| Contact | Organization Name (Contact) (Contact) | Email (Contact) (Contact) | Location | Description of Research Focus Area | Description of Teaming Partner | Technical Areas |
| David Kessler | Columbia University Medical Center | dk2592@cumc.columbia.edu | New York City, NY | I have dedicated my career to research in leveraging novel technology to improve training and patient outcomes. I have a broad background in technology-enhanced clinical research, with specific training and expertise in point-of-care ultrasound and healthcare simulation science.
As a PI or co-investigator on several large-scale, grant-funded, multi-center studies resulting in numerous publications, I understand the critical importance of constructing a realistic and effective research plan. | As a medical content expert with specific expertise in point-of-care ultrasound, I am looking to join data scientist teams who are seeking subject matter experts for this effort. | TA3: Quantify Uncertainty & Improve Clinician Performance, TA1: Automated Surrogate Ground Truth Label Extraction |
| Julia Komissarchik | Glendor, Inc | julia@glendor.com | Draper, UT | Glendor is on a quest to safeguard patients’ privacy by de-identifying Protected Health Information (PHI) while empowering BAA-free data sharing. Glendor PHI Sanitizer - automatic in situ PHI De-identification software that is easily integrated into the existing data workflow. | We are looking to leverage our expertise in
1. the automatic extraction and integration of data across different clinical use cases to establish a “ground truth” about each patient.
2. aggregation and sharing of data across medical institutions and across performers to advance development | TA4: Core Data Infrastructure, TA1: Automated Surrogate Ground Truth Label Extraction |
| Beatrice Knudsen | University of Utah | Beatrice.Knudsen@path.utah.edu | Salt Lake City, UT | Computational Pathology | Device developers, algorithm developers, telehealth experts | TA1: Automated Surrogate Ground Truth Label Extraction |
| Shahriar Nirjon | University of North Carolina at Chapel Hill | nirjon@cs.unc.edu | Chapel Hill, NC | Robustness, Transparency, and debuggability of AI-enabled sensor technology (e.g., movement, camera, audio, and radio signals) to model human activity and gestures; | (1) Medical domain experts who can bring application-specific requirements; conduct user study; (2) Core machine learning and AI researcher; | TA2.1: Continuous Degradation Detection Tools, TA2.2: AI-Based Root-Cause-Analysis Tools |
| Eric Rosenthal | Massachusetts General Hospital | erosenthal@mgh.harvard.edu | Boston, MA | Our NIH Bridge2AI for Clinical Care Program in the CHoRUS Research Network has developed a 14-center Collaborative Cloud enclave of standardized high-resolution hospital admission data including imaging, waveforms, EHR, and text data, enabling testing algorithms that predict hospital and critical care outcomes longitudinally during a patient’s hospitalization as well as time trends across years, e.g., in response to changes in practice. We maintain teaming, ethicolegal, data, and tooling cores. | AI implementation science skillset, computer science methodological innovation. | TA4: Core Data Infrastructure, TA2.3: AI Model Self-Correction Tools, TA3: Quantify Uncertainty & Improve Clinician Performance, TA1: Automated Surrogate Ground Truth Label Extraction |
