Prof. Behar is heading the Artificial Intelligence in Medicine Laboratory (AIMLab.) at the Technion Faculty of Biomedical Engineering. Dr. Behar’s work involves research of medical artificial intelligence to benefit patient care and includes development of machine learning algorithms for analyzing large medical unstructured, i.e., physiological time series and medical images and datasets, with an emphasis on early diagnosis in the medical disciplines of cardiology, sleep medicine and ophthalmology. The lab has already conducted large-scale experiments on extensive databases, encompassing millions of participants and thousands of hours of continuous physiological recordings. Dr. Behar made significant contributions to the discovery of diagnostic biomarkers in cardiology and sleep medicine. In 2022, he initiated the Technion-Rambam Initiative in Medical AI (TERA), a collaborative effort between the Technion and Rambam Health Care Campus, utilizing large medical datasets and state-of-the-art AI advances to combat human diseases. Dr. Behar is the founder of SleepAI a medical AI spin-out company of his laboratory which is incorporated at eHealth Venture (eHV).

Course: Deep learning for continuous physiological time series analysis
July, 2nd, 11:00 -12:30

Jeffrey Budzinski is a software and systems architect at Cleveland Clinic. In this role, he works to accelerate Cleveland Clinic’s digital transformations in areas including cloud, data, analytics, and artificial intelligence to improve access to care and enhance the patient and caregiver experience. Previously, Mr. Budzinski was Vice President of Architecture at Yahoo and has over 25 years of Silicon Valley experience building products in diverse domains, including search, advertising, security, and finance. Mr. Budzinski holds degrees from the University of Maryland College Park and Oregon State University

Course: Unlocking Healthcare Innovation with Data Accessibility
June, 30th, 11:00 – 11:30

Ninon Burgos is a CNRS research at the Institut du Cerveau in Paris, co-director of the ARAMIS team and a member of PR[AI]RIE, the PaRis Artificial Intelligence Research InstitutE. She received her PhD from University College London in 2016 and her “Habilitation to supervise research” from Sorbonne University in 2022. In 2019, Ninon Burgos received the Cor Baayen Early Career Researcher Award from ERCIM. Ninon Burgos’ research focuses on the processing and analysis of medical images, the use of images to guide diagnosis, and the application of these methods in the clinic. In particular, she has contributed to: i) the detection of anomalies using traditional image processing techniques and deep generative models, ii) the transfer to the clinic of machine learning approaches for quality control and computer-aided diagnosis, iii) the reproducibility of medical image processing and computer-aided diagnosis tools based on statistical learning, and iv) the development of open-source software.

Course: tbd
July 3rd, 9:00-10:30

Dr. Celi is the principal investigator behind the Medical Information Mart for Intensive Care (MIMIC) and its offsprings, MIMIC-CXR, MIMIC-ED, MIMIC-ECHO, and MIMIC-ECG. With close to 100k users worldwide, an open codebase, and close to 10k publications in Google Scholar, the datasets have undoubtedly shaped the course of machine learning in healthcare in the United States and beyond. His group has written 3 open-access textbooks: “Secondary Analysis of Electronic Health Records” in 2016, “Global Health Informatics: Principles of eHealth and mHealth to Improve Quality of Care” in 2017, and “Leveraging Data Science for Global Health” in 2020. The first has been downloaded over 1.7 million times and translated into Mandarin, Spanish, Korean and Portuguese. The group has created two open online courses, “Global Health Informatics” and “Collaborative Data Science for Healthcare”. Finally, in partnership with hospitals, universities and professional societies across the globe, Dr. Celi and his team have organized over 50 datathons in 22 countries, bringing together students, clinicians, researchers, and engineers to leverage data routinely collected in the process of care.

Course: Health AI Systems Thinking for Equity (HASTE) workshop
July, 1st, 11:00 -12:30

Professor of Anesthesia and Intensive Care Medicine
MD & Bioinformatics, Habilitation to Direct Research, MBA in Health Sciences
Expert in physiological signal processing and new monitoring tools for anesthesia and intensive care.

Keynote: tbd
June 30th, morning 

Professor Jakob Kather holds dual appointments in medicine and computer science at the Technical University (TU) Dresden, Germany, serves as a senior physician in medical oncology at the University Hospital Dresden and holds an additional affiliation with the National Center for Tumor Diseases (NCT) in Heidelberg. His research is focused on applying artificial intelligence in precision oncology. Prof. Kather’s research team at TU Dresden is using deep learning techniques to analyze a spectrum of clinical data, including histopathology, radiology images, textual records, and multimodal datasets. Guided by the belief that medical and tech expertise needs to be combined, medical researchers in his team learn computer programming and data analysis, while computer scientists are immersed in cancer biology and oncology. Prof. Kather chairs the “Working group on Artificial Intelligence” at the German Society of Hematology and Oncology (DGHO) and is a member of the pathology task force of the American Association for Cancer Research (AACR). His work is supported by numerous European and national grants, which enable the team to develop new deep learning methods for medical data analysis techniques and to apply them in precision oncology.

Course: Computational Pathology, Foundation Models, and Vision-Language Models
July, 2nd, 09:00 – 10:30

Dr. David Sontag is Professor at MIT in the Department of Electrical Engineering and Computer Science (EECS) and von Helmholtz Professor in the Institute for Medical Engineering and Science (IMES). He is also a principal investigator in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT and co-founder and CEO at Layer Health. Dr. Sontag’s research interests are in machine learning and artificial intelligence. As part of IMES, he leads a research group that aims to transform healthcare through the use of machine learning. Dr. Sontag joined MIT in 2017 from New York University, where he was Assistant Professor in Computer Science and Data Science from 2011 to 2016, and before this he was a postdoctoral researcher at Microsoft Research New England from 2010 to 2011. Dr. Sontag received the Sprowls award for outstanding doctoral thesis in Computer Science at MIT in 2010, best paper awards at the conferences Empirical Methods in Natural Language Processing (EMNLP), Uncertainty in Artificial Intelligence (UAI), and Neural Information Processing Systems (NeurIPS), faculty awards from Google, Facebook, and Adobe, and a NSF CAREER Award. Dr. Sontag received a B.A. from the University of California, Berkeley.

Course: tbd

Thomas Walter obtained his PhD from the Ecole Nationale Supérieure des Mines de Paris in 2003, in the field of medical image analysis at the school’s Centre de Morphologie Mathématique. He then joined Jan Ellenberg’s group at EMBL in Heidelberg, where he developed artificial intelligence methods for computational phenotyping. In 2012, he joined the Bioinformatics Center at Mines ParisTech, before becoming the Center’s Director in 2018. His work focuses on biomedical image analysis, with applications including high-content screening and computational pathology. In parallel, Thomas Walter continues to teach deep learning for image analysis, and is active in FranceBioImaging and the research group ImaBio. Since 2019, he has also held a PRAIRIE (PaRis Artificial Intelligence Research InstitutE) chair.

Course: Computer vision 1-0-1
June 30th, 6:30-7:30pm