Johns Hopkins University Computer Science

A Johns Hopkins University team led by John C. Malone Associate Professor Mathias Unberath will work with Kitware to create virtual simulations and validation testing on four Advanced Research Projects Agency for Health-funded robotic solutions for stroke.

Unberath and the Johns Hopkins team will develop simulation environments and testing datasets for robotic devices that account for the physical variability inherent in human anatomy and blood vessels, as well as changes in device movement, the flow of imaging contrast through blood vessels, blood flow, and other physical properties.

Mechanical engineer Rajat Mittal, who is working with Unberath on the project, says that while many physical systems rely on how water and air flow in an environment, the biology of blood vessels comprises a complex mix of substances that must navigate through highly flexible structures. He plans to use computing methods that rely on GPUs—or graphics processing units, which can handle math and data processing much faster than standard CPUs—to train the robots in real time.

“Our goal is to create publicly available resources that other scientists can build on to test and validate autonomous robotic medical devices,” says collaborator Gregory D. Hager, the Mandell Bellmore Professor of Computer Science and director of the Laboratory for Computational Sensing and Robotics.

Teams will use virtual simulations to train the robots; Hager says it’s essential to validate those simulations for accuracy and to gauge how well they reflect realistic scenarios among stroke patients.

Congratulations to Russell Taylor on being selected for the 2027 IEEE Robotics & Automation Society IEEE Robotics and Automation Award! 🏆

This prestigious award recognizes his contributions to, leadership in, and translation of surgical and medical robotics. 🤖 🩺

One of the IEEE’s most prestigious honors, this award recognizes a select few extraordinary individuals’ contributions to and significant advancements in the field of robotics and automation.

Taylor was nominated for the award by Allison Okamura, currently the Richard Weiland Professor of Engineering in the Stanford University Department of Mechanical Engineering and formerly a research professor in the Johns Hopkins Department of Mechanical Engineering.

“Russ Taylor is the father of surgical robotics,” says Okamura, whose position at Hopkins was part of the NSF-funded Computer-Integrated Surgical Systems and Technology Engineering Research Center led by Taylor. “Four decades ago, he saw that computers could make surgery safer and more precise, and so he built the field—from the ROBODOC system for hip surgery to the remote-center-of-motion mechanism that underlies the da Vinci surgical system now used in operating rooms worldwide.

“This award recognizes not just his inventions, but also his role as a leader and educator: He led the development of an entire discipline at the intersection of engineering and medicine.”

“Since meeting Russ as a graduate student over 40 years ago, I have witnessed his incredible drive for innovation, his formative role and decades of leadership in computer-integrated medicine, and his passion for making the world a better place by translating technology into real-world impact,” adds Gregory D. Hager, the Mandell Bellmore Professor of Computer Science at Johns Hopkins and Taylor’s longtime colleague. “I have long said that if there is a box, Russ is the person who could be guaranteed to think outside of it.”

Taylor’s research interests include artificial intelligence, robotics, human-machine cooperative systems, medical imaging and modeling, and computer-integrated interventional systems. His 50+ years of professional experience in the fields of computer science, robotics, and computer-integrated interventional medicine have led to over 650 peer-reviewed journal and conference publications and over 100 patents.

A Fellow of the IEEE, the National Academy of Inventors, the American Institute for Medical and Biological Engineering, the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society, and the Engineering School at the University of Tokyo and a Member of the National Academy of Engineering, he is also the recipient of numerous additional awards, including the Maurice Müller Award for Excellence in Computer-Assisted Orthopaedic Surgery, the IEEE Pioneer in Robotics and Automation Award, the MICCAI Society Enduring Impact Award, the Engineering in Medicine and Biology Society’s Technical Field Award, and the Honda Prize.

Taylor holds secondary appointments in Mechanical Engineering, Radiology, Surgery, Nursing, and Otolaryngology—Head and Neck Surgery at Johns Hopkins, where he is continuing to make substantial contributions in all aspects of medical robotics, including mechanism development, robot systems and control, image analysis and image guidance, human-machine interfaces, and a wide range of application areas, including orthopedics, minimally-invasive endoscopic surgery, image-guided needle placement, ophthalmology, otology, laryngology, sinus surgery, and radiation oncology.

As for the future of the field, Taylor points to the extraordinary advances in computational capabilities and technology in recent years that are beginning to enable the us to develop systems with increasing “embodied intelligence.”

