Gregory D. Hager

About

Gregory D. Hager is the Mandell Bellmore Professor of Computer Science at Johns Hopkins University, with joint appointments in the Department of Electrical and Computer Engineering and the Department of Mechanical Engineering. He is renowned for his research in collaborative and vision-based robotics, time-series analysis of image data, and medical applications of image analysis and robotics. Hager has published over 300 articles and books on these topics and is the founding director of the Johns Hopkins Malone Center for Engineering in Healthcare, an interdisciplinary research center focused on developing innovative healthcare technology and systems. His significant contributions to vision-based robotics have earned him recognition as an IEEE Fellow, as well as fellowships from the MICCAI Society, the Association of Computing Machinery (ACM), the American Institute for Medical and Biological Engineering (AIMBE), and the American Association for the Advancement of Science (AAAS). In 2014, he was awarded a Hans Fischer Fellowship at the Technical University of Munich’s Institute of Advanced Study, where he also holds an appointment in computer science. Hager co-founded two startups: Clear Guide Medical, which provides a platform for more accurate ultrasound-guided procedures, and Ready Robotics, which aims to make industrial robots easier to use. He earned his BA in mathematics and computer science summa cum laude from Luther College in 1983, followed by an MS in 1986 and a…

Research topics

• Computer Science
• Artificial Intelligence
• Engineering
• Computer vision
• Human–computer interaction
• Simulation
• Machine Learning
• World Wide Web
• Pathology
• Knowledge management

Selected publications

• Surgical data science – from concepts toward clinical translation

  Medical Image Analysis · 2022 · 320 citations

  Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational succe…

  DOI

• Large-scale pancreatic cancer detection via non-contrast CT and deep learning

  Nature Medicine · 2023-11-20 · 293 citations

  articleOpen access

  Pancreatic ductal adenocarcinoma (PDAC), the most deadly solid malignancy, is typically detected late and at an inoperable stage. Early or incidental detection is associated with prolonged survival, but screening asymptomatic individuals for PDAC using a single test remains unfeasible due to the low prevalence and potential harms of false positives. Non-contrast computed tomography (CT), routinely performed for clinical indications, offers the potential for large-scale screening, however, identi…

  PublisherOA PDFDOI

• On the use of simulation in robotics: Opportunities, challenges, and suggestions for moving forward

  Proceedings of the National Academy of Sciences · 2020 · 155 citations

  The last five years marked a surge in interest for and use of smart robots, which operate in dynamic and unstructured environments and might interact with humans. We posit that well-validated computer simulation can provide a virtual proving ground that in many cases is instrumental in understanding safely, faster, at lower costs, and more thoroughly how the robots of the future should be designed and controlled for safe operation and improved performance. Against this backdrop, we discuss how s…

  DOI

• Learning From Synthetic Animals

  2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · 2020 · 119 citations

  Despite great success in human parsing, progress for parsing other deformable articulated objects, like animals, is still limited by the lack of labeled data. In this paper, we use synthetic images and ground truth generated from CAD animal models to address this challenge. To bridge the domain gap between real and synthetic images, we propose a novel consistency-constrained semi-supervised learning method (CC-SSL). Our method leverages both spatial and temporal consistencies, to bootstrap weak…

  DOI

• A Roadmap for US Robotics – From Internet to Robotics 2020 Edition

  Foundations and Trends in Robotics · 2021 · 53 citations

  Recently, the robotics industry celebrated its 60-year anniversary. We have used robots for more than six decades to empower people to do things that are typically dirty, dull and/or dangerous. The industry has progressed significantly over the period from basic mechanical assist systems to fully autonomous cars, environmental monitoring and exploration of outer space. We have seen tremendous adoption of IT technology in our daily lives for a diverse set of support tasks. Through use of robots w…

  DOI

Recent grants

• ITR: Modeling Synthesis and Analysis of Human-Machine Collaborative Systems

  NSF · $1.1M · 2002–2008

• Manipulating and Perceiving Simultaneously (MAPS) for Haptic Object Recognition

  NSF · $216k · 2007–2010

• CPS:Medium:Hybrid Systems for Modeling and Teaching the Language of Surgery

  NSF · $1.5M · 2009–2013

Frequent coauthors

• Russell H. Taylor

  85 shared

• Masaru Ishii

  Johns Hopkins Medicine

  67 shared

• Emad M. Boctor

  Johns Hopkins University

  39 shared

• Austin Reiter

  Meta (Israel)

  37 shared

• S. Swaroop Vedula

  Malone University

  34 shared

• Chris Paxton

  31 shared

• Gábor Fichtinger

  Queen's University

  30 shared

• Nassir Navab

  27 shared

Education

• Ph.D., Electrical Engineering and Computer Science

  University of California, Berkeley

  1990

• B.S., Electrical Engineering and Computer Science

  University of California, Berkeley

  1985

Awards & honors

• Hans Fischer Fellowship at the Technical University of Munic…

Similar researchers at Johns Hopkins University

• Alan Yuilleh-134
• Adam Charlesh-126
• Rama Chellappah-124