RAPID Teaming Profiles
Thank you for showing an interest in ARPA-H’s Rare Disease AI/ML for Precision Integrated Diagnostics (RAPID) program. This page is designed to help facilitate connections between prospective proposers. If either you or your organization are interested in teaming, please submit your information via the portal linked below. Your details will then be added to the list below, which is publicly available.
RAPID anticipates that teaming will be necessary to achieve the goals of the program. Prospective performers are encouraged (but not required) to form teams with varied technical expertise to submit a proposal to the RAPID solicitation.
Please note that by publishing the teaming profiles list, ARPA-H is not endorsing, sponsoring, or otherwise evaluating the qualifications of the individuals or organizations included here. Submissions to the teaming profiles list are reviewed and updated periodically.
Interested in learning more about the RAPID program?
- Read the RAPID draft ISO.
- Register to attend the hybrid Proposers’ Day.
- View the RAPID program overview page.
RAPID Teaming Profiles
To narrow the results in the Teaming Profiles List, please use the input below to filter results based on your search term. The list will filter as you type.
| Contact | Organization Name | Location | Description of Research Focus Area | Description of Teaming Partner | Technical Areas | |
| Tracey Sikora | NORD | tsikora@rarediseases.org | Washington, DC | NORD's mission is to improve the lives of people with rare diseases by advancing care, research, and policy. Our network of 40 Rare Disease Centers of Excellence, spanning 26 states and Washington, DC, leads collaborative, multi-center research and works to reduce the diagnostic odyssey for all rare disease patients. Additionally, NORD's IAMRARE registry platform supports registries collecting patient data for over 140 different rare diseases. | NORD seeks partnerships with stakeholders committed to advancing rare disease research. We provide expertise in registries/natural history studies, multi-site clinical research, and drug development, and aim to foster collaboration that accelerates treatments and improves outcomes for rare disease patients. | TA1: Massive-Scale Rare Disease Dataset |
| Azadeh Nasuhidehnavi | Binghamton University | anasuhi@binghamton.edu | Johnson City, NY | Investigating immune and metabolic alterations during Trypanosoma cruzi infection | We are looking for collaboration for spatial transcriptomics and also sing-cell RNA sequencing to complement these data with our spatial metabolomics approach | TA2: Novel Diagnostic Indicators and Population-Scale Discovery |
| Tom Northardt | Triton Systems Inc. | tnorthardt@tritonsys.com | Chelmsford, MA, MA | Triton Systems, Inc. is a high-technology development small business founded in 1992 that develops advanced technologies for commercial, industrial, and Government applications. Triton's offices are located in Chelmsford, MA. Triton has 14 laboratories and development centers that support a full spectrum of in-house operations in chemistry, biology, sensors, composites, advanced manufacturing, acoustics, robotics, optics, assembly and test, ergonomic evaluation, and virtual reality. | For the RAPIDS program, Triton can offer statistical modeling, screening algorithms, and AI/ML support. Triton would be looking to support with regard to TA1 | TA3: Sustainable Platform for AI Diagnostic Development |
| Julia Komissarchik | Glendor, Inc | julia@glendor.com | Draper, UT | Software for PHI Deidentification of Multimodal Medical Data to empower medical data sharing, while protecting patient's privacy. Fully automatic: no tweaking or customization needed At Source (on prem or cloud): unlike APIs and 3rd party services that require sensitive data to be shared Easy to integrate and use: no BAA required, 1 min to install and start running Multiple Modalities Multiple Formats: medical images, reports, videos, photos, audio | We are looking for partners who would like to deidentify data contributed by the patients. Use Case 1: deidentification of data by the patients and their caretakers, to ensure that the patients are comfortable sharing the data. Use Case 2: deidentification of data before it enters the aggregator site, to ensure that the patients are comfortable sharing the data & to avoid HIPAA violations and reputation damage to the institutions. Can be used as a standalone tool or to augment existing workflow. | TA3: Sustainable Platform for AI Diagnostic Development |
| Mahdi Moqri | Biomarkers of Aging Consortium | mmoqri@bwh.harvard.edu | Boston, MA | Biomarkers of Aging Consortium's Biolearn platform enables curation and aggregation of large scale health data, in particular omics datasets, by developing data standardization protocols. Our core members are leading research labs from Stanford and Harvard and our expertise is on development and validation of predictive models for patient outcomes using data harmonization and Machine Learning. List of Biolearn Harmonized Cohorts are available at https://www.agingconsortium.org/biolearn | We are looking for partners with domain expertise or omics datasets from patients with rare diseases. | TA1: Massive-Scale Rare Disease Dataset |
| Peter Fish | Mendelian | jez@mendelian.co | London, UK | Mendelian's platform, MendelScan, finds undiagnosed rare disease patients by searching population EHRs for patterns of disease and provides clinicians with end-to-end functionality to secure diagnoses. MendelScan covers 20% of England's NHS, and has won prestigious awards (eg NHS AI Award, EU, MIT). Mendelian plans to adapt MendelScan to the US market, to develop a single platform for deploying modular solutions for finding undiagnosed rare disease patients. | Mendelian will focus on TA3 (developing a platform), and seeks partners with expertise in: (1) TA1 - EHR and other data stream integration in the US market; (2) rare disease clinical champions (eg academic partners in specific rare diseases / phenotypes); (3) TA2 - partners with multi-modal research approaches for finding undiagnosed patients (that could be integrated into the MendelScan platform). | TA1: Massive-Scale Rare Disease Dataset |
| Adam Buchanan | Geisinger | ahbuchanan@geisinger.edu | Danville, PA | We are a rural, genomics-enabled learning health system that is focused on population-level identification of and care for families with inherited risks for serious diseases. Our robust genomic data environment supports our clinical and research endeavors as we implement and evaluate interventions to scale the incorporation of genomic data into care across our health system. | Like-minded health systems with robust learning health system infrastructures and patient populations that are complementary in race, ethnicity, and setting. | TA2: Novel Diagnostic Indicators and Population-Scale Discovery |