“I am most excited by the prospect of developing more and more capable human-machine partnerships in healthcare,” he says. “In addition to surgery, I think that there are enormous needs—and opportunities—in nursing and clinical care. Beyond this, I expect to see similar partnerships evolving in many other fields, presenting both great challenges and great opportunities for our society.

“In terms of the impact of technological change, the promise and the peril are there for all of us.”

Explore what our researchers will be presenting next week at IEEE and :

At , “Reeling It In: Flexible Needle Pick Up via Thread Manipulation for Autonomous Suturing” by Zih-Yun “Sarah” Chiu and collaborators from UC San Diego and Carnegie Mellon University proposes an autonomous framework that uses a suture thread 🪡 as an assistive tool for indirect needle pickup.

At :

In “Distilling Photon-Counting CT into Routine Chest CT through Clinically Validated Degradation Modeling” Junqi Liu, Xinze Zhou, Wenxuan Li, Kai Ding, Alan L. Yuille, Zongwei Zhou, and researchers at UCSF, Harvard University, Emory University, and NVIDIA propose a simulated degradation-to-enhancement method that learns to reverse realistic acquisition artifacts in low-quality energy-integrating CT by leveraging high-quality photon-counting CT as reference.

Wenxuan Li, Pedro R. A. S. Bassi, Xinze Zhou, Qi Chen, Alan L. Yuille, Zongwei Zhou, and collaborators at Harvard, Massachusetts General Hospital, University Hospital Basel, the University of Zurich, Medipol University, Jagiellonian University, the Warmian-Masurian Cancer Center, NVIDIA, and UCSF present the first large-scale, open-source longitudinal and multimodal dataset for multicancer screening in “CancerVerse: A Fully Open Longitudinal and Multimodal Dataset for Multicancer Screening.”

“MRI2Rep: Autoregressive Structured Report Generation for 3D Liver MRI” by Yale University researchers and Zongwei Zhou proposes an autoregressive framework for liver MRI report generation.

In “Merlin Plus: A Large-Scale, Multi-Cancer, Image-Mask-Report Dataset,” Pedro R. A. S. Bassi, Wenxuan Li, Xinze Zhou, Alan L. Yuille, Zongwei Zhou, and collaborators at Harvard, Massachusetts General, Jagiellonian University, Stanford University, the Warmian-Masurian Cancer Center, and UCSF present the first large-scale CT dataset with radiologist-created tumor masks across 9 organs.

and Pedro R. A. S. Bassi, Wenxuan Li, Jieneng Chen, Xinze Zhou, Alan L. Yuille, Zongwei Zhou, and researchers from Duke University, UC Berkeley, the University of Zurich, Medipol University, and UCSF will present “RT-Super: Learning Tumor Segmentation from Future Reports.”

Congratulations, Prof. Zhou! Learn more about how this National Institutes of Health (NIH) funding will help develop AI for detection:

Zongwei Zhou awarded $2.4 million NIH grant The four-year R01 grant will fund the development of AI algorithms capable of detecting three types of abdominal cancers on CT scans, enabling earlier diagnosis and treatment.

Attending IEEE’s ? Stop by the Scalable Tactile Sensing for Dexterous Manipulation Workshop organized by Homanga Bharadhwaj, Gregory D. Hager, and more!

More details below:

Scalable Tactile Sensing for Dexterous Manipulation IROS 2026 workshop · September 27, 2026 · Pittsburgh, Pennsylvania

Matthew Green, a Johns Hopkins professor and expert in cryptography, says the impressive thing about the discovery was its reliance on several existing methods that no one previously thought to put together.

“What’s particularly concerning (and so especially ripe for AI) is that the attack does not invent fundamentally new mathematics,” Green writes. “It simply extends a bunch of tools that were lying around and well-known, and gets a good result.”

Mythos attack on 3rd-round PQC algorithm candidate puts it out of commission HAWK withstood years of testing that had yet to uncover a fatal weakness found through Mythos.

Including Johns Hopkins University’s Suchi Saria! Read the full coverage of Prof. Saria’s work on using AI for early disease detection here: https://www.cs.jhu.edu/news/creator-of-sepsis-detection-tool-named-to-time100-ai-list/

🧡 Welcome, new faculty! 🧡

Scientists have reconstructed the complete genome of a real person, with full sets of chromosomes from each parent—a breakthrough expected to advance research, improve the diagnosis of genetic diseases, and make personalized genomics routine in medical care.

Human genome milestone opens door for personalized genomics The ability to quickly and affordably survey a patient’s entire genome is expected to accelerate research, diagnostics, and precision medicine.