Monday, August 18, 2025

Registration now open: 2025 NSF Cybersecurity Summit – October 20–23 in Boulder, CO


We’re excited to announce that registration is now open for the 2025 NSF Cybersecurity Summit, taking place October 20–23, 2025, at the UCAR Center Green Campus and NSF NCAR in Boulder, Colorado.

This annual Summit brings together cybersecurity practitioners, technical leaders, and risk managers from the NSF Major Facilities and Cyberinfrastructure community. Attendees will also include key stakeholders and thought leaders from across the scientific and cybersecurity landscapes.

🔹 Explore the 2025 Program
Check out this year’s engaging sessions and speakers.

🔹 Register to Attend
Please complete your registration by October 6, 2025.

🔹 Plan Your Stay
Trusted CI has secured hotel accommodations for Summit attendees.

If you have any questions, please don’t hesitate to reach out. Please send an email to summit@trustedci.org

We look forward to seeing you in Boulder this October!

Sunday, August 10, 2025

Trusted CI Webinar: Securing Medical Imaging AI Models Against Adversarial Attacks, Monday August 25th @ 10am Central

University of Pittsburgh's Shandong Wu is presenting the talk, Securing Medical Imaging AI Models Against Adversarial Attacks, on Monday August 25th at 10am, Central time.

Please register here.

While AI is increasingly present in clinical practice especially for medical imaging, it is imminent to ensure cybersecurity of imaging diagnostic AI models. Newly advanced adversarial attacks pose a threat to the safety of medical AI models, but little is known about the characteristics of this threat. Medical adversarial attacks may lead to serious consequences including patient harm, liability of healthcare providers, and other ethical issues or crimes. It is imperative to study this cybersecurity issue to mitigate potential negative consequences and to ensure safety of health care. In this talk, the speaker will discuss cyber vulnerabilities of deep learning-based medical imaging diagnosis models under adversarial attacks, show real-world experiments on how adversarial attacks can fool AI models to decrease diagnosis performance and to confuse experienced radiologists, and present several methods of defending adversarial attacks to secure AI models in medical imaging applications.

Speaker Bio: 

Shandong Wu, PhD, is a Professor in Radiology, Biomedical Informatics, Bioengineering, and Intelligent Systems at the University of Pittsburgh. Dr. Wu leads the Intelligent Computing for Clinical Imaging (ICCI) lab, and he is the founding director of the Pittsburgh Center for AI Innovation in Medical Imaging. Dr. Wu’s work focuses on developing trustworthy medical imaging AI for clinical/translational applications. Dr. Wu's lab received multiple research awards such as the RSNA Trainee Research Award twice in 2017 and 2019, the 2021 AANS Natus Resident/Fellow Award for Traumatic Brain Injury, the 2025 SPIE Imaging Informatics Best Paper Award, etc. Dr. Wu’s research is supported by NIH, NSF, multiple research foundations, Amazon AWS, Nvidia, and many institutional funding sources. Dr. Wu has published > 190 journal papers and conference papers/abstracts in both the computing and clinical fields. His research has been featured in hundreds of scientific news reports and media outlets in the world. 


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