| Stephen Montgomery | Stanford University | smontgom@stanford.edu | Stanford, CA | Our rare disease effort has focused on recruitment, generation of novel multi-modal data and syntheses of these data using computational techniques including AI/ML. Our site is connected to the Stanford Health Care system and is a key contributor to the GREGoR project https://gregor.stanford.edu/about-us. Our areas of research have focused on how to (a) synthesize patient data to provide diagnoses and (b) evaluate novel data types to increase diagnoses. | Our organization is open to a range of collaborators from rare disease patients and advocacy groups, to machine learning experts, to IT experts, to health care, disease domain and policy experts. | TA2: Novel Diagnostic Indicators and Population-Scale Discovery |
| Matthew Wheeler | Stanford University | wheelerm@stanford.edu | Palo Alto, CA | The Center for Undiagnosed Diseases at Stanford is a member clinical site of the Undiagnosed Diseases Network. Over the past 10 years we have leveraged diagnostic technologies and diagnostic expertise together with novel methods to find and discover diagnoses for patients whose diseases are rare or unrecognized by their clinicians or by medical science. As members of the Undiagnosed Diseases Network (UDN), the Stanford team leverages novel molecular diagnostics to enable rare disease diagnosis. | The Center team and UDN are interested in partners with expertise in integration with EHR systems, data analysis platforms, and scalable molecular solutions, in addition to partners who can scale participant interactions and connection of diagnosed patients to therapeutic and research development opportunities. | TA3: Sustainable Platform for AI Diagnostic Development |
| Ryan Smith | TruDiagnostic | ryan@trudiagnostic.com | Lexington, KY | We are a CLIA lab and health data company which specializes in the development of DNA methylation algorithms. Since our inception 4 years ago, we have built the largest DNA methylation dataset in the world and have created algorithms for chronological age, biological age, 18 cell immune deconvolution, mitotic clocks, cancer diagnostics, methylation risk scores for 10+ diseases and epigenetic biomarker proxies for the prediction of 1700 metabolomic, proteomic, and clinical values. | We are looking to leverage the robust amount of data available through DNAm to help diagnose some of these rare diseases by helping a team train novel algorithms with DNAm data through our expertise on array platforms and epigenetic disease and biomarker modeling. We have access to large biobank datasets and an internal dataset of 100,000+ patients. | TA1: Massive-Scale Rare Disease Dataset |
| Chase Egbo | Millennial Informatics | eegbo@millennialinformatics.com | Atlanta, GA | Millennial Informatics specialize in developing tailored data science, analytics and data processing pipelines, and visualization solutions. Our solutions empower organizations and teams to harness the full potential of their data. | We can supply the advance data science, data integration and analytics platform solution and professional services. An I deal partner for teaming includes health research focused organization, and other technology/EHR service providers. | TA1: Massive-Scale Rare Disease Dataset |
| Debi Willis | PatientLink | debi@mypatientlink.com | Oklahoma City, OK | PatientLink builds software to enable data interoperability between clinical heath systems, patients, & research teams. We currently provide access to patients in over 65,000 clinic locations across the US. We also provide the ability for patients to share their EHR data, adverse events, patient reported outcomes (& any other data that can be completed by the patient) with research teams. Our mission is to enable the fast, complete, and accurate flow of data between systems to find cures faster. | We provide the technology and not the clinical/research expertise. We would like to partner with a group who would like to use our technology to better engage patients and receive a more complete set of data for better understanding and monitoring of patient health. | TA3: Sustainable Platform for AI Diagnostic Development |
| Stephen Kingsmore | Rady Children's Institute for Genomic Medicine | stephen.kingsmore@gmail.com | San Diego, CA | Technology development for scalable, inpatient rapid diagnostic genome sequencing (RDGS) & newborn screening by genome sequencing (gNBS) for genetic diseases; clinical trials to evaluate clinical utility & cost effectiveness of RDGS & gNBS to facilitate reimbursement; federated queries of large genome datasets to validate genetic disorders and variants for population screening; clinical guidance engine development to bridge gaps between diagnostic results & genomic medicine interventions. | Additional curated datasets of longitudinal rare disease patient data; additional digital tools to collect data directly from patients; AI/ML platforms for data collection, and advancements in diagnostic decision support systems for rare diseases | TA1: Massive-Scale Rare Disease Dataset |
| Dmitri Krylov, PhD | Advantex Consulting, LLC/DXComposer.ai | dmkrylov@gmail.com | Potomac, MD | AI medical diagnostic development with over 20 years of experience in medical diagnostics | Business development | TA1: Massive-Scale Rare Disease Dataset |
| Lukas Lange | Probably Genetic | lukas@probablygenetic.com | San Francisco, CA | Probably Genetic’s mission is to diagnose 200 million rare disease patients. We developed a patient-facing AI platform that: 1. Sources undiagnosed patients online 2. Collects multi-modal phenotypic data & predicts what disease they might have 3. Runs a whole genome sequencing test 4. Connects patients to next steps (e.g. trial/treatment) Our research is focused on using AI to (I) improve multi-modal phenotypic data collection from patients and (II) predict the disease of undiagnosed patients. | We are eager to learn more about the performers in the RAPID program and gain a deeper understanding of the work being done across different technical areas. Specifically, we’re interested in how performers in TA3 will shape the requirements for the multimodal dataset of rare disease patients in TA2. Additionally, we’re excited to connect with organizations advancing innovation in rare disease diagnostics. | TA2: Novel Diagnostic Indicators and Population-Scale Discovery |
| Sid Chakravarthy | Vako | sid@vako.ai | Chantilly, VA | Vako focuses on AI-powered solutions for rare disease communities, developing platforms that provide real-time, personalized support for patients and caregivers. Our research includes integrating patient advocacy group knowledge into scalable AI systems, enabling multilingual support, and creating disease-specific datasets for healthcare innovation. | We are seeking partners with expertise in large-scale data collection, AI/ML diagnostic development, and rare disease research. Ideal collaborators include organizations with access to rare disease datasets, clinical trial networks, and patient advocacy networks to co-develop scalable platforms that enhance diagnosis and patient engagement. | TA3: Sustainable Platform for AI Diagnostic Development |
