed postdoctoral studies at UC Berkeley, UC Irvine, and the Hebrew University in the area of Machine Learning. She joined IBM Research in 2005 and has since led various projects in the area of machine learning and healthcare. Michal has published more than 40 peer-reviewed papers and served as program committee in conferences such as AAAI, ICML and UAI and reviewer in journals such as Machine Learning and Nature. She serves at various boards and committees such as the Israeli national digital health committee and is elected to serve at the managing board of the Israeli Society of HealthTech.