Alexandros Karargyris has been leading projects related to applications in the intersection of surgery and artificial intelligence (AI). Previously, he worked as a researcher at IBM and NIH for more than 10 years. His research interests lie in the space of medical imaging, machine learning and mobile health. He has contributed to healthcare commercial products and imaging solutions deployed in under-resourced areas. His work has been published in peer-reviewed journals and conferences. 

Renato Umeton heads artificial intelligence (AI) and data science in the Informatics & Analytics department of Dana-Farber Cancer Institute, a teaching-affiliate of Harvard Medical School. His work focuses on Operations, translating AI from the research realm into the Clinic and into Enterprise software offerings that benefit patients and cancer researchers, at scale. Renato started working on artificial intelligence, data science and big data in 2007, when these areas were yet to be well defined; since then he has published in several scientific fields, he has worked in academia, in consulting, in hospital settings, in biotech, and is currently affiliated also with Harvard School of Public Health, Massachusetts Institute of Technology, and Weill Cornell Medicine. 

Micah Sheller currently works as a senior research scientist in Intel’s Security and Privacy Research Labs, where he leads secure federated learning research. He developed the first version of the OpenFL open-source federated learning platform and is the technical federated learning lead for the FeTS initiative, which recently trained a 3DResUNet across 53 hospitals. Micah has had the pleasure of working on a wide range of projects since his first Intel internship in 1999, when he worked on the Intel Web Tablet. Work in USB 3.0, the prototype Intel SGX software runtime, passive and continuous biometrics and more has kept Micah happily learning and making friends throughout his career. 

Spyridon “Spyros” Bakas is an Associate Professor at Indiana University School of Medicine, the Director of the Center for Federated Learning in Medicine, and the Computational Pathology Division Director within the Pathology Department, with secondary appointments in the Departments of Radiology, Neurosurgery, Biostatistics, & Computer Science. He focuses on developing and benchmarking clinically-relevant medical imaging computational algorithms, targeting quantification, imaging-genomics, and federated learning (FL), towards enabling personalized medicine and addressing health disparities and inequities. His collaborative group introduced FL in healthcare in 2018 and drove the largest to-date real-world FL study including 71 institutions across 6 continents. He has co-authored >100 peer-reviewed manuscripts and >70 abstracts, with collaborators across academic ranks and disciplines. He is in the MICCAI Board of Directors, a founding board member (treasurer) of the MICCAI Special Interest Group on Biomedical Image Analysis Challenges (SIG-BIAS), and the organizer of numerous challenges, workshops, & tutorials at both technical & clinical meetings.

Sarthak Pati is a Software Architect and AI Researcher at Indiana University’s Division of Computational Pathology, and has been working with the Medical Working Group of MLCommons since 2022. He is dedicated to designing reproducible, robust, and privacy-focused AI solutions in healthcare. He firmly believes in the F.A.I.R. objective, and has been actively contributing to multiple open-source projects like the Generally Nuanced Deep Learning Framework (GaNDLF), Open Federated Learning library (OpenFl) , and the Federated Tumor Segmentation (FeTS) platform.

Johnu George is a staff engineer at Nutanix with a wealth of experience in building production grade cloud native platforms. He has a strong distributed systems background and has led efforts in building large scale hybrid data pipelines. He holds multiple patents in this area and has been an invited speaker at various conferences like Kubecon, Apache Big Data etc. He is an active open source contributor and has steered several industry collaborations on projects like Kubeflow, Apache Mnemonic and Knative. His current research interests include machine learning system design, distributed learning infrastructure improvements and ML workloads characterization. He is an Apache PMC member and currently chairing Kubeflow Training and AutoML Working groups. 

Alejandro Aristizabal is a machine learing engineer with Factored. He has more than 4 years of experience in Software Development. He holds a bachelors degree in sound engineering. He is very interested in AI Safety, Security and Privacy, as well as human-machine interaction. 

Hasan Kassem is a machine learning engineer with a background in MLOps and Federated Learning. He has been working for more than three years in the fields of software engineering and AI. He holds a master’s degree in robotics and intelligent systems, where his focus was on federated machine learning for surgical applications. He enjoys his free time playing chess and understanding the psychology of human personalities.