| Bruce Wang | University of California San Francisco | bruce.wang@ucsf.edu | San Francisco, CA | Clinical and basic research on the porphyrias. Including longitudinal natural history study of over 1000 porphyria patients in the United States. We have recently completed a pilot study using machine learning algorithm to improve diagnosis of acute hepatic porphyrias. | We are looking for partners to help us improve our ability to extract relevant clinical information for our existing cohort of confirmed porphyria patients. We are also looking for partners to work with us to improve diagnostic accuracy for the porphyrias. | TA2: Novel Diagnostic Indicators and Population-Scale Discovery |
| Cong Trinh | University of Tennessee Knoxville | ctrinh@utk.edu | Knoxville, TN | Analysis and modeling of cellular metabolism, omics integration, AI/ML, graph theory, optimization, cellular robustness, and stress responses. | Complementary expertise. | TA2: Novel Diagnostic Indicators and Population-Scale Discovery |
| Matt Wolfe | Virginia Tech Applied Research Corporation | matt.wolfe@vt-arc.org | Arlington, VA | Virginia Tech Applied Research Corporation’s research focus areas are engineering, test and evaluation of wireless communication and sensing systems; enhancing human/machine decision making through applied information and data sciences; and accelerating discovery with AI/ML capability development. Our success is grounded in forming highly collaborative, trusted partnerships across government, industry, and academia and passion for achieving enduring and highly contextual impact. | Our team is interested in partnering with organizations with access to rare disease patients’ demographic, diagnostic, prognosis and outcome datasets to support model development, training, and testing/validation. We also seek partners with access to patients for additional clinical data collection to support model testing and refinement and facilitate transition of technologies to the clinical setting. | TA3: Sustainable Platform for AI Diagnostic Development |
| Yiwei She | TNPO2 Foundation | yiwei@tnpo2.org | New York, NY | Identifying neonatal rare disease patients (via rWGS) sustainably at scale and connecting them with a therapeutic modality most likely to help in the shortest time possible. Continuously monitoring published literature for, as well as producing novel research to identify pathogenic alleles from public data; quickly open sourcing consented data to benefit as many patients as possible. | Compliant (HIPAA and other regulations) cloud infrastructure to build applications on top of them. | TA2: Novel Diagnostic Indicators and Population-Scale Discovery |
| Kyle Retterer | Geisinger | kretterer1@geisinger.edu | Danville, PA | Geisinger is interested in genomics-first approaches to population health, including improving diagnosis and care for under and misdiagnosed patients with rare genetic disease. Our MyCode program has enrolled over 350k participants and generated exome sequencing data on 230k. We are actively interested in deep phenotyping of these participants both retrospectively and prospectively. These data will enable genomics-first, population-level characterization of disease phenotypes and penetrance. | We are interested in working with partners who can help us extract deep phenotypes from unstructured higher dimensional data. We are also interested in combining and sharing phenotypic data with other healthcare population cohorts. | TA1: Massive-Scale Rare Disease Dataset |
| Richard Liang | Graphen Drugomics | richardliang@drugomics.graphen.ai | New York, NY | Graphen Drugomics focuses on pioneering research in drug discovery and precision medicine through cutting-edge multi-omics and AI-driven approaches. Key areas include target identification using transcriptomics and proteomics, small molecule drug development, biomarker discovery, and leveraging machine learning for patient stratification and treatment optimization. We aim to advance therapeutic efficacy and accelerate breakthroughs in oncology, neurodegenerative diseases, and rare disorders. | Graphen Drugomics seeks teaming partners with expertise in complementary areas, such as high-throughput screening. Ideal partners share a commitment to innovation and have a track record in preclinical or clinical development, biomarker validation, or data integration. We value collaborators who bring unique capabilities, resources, or insights to accelerate the development of precision therapeutics and transformative solutions. | TA2: Novel Diagnostic Indicators and Population-Scale Discovery |
| Stefanie Eichner | FDNA Inc. | stefanie@fdna.com | Atlanta, GA | FDNA's research focus today centers on leveraging AI-powered phenotypic analysis to enable early detection of rare genetic diseases. This includes integrating additional modalities like voice biomarkers & video analysis (movement and gait) to reduce the diagnostic odyssey and broaden insights into patient phenotypes. By expanding beyond traditional methods, FDNA supports clinicians in identifying complex conditions earlier and more effectively. | FDNA seeks teaming partners with complementary expertise in rare disease diagnosis, including clinical institutions, AI technology developers, and data-rich organizations. Ideal partners provide diverse datasets, support advanced modalities (voice, video), and enhance diagnostic accuracy. FDNA values collaborators with regulatory, commercialization, or patient advocacy expertise to scale patient-centered, AI-driven solutions for transformative healthcare. | TA2: Novel Diagnostic Indicators and Population-Scale Discovery |
| Geoffrey Siwo | University of Michigan Medical School | siwog@med.umich.edu | Ann Arbor, MI | We are developing innovative computational frameworks that leverage advances in generative artificial intelligence (e.g. LLMs) to enable the integration of fundamental biological knowledge, genetic data and patient derived phenotype descriptions. We are seeking to apply these approaches in the development of scalable and more accessible approaches for rare disease diagnosis, drug repurposing and discovery of causal genes and therapeutics. | We are interested in partnering with rare disease registries, patient foundations and AI companies. | TA2: Novel Diagnostic Indicators and Population-Scale Discovery |
| Joshua Resnikoff | TMA Precision Health | joshua@tmaprecisionhealth.com | Boston, MA | TMA is a tech-enabled end-to-end integrated service that identifies rare disease patients from Payer data and delivers genome-based treatment insights. | Organizations that have sequencing and interpretation assets to increase the speed of delivery, and organizations that are excellent at organizing and analyzing massive datasets in order to build more robust genome::phenome models, biomarker identification, etc. | TA2: Novel Diagnostic Indicators and Population-Scale Discovery |
| Charlene Son Rigby | Global Genes | charlene.sonrigby@globalgenes.org | San Francisco, CA | RARE-X is the research arm of the nonprofit patient advocacy organization Global Genes. RARE-X provides a scalable and standardized model for longitudinal rare disease data collection. Launched in 2021, RARE-X's global footprint includes more than 75 disorders from patients in over 90 countries, collected in partnership with 122 patient advocacy organizations. | We are open to partnering with healthcare organizations, researchers, and other innovative collaborators. We are seeking partners with expertise in rare disease AI application development. | TA1: Massive-Scale Rare Disease Dataset |
| Heath Naquin | Science Center | hnaquin@sciencecenter.org | Philadelphia, PA | Commercialization Support and Consortia Management for Life Sciences based solutions | Technical collaborators and actors along with industry partners | TA3: Sustainable Platform for AI Diagnostic Development |
| Peter Washington | University of California, San Francisco | peter.washington@ucsf.edu | San Francisco, CA | Dr. Peter Washington is an assistant professor at UCSF's new Division of Clinical Informatics and Digital Transformation leader a research program in consumer digital health informatics. Dr. Washington's NIH and NSF-funded research involves analyzing data streams from devices such as smartphones, websites, and custom websites/apps to develop scalable, accessible, and performant digital diagnostics. | As an assistant professor at UCSF's new Division of Clinical Informatics and Digital Transformation and an established researcher in developing human-centered AI/ML models from consumer digital health data streams (e.g., wearables, smartphones, web data, etc), I am interested in partnering with clinical/domain experts for a rare disease that scalable and accessible digital devices might be able to identify (using computer vision applied to a smartphone camera, analyzing Samsung sensors, etc). | TA3: Sustainable Platform for AI Diagnostic Development |
| KEVIN BUSTO | Vega Technology Group LLC | kbusto@vegatcgroup.com | North Canton, OH | Digital Imaging systems from 190nm to 14um of the light spectrum. Advanced embedded diagnostic system development, design, proof of concept, prototype to manufacturing. Embedded/Edge computing with AI/ML. | Partners with capabilities in data sets, software development. CM's with experience and certification in Medical Device design and manufacturing. | TA3: Sustainable Platform for AI Diagnostic Development, TA2: Novel Diagnostic Indicators and Population-Scale Discovery, TA1: Massive-Scale Rare Disease Dataset |
| Melissa Haendel | University of North Carolina Chapel Hill | melissa@tislab.org | Chapel Hill, NC | We focus on semantic interoperability, data harmonization, and AI/ML in various data models and knowledge graphs for rare disease discovery. We co-founded the Monarch Initiative, which has created the Exomiser variant prioritization tool, and the Human Phenotype and Mondo disease ontologies. We also co-founded the National Covid Cohort Collaborative, the largest publically available HIPAA-limited dataset in the US, and lead the All of Us Center for Linkage and Acquisition of Data. | We are interested in partnering with rare disease registries, PAGs, pharma, and other technology partners. | TA3: Sustainable Platform for AI Diagnostic Development, TA1: Massive-Scale Rare Disease Dataset, TA2: Novel Diagnostic Indicators and Population-Scale Discovery |
| Tyona Pike | F&I Foresight Initiatives | Pikesforesightinitiatives@gmail.com | Glen Burnie, MD | Our focus includes supporting patients, caregivers, families, and medical teams by addressing unmet needs, enhancing awareness, and fostering partnerships to advance diagnosis, treatment, and care. We aim to work with organizations to reduce diagnostic delays, improve patient outcomes, and develop patient-centric approaches that prioritize the unique challenges posed by rare diseases | Seeking a partner to handle the technical and research aspects of our efforts in the rare disease space. While we focus on growing and maintaining support for patients, caregivers, friends and family, medical teams, and those aiding individuals along the diagnostic odyssey, we aim to collaborate with an organization that can enhance our research capabilities and drive innovative solutions. Together, we can create a comprehensive support system that addresses the unique challenges faced. | TA3: Sustainable Platform for AI Diagnostic Development |
| Kemar Green | Johns Hopkins University School of Medicine | kgreen66@jhmi.edu | Baltimore, MD | Our research focuses on developing an autonomous human-AI interface using AR/VR systems for remote screening and triaging of rare neurologic diseases. Our platform integrates real-time eye movement neurophysiologic data with multimodal inputs (clinical notes, imaging, multi-omics, neurophysiology) and leverages LLMs to determine disease status. This innovative approach aims to revolutionize diagnostics by enabling precise, scalable, and remote assessments for complex neurologic conditions. | We seek partners specializing in AR/VR technology development, LLM integration, and rare neurological disease advocacy to enhance our platform's capabilities. Additional needs include expertise in EMR systems, advanced eye tracking technologies, and collaborations with foundations focused on rare neurologic diseases. Together, we aim to build an autonomous human-AI interface for remote screening, triaging, and diagnostics of rare and complex neurologic conditions. | TA3: Sustainable Platform for AI Diagnostic Development, TA1: Massive-Scale Rare Disease Dataset, TA2: Novel Diagnostic Indicators and Population-Scale Discovery |
| Valentin Normand | Ambr Institute | valentin@ambr.no | Minneapolis, MN | The Ambr Institute focuses on developing software as medical devices for personalized diagnostics and preventive medicine. One of their current research focuses on creating a SaMD for diagnosing and treating Neuroendocrine Carcinoma (NEC), a rare and aggressive cancer. The project aims to integrate diverse data sources, including genetic, epigenetic, imaging, and liquid biopsy data, to improve NEC diagnosis accuracy, identify tumour subtypes, and guide personalized treatment decisions. | The project could benefit from: 1) data providers; due to the small proportion of patients, data are rare. 2) Patient advocacy groups could also contribute to helping the project. 3) Funding partners, both via investors and donations to our academic partners. 4) Other diagnostics companies interested in contributing to significantly improve diagnostics of such aggressive cancers. | TA3: Sustainable Platform for AI Diagnostic Development, TA2: Novel Diagnostic Indicators and Population-Scale Discovery |
| Daniel Hwang | Databricks | daniel.hwang@databricks.com | McLean, VA | Databricks offers a unified platform that aligns with ARPA-H's RAPID program goals. Its data intelligence platform, powered by generative AI and built on a lakehouse architecture, can accelerate rare disease diagnosis by enabling efficient processing of large-scale patient data. Databricks' expertise in data integration, AI model development, and scalable analytics solutions makes it an ideal partner for building the comprehensive rare disease dataset and AI based detection model. | Databricks seeks teaming partners with complementary expertise in data integration, AI model development, and scalable analytics solutions aligned with our lakehouse architecture. Ideal partner should possess strong industry knowledge, particularly in healthcare and rare disease research, and demonstrate innovation in developing high impact AI solutions. Collaboration includes technology integration, joint solution development, and go to market synergies to address complex challenges. | TA3: Sustainable Platform for AI Diagnostic Development, TA1: Massive-Scale Rare Disease Dataset |
| NATALIA LUCHKINA | EverythingALS | natalia@everythingals.org | Palo Alto, CA | EverythingALS, a data-driven nonprofit, aims to eliminate barriers in rare disease research and diagnostics, starting with ALS. We conduct citizen-driven, remote studies with ALS patients and gene carriers using digital tech and sensors, expanding into clinical care. Supporting 7,000+ patients, caregivers, and pharma consortia, we provide data insights and develop models to identify novel digital biomarkers. Through Sensor Lab, we validate devices to enhance diagnostics and care innovation. | We can provide ALS multimodal data, access to patients and ALS clinicians, statistical models, and digital biomarkers. Ideal collaborators include rare disease experts to extend our playbook beyond ALS, tech innovators to enhance scalability and interoperability, biomarker specialists, and others who can help us scale and expand into other neurodegenerative and rare diseases. | TA2: Novel Diagnostic Indicators and Population-Scale Discovery, TA1: Massive-Scale Rare Disease Dataset, TA3: Sustainable Platform for AI Diagnostic Development |
| David Connor | Rare Disease Data Trust | dconnor@finding-rare.com | NASHVILLE, TN, TN | We have developed (3) search models for PNH, DEB & hATTR with >90% CI, we have a live clinical program currently in DEB, against 4.2m patient lives, we anticipate discovery of a sample of suspect isolates that our data partner will test, confirm & diagnose, for definitive patient centric outcomes of patient rescued from diagnostic odyssey and accelerated to appropriate care. Disease state model development and undiagnosed DEB patients is our current focus. | Our business model permits compliant sponsorship of both the development and deployment of disease state specific rare/ultra rare disease state search models into/against large patient consented, provider connected and clinically actionable healthcare data sets, to further refine the accuracy of the search model, with a derivative of rescued lost and undiagnosed rare disease patients. The government could both sponsor the development of a focused set of models and sponsor their deployment. | TA2: Novel Diagnostic Indicators and Population-Scale Discovery, TA3: Sustainable Platform for AI Diagnostic Development, TA1: Massive-Scale Rare Disease Dataset |
| Alison Sizer | Growth Impact LLC | alison@growthimpact.co | Midlothian, VA | Gender inequities in rare disease, new venture development across stages of idea, incubation and launch and testing desirability, feasibility and viability. Expertise in market research, design thinking / innovation, business model development, go-to-market strategy and partnerships. Rare disease patient (HPP) who experienced the long delay and journey to diagnosis. | Seeking to partner with organizations (patient advocacy groups, digital health / technology companies, research and other) to collaborate on the gender inequities in rare disease and/or whom could leverage my strategic business and innovation expertise in incubation and bringing innovations to market. | TA2: Novel Diagnostic Indicators and Population-Scale Discovery, TA1: Massive-Scale Rare Disease Dataset, TA3: Sustainable Platform for AI Diagnostic Development |
| Jimmy Lin | Rare Genomics Institute | jimmy.lin@raregenomics.org | San Mateo, CA, CA | Genome sequencing for rare diseases | Software engineers, bioinformatics, and AI/ML expertise to understand medical records to map to genomics information | TA2: Novel Diagnostic Indicators and Population-Scale Discovery |
| Silviu-Alin Bacanu | Virginia Commonwealth University | sabacanu@vcu.edu | Richmond, VA | Our team main strength is large scale methods for statistical genetics for common (Dr. Bacanu is co-director of the analytic group in PGC Anxiety), rare (Drs. Nguyen, Riley and Bacanu) and combined common and rare variants (Drs. Nguyen and Bacanu). Dr. Bacanu is a statistical geneticist and a mathematical statistician, Dr. Hoang is a rare variant statistical geneticist and a mathematician and Dr. Riley is a molecular geneticist. Our secondary strength consist of AI methodology (Dr. Ghosh). | The primary asset of potential collaborators consists of access to massive scale rare diseases data set. Secondary assets are wet lab validation capabilities and AI methodological prowess for genetics data (with a special emphasis on adjusting for genetic and other confounders). | TA3: Sustainable Platform for AI Diagnostic Development |
| Jessica Chong | University of Washington | jxchong@uw.edu | Seattle, WA | rare disease gene discovery, clinical implementation of genetic testing, variant interpretation | . | TA2: Novel Diagnostic Indicators and Population-Scale Discovery |
| Vivek Rudrapatna | UCSF | vivical@gmail.com | NA, CA | We have recently developed an algorithm that can identify undiagnosed patients suffering from acute hepatic porphyria, a family of rare heritable metabolic disorders of heme biosynthesis. Said algorithm makes predictions using EHR data, and utilizes signals from knowledge graphs to enhance predictive accuracy in the face of small numbers of cases. We can contribute expertise to TA3. | Teams that can assemble disconnected clinical data across datasets (TA1), can contribute clinical data (TA1), and can deploy tools to collect data from patients (TA2) | TA3: Sustainable Platform for AI Diagnostic Development |
| Alain Cappeluti | OTraces Inc | acappeluti@otraces.com | Sykesville, MD | OTraces has developed our AI/ML diagnostic platform utilizing the physics of signal processing to reduce the background noise that reduces the ability to find an accurate biological signal that describes the presence of disease. This is combined with a system process to select a set of orthogonal biomarkers with robust signals. The selected biomarkers are used to train our algorithms using meta variables such as age. We then use multivariant spacial proximity analysis to score unknowns. | Capability to sort large data sets in search for relevant biomarker information. Capability to process patient samples to identify possible protein biomarkers for the disease of interest. | TA2: Novel Diagnostic Indicators and Population-Scale Discovery |
| Timothy Chou | BevelCloud | tim@bevelcloud.io | Palo Alto, CA | Building trustworthy, privacy-preserving AI applications requires access to large amounts of diverse data. The current centralized approach of moving data to train the AI application will NOT work instead we are building a global, privacy-preserving, real-time, distributed AI cloud infrastructure to move the AI application to the data in the clinic, hospital or research building. | We are focused on the infrastucture - interested in teaming with application providers | TA1: Massive-Scale Rare Disease Dataset |
| Chialin Wei | University of Washington | weicl@uw.edu | Seattle, WA | Our team, UW Center for Rare Disease Research, has developed critical analytic tools and methodological innovations on generating, curating, integrating, analyzing and sharing multi-scale datasets, including phenotype, epigenetics, chromatin and phenotype data, for over 1,500 rare diseases. Our platform enables the robust gene discovery for unsolved cases and functional interpretation of causal variants, both are critical to accelerate the rare diseases diagnostics at population scale. | We are looking for partners specialized in 1. patient collections to expand our current rare diseases cohorts, 2. data scientists in building new algorithms for integration and detection tools. | TA1: Massive-Scale Rare Disease Dataset |
| Rustem Ismagilov | California Institute of Technology | rustem.admin@caltech.edu | Pasadena, CA | We have been focused on building technologies for massive sample and data collection, analysis, and harmonization. | We are looking for people with existing boots-on-the-ground groups/networks to enable sample collection from relevant patient populations. | TA3: Sustainable Platform for AI Diagnostic Development |
| Joseph Cohn | Soar Technology LLC | joseph.cohn@soartech.com | Ann Arbor, MI | Soar Technology creates AI solutions that enhance human information processing and understanding. Our research spans data curation, structuring, analysis, visualization, and dissemination, rooted in our human cognition and human-system interaction expertise. Initially focused on DoD, we now adapt our capabilities, along with expertise in explainable, trustworthy AI/ML, to solve healthcare challenges in logistics, predictive analytics, and operational efficiency for scalable, adaptive systems. | Rare Disease Expertise: Proficiency curating datasets tailored for rare diseases, including knowledge of disease phenotyping, biomarker discovery, and clinical trial data; Validation in Clinical Settings: Experience deploying diagnostic models in diverse clinical environments and conducting validation studies to ensure accuracy and usability; Regulatory Compliance: Experience with regulatory pathways and requirements, ensuring solutions meet standards for healthcare use and deployment. | TA3: Sustainable Platform for AI Diagnostic Development |
| Mahwash Kassi | Houston Methodist | mkassi@houstonmethodist.org | Houston, TX | Multi-omics in cardiac sarcoidosis | AI/ML | TA3: Sustainable Platform for AI Diagnostic Development |
| Paul Schmidt | Alexion Rare Disease | Pschmidt.ventures@gmail.com | Boston, MA | Rare Disease | Data driven diagnostics | TA1: Massive-Scale Rare Disease Dataset |
| Arezoo Movaghar | Wake Forest University-School of Medicine | amovagha@wakehealth.edu | Winston-Salem, NC | Wake Forest University, School of Medicine is part of Advocate Health Enterprise serving over 12 million patients across 6 State. Our team has access to comprehensive longitudinal data from this population. We have created multiple AI- screening platform for early identification of patients with rare disease. These methodologies have been successfully tested on multiple independent health systems. | We are open to collaborate in merging datasets to expand to a larger population. | TA1: Massive-Scale Rare Disease Dataset |
| robert davis | University of Tennessee | rdavis88@uthsc.edu | Memphis, TN | The Genomics Information Commons (GIC) is a consortia of Boston Children's Hospital, Cincinnati Children's Hospital, Children's Hospital of Philadelphia, Washington University, University of Pittsburg, Riley Hospital for Children, Rady's Children's Hospital, and Le Bonheur Children's Hospital to elucidate genetic variation in human disease with a large database of highly phenotyped genomic sequences. | We seek to increase size and diversity to improve power and representativeness. We also seek partners to expand acquisition of diverse datatypes from the population to help next-generation phenotyping efforts. | TA2: Novel Diagnostic Indicators and Population-Scale Discovery |
| Eric Luellen | Turing Biosciences | Eric.Luellen16@gmail.com | Boston, MA | Turing Biosciences focuses on developing AI-ML-driven platforms for accelerating the diagnosis of rare diseases. By integrating functional metabolomics, genomics, and multi-omics data, Turing identifies biomarkers for scalable, non-invasive diagnostics. Our approach leverages DARPA-inspired methodologies to build robust datasets and deploy cost-effective, direct-to-patient tools that democratize access to precision diagnostics, enabling faster and more equitable healthcare solutions. | Turing seeks partners with expertise in patient recruitment and multi-omics data processing. Ideal collaborators include research institutions for advanced metabolomics, advocacy groups to engage diverse rare-disease communities, and CROs for regulatory compliance and high-throughput sample analysis. We value partners who share our vision for democratizing diagnostics and can enhance scalability and patient accessibility to advance rare-disease diagnostics. | TA3: Sustainable Platform for AI Diagnostic Development |
| Adam Bly | System Inc. | adam@system.com | New York, NY | We are building the first systems model of the world, leveraging large language models, advanced graph technology, complexity science, and causal inference techniques. Starting in health, we have assembled a large-scale graph based on statistical and mechanistic findings extracted daily from literature and expert-curated databases. Through our APIs, we are building knowledge infrastructure to support systems-based modeling, application development, and research. | We seek to partner with subject matter experts in rare disease biology and medicine to inform the development of a solution built on our platform. | TA2: Novel Diagnostic Indicators and Population-Scale Discovery |
| Timothy Chou | BevelCloud | tim@bevelcloud.io | Palo Alto, CA | Building trustworthy, privacy-preserving AI applications requires access to large quantities of diverse data. The current centralized approach of moving training data to the application will NOT work, instead we have engineered a privacy-preserving, real-time, distributed AI cloud infrastructure to move the AI application to the data in the clinic, hospital or research lab. | Interested in teaming with rare disease AI application developers | TA3: Sustainable Platform for AI Diagnostic Development, TA1: Massive-Scale Rare Disease Dataset, TA2: Novel Diagnostic Indicators and Population-Scale Discovery |
| Suzanne Rae | Perimeter Health | info@perimeter.health | Tampa, FL | Perimeter Health focuses on AI-driven monitoring of Variants of Uncertain Significance (VUS), creating a two-way marketplace for genomic data sharing, and developing scalable, sustainable platforms for long-term variant tracking. We emphasize ethical data sharing, innovative storage solutions via API integrations, and addressing user needs on both data input and output sides. Long-term, we aim to translate these capabilities into space medicine applications. | We are seeking partners who bring expertise in genomic data aggregation, AI-driven diagnostics, and scalable platform development. Ideal partners include institutions with rare disease datasets, innovative diagnostic research capabilities, software engineers, and/or experience in creating secure, sustainable data-sharing frameworks. Collaboration in AI/ML algorithm development, ethical data use, and integration into healthcare workflows is key to advancing our mission. | TA3: Sustainable Platform for AI Diagnostic Development |
| Dipanjan Pan | The Pennsylvania State University | dipanjan@psu.edu | State College, PA | Pan Lab at Penn State brings extensive experience in point of care molecular biosensing of various diseases. The team brings track record in high accuracy nucleic acid and protein based biosensing without the need of conventional PCR based techniques. | We are looking to partner with companies with AI/ML expertise to utilize our sensing data for rare disease applications. | TA2: Novel Diagnostic Indicators and Population-Scale Discovery |
| Katharina Schmolly | zebraMD | kschmolly@gmail.com | Lebanon, NH | We are a group of physicians and scientists developing an end to end solution for the real world implementation of a tool that can utilize rare disease algorithms at the point of care and enable a large, self updating and self learning real world data platform of rare disease patients. Our data sources are real patients with as much multifaceted data as possible, not just clean, scrubbed research data. | We are looking to partner with a TA1 organization to increase the amount of data available for this massive platform and a TA2 organization who would like to use our E2E integration ability and aggregated real world data to run existing algorithms and develop new ones. | TA3: Sustainable Platform for AI Diagnostic Development, TA1: Massive-Scale Rare Disease Dataset, TA2: Novel Diagnostic Indicators and Population-Scale Discovery |
| Kasie Bailey | Truveta | kasieb@truveta.com | Bellevue, WA | Truveta data includes inpatient and outpatient care from over 900 hospitals and 20,000 clinics and over 120 million de-identified patient records. Our data also includes clinical notes and radiological imaging. Our data covers an average of 5 years of patient's history. Also, important to note, Truveta data has over 1 million mother-baby linkages. Our data is representative of the US census. | prime, AI modelers, and rare disease partners | TA1: Massive-Scale Rare Disease Dataset, TA2: Novel Diagnostic Indicators and Population-Scale Discovery, TA3: Sustainable Platform for AI Diagnostic Development |
| Patrick Short | Sano Genetics | patrick@sanogenetics.com | Boston, MA | Sano Genetics' mission is to accelerate the world's transition to precision medicine. Our platform combines direct-to-patient and healthcare embedded digital phenotyping, genetic testing, counselling, and long-term engagement to support rare disease patient finding. We have supported 30+ programs internationally. The platform is therapy-area agnostic, and covers wide demographics including hearing and visual impairments, caregiver supported research including pediatrics. | Our strength in this competition is in TA2, and contributing to scaled data collection in TA1 (genetics, EHR, digital phenotyping). We would be very excited to partner with collaborative organizations with strength in TA1 and TA3 to put together a solution that can bring an end to the diagnostic odyssey for the tens of millions of rare disease patients in the US through this competition, and provide a blueprint that could be scaled worldwide to help hundreds of millions more. | TA2: Novel Diagnostic Indicators and Population-Scale Discovery, TA1: Massive-Scale Rare Disease Dataset |
| Brian Dennis | Netrias LLC | bdennis@netrias.com | Washington, DC | Netrias is advancing data-intensive biomedical research with its AI-driven metadata harmonizer, a cutting-edge solution leveraging fine-tuned large language models to automate metadata curation, wrangling, and standardization. We are also expanding into AI based mechanisms for accelerating biomedical domain specific data engineering and ML operations practices. Our approach brings together concepts from AI-assisted coding, data workflow orchestration, and ML evaluation frameworks. | Netrias is looking for teaming partners with complementary rare disease domain expertise to address technical gaps and tackle broader challenges in biomedical data harmonization that can revolutionize continuous aggregation and curation of patient data. We are also interested in working with organizations that have foundational technologies to support innovation in building biomedical data commons and model evaluation frameworks. | TA3: Sustainable Platform for AI Diagnostic Development, TA1: Massive-Scale Rare Disease Dataset |
| Mai-Lan Ho | MU | mai-lanho@health.missouri.edu | Columbia, MO | Multimodal imaging, genomics, and molecular characterization of rare and orphan diseases Precision theranostics technologies for targeted diagnosis and therapy Artificial intelligence analysis of population datasets with multimodal data linkage Multi-omic approaches to bioinformatics, health informatics, and geoinformatics | Strategies for diverse multimodal data integration, harmonization, synthesis, bias reduction | TA1: Massive-Scale Rare Disease Dataset |
| Chathuri Daluwatte | Alexion AstraZeneca Rare Disease | chathuri.daluwatte@alexion.com | Boston, MA | Conversational diagnostic AI, AI Software as a Medical Device deployment paradigms in cloud and edge (business models and technology paradigms, AI Software as a Medical Devices and AI care coordination solutions for diagnosis and disease management in cardiology, rare oncology, genomic medicine, neurology, bone metabolism and ophthalmology | Global footprint Platform Engineering approach SaMD that address compliance and regulatory within workflow Edge AI | TA3: Sustainable Platform for AI Diagnostic Development |
| Marcine Snyder | IQVIA | marcine.snyder@iqvia.com | IQVIA is a global company. Our U.S. Government Solutions team is based in Falls Church, VA., MD | IQVIA is a global leader in health data, technology, and analytics services, supporting the life sciences industry, health systems, and governments. Our comprehensive solutions encompass sourcing diverse real-world health data, advanced data management, analysis platforms, artificial intelligence, and clinical decision support tools for healthcare applications. Our award-winning AI technologies include regulatory, clinical research, and healthcare applications. | IQVIA is open to partnering with healthcare organizations, academic researchers, data partners, and other organizations to develop innovative solutions. | TA1: Massive-Scale Rare Disease Dataset, TA2: Novel Diagnostic Indicators and Population-Scale Discovery, TA3: Sustainable Platform for AI Diagnostic Development |
| Anjun Chen | ELHS Institute Inc. | aj@elhsi.org | Palo Alto, CA | The ELHS Institute pioneers the development of low-cost, equitable Learning Health System (ELHS) units for every clinical team by providing free, pre-clinically validated, fine-tuned open-source LLMs for the screening and early detection of a broad range of diseases. Our novel GenAI-LHS solution enables the creation of highly efficient, distributed clinical research networks, designed to shorten the diagnostic journey for patients with rare diseases (initial results published in Nature). | We are seeking multiple clinical teams in hospitals and community clinics as partners to demonstrate GenAI-enabled clinical research networks for identifying patients with rare diseases during routine clinical care. In collaboration with Stanford University professors, we will deploy and operate high-accuracy LLMs in doctor-controlled environments to ensure the responsible use of AI, provide research training, and support doctors in publishing innovative GenAI studies in top-tier journals. | TA2: Novel Diagnostic Indicators and Population-Scale Discovery |
| Emma Wyllie | Datavant | EmmaWyllie@datavant.com | Phoenix, AZ | Datavant is a leader in privacy-preserving data exchange, connecting health data for 500+ institutions and 300 million patients, across 10 billion records. Their HIPAA-compliant solution combines with our ecosystem of data partners to support 800+ live connections. With advanced medical record retrieval and tokenization, Datavant enables the largest de-identified real-world data exchange, supporting everyone from pharmaceutical companies to large research institutions like the NIH. | We can facilitate data linkage and supplementary real-world data (RWD) inclusion as part of TA3. Performers in TA1 & TA2 would be ideal potential teamining partners, as well as those that can provide the platform capabilities required for TA3. | TA3: Sustainable Platform for AI Diagnostic Development |
| Natan Vidra | Anote | vidranatan@gmail.com | New York, NY | Anote focuses on data annotation, fine tuning large language models, and evaluation of LLMs. We built an end to end MLOps platform to compare the results across a variety of LLMs. | We are looking to team / partner with teammates that understand the healthcare domain extensively, and are looking for AI / ML solutions to complement their skillset. | TA2: Novel Diagnostic Indicators and Population-Scale Discovery |
| Natasha Shelby | Caltech | tshelby@caltech.edu | Pasadena, CA | We have been focused on building technologies for massive sample and data collection, analysis, and harmonization. | We are looking for people with existing boots-on-the-ground groups/networks to enable sample collection from relevant patient populations. | TA3: Sustainable Platform for AI Diagnostic Development |
| Kelly Klepinger | LabVantage | kklepinger@labvantage.com | Somerset, NJ | We are the global leader for COTS LIMS and ELN solutions for research and diagnostics (https://www.labvantage.com/labvantage-named-global-lims-company-of-the-year-by-frost-sullivan/). Our sister company - TCG Digital, is a leading Advanced Analytics solution provider whose product is fully integrated with LabVantage. Clients include NIH, CDC, Army (MRDC), many of the fortune 500 pharma and biotechs, CRO's and many others. Experienced using laboratory / research data and other data sets to | Complimentary skillsets and capabilities. This section will be updated | TA1: Massive-Scale Rare Disease Dataset |
| Robert Green | Mass General Brigham / Harvard Medical School | rcgreen@bwh.harvard.edu | Boston, MA | Our team includes experts from Broad Institute, Mass General Brigham, Ariadne Labs, Cedars-Sinai and several AI-based early stage technology companies focusing on rare disease variant curation, genomic screening, implementation science and AI-driven multi-modal data integration. We have established collaborations with a network of pediatric practice sites, including predominantly Black, Hispanic, and Indigenous communities, to advance equitable genomic implementation and improve health outcomes. | With extensive experience in clinical research and implementation along with leaders in AI-driven multi-modal technology, ideal collaborators will bring capabilities in advanced data integration, interoperability, and sustainable system design and scalability. | TA3: Sustainable Platform for AI Diagnostic Development |
| Kasie Bailey | Truveta | kasieb@truveta.com | Bellevue, WA | In the table it says we need to provide 8 exports of PPRL, but the language in some sections still says monthly. Truveta data includes inpatient and outpatient from 900 hospitals and over 20,000 clinics and over 120 million de identified patient records updated daily. Truveta data also has more than 5 billion clinical notes, nearly 100 million medical images, and billions of linked claims records. | rare disease, AI model developers, academia, and prime | TA2: Novel Diagnostic Indicators and Population-Scale Discovery |
| Elliot Polak | My XXY Chromodiversity Foundation | elliot@chromodiversity.com | Tempe, AZ | Chromodiversity Compass™ is a pioneering mobile health platform that integrates genetic insights with neurodevelopmental tracking to empower families and clinicians. Utilizing AI-driven analytics, it bridges critical gaps in care by enabling earlier interventions, reducing healthcare costs, and improving outcomes for individuals with genetic differences. Its scalable infrastructure supports large-scale screening initiatives, and its user-friendly design ensures widespread adoption. | We are looking for research partners conducting large scale newborn screening projects interested in tracking neurodevelopment in children, as well as investment & technology partners that can help scale and validate our platform. | TA2: Novel Diagnostic Indicators and Population-Scale Discovery |
| Ljubica Caldovic | Children's National Hospital | lcaldovic@childrensnational.org | 7144 13th Pl; Washington DC 20012; USA, DC | Diagnosing rare genetic disorders and functional validation of variants found in patients | Integration of experimental evidence for variant effect with medical records. Modeling inborn errors of metabolism in human organoids. | TA1: Massive-Scale Rare Disease Dataset |
| Raviv Itzhaky | Geneyx Genomex Ltd | raviv@geneyx.com | 11/B Galgale-Haplada St. Herzliyah, Israel 4672211 | scalable and rapid genetic analysis and annotation using multi-omics data integration and AI prediction for efficient molecular diagnosis and monitoring | Technological partnership to advance integration capabilities and AI based prediction optimization and clinical partners with well characterized cohorts patients with any NGS or Omics data. clinical partners with well characterized patient cohorts with any NGS or Omics data to expand our existing collaboration with The University of Iowa based Neuro-pediatric laboratory headed by Dr. Chandra Baharat | TA1: Massive-Scale Rare Disease Dataset |
| Eran Desheh | Ada Health | eran.desheh@ada.com | Berlin | Our industry-leading AI-powered clinical assessment platform enables earlier identification of patients with rare diseases. Research focus areas include significantly scaling up rare disease coverage, leveraging generative AI to effectively and efficiently capture additional patient data, deepening integrations into clinical workflows and combining Ada data with other data sources to validate models, optimize patient identification and accelerate the downstream journey to confirmed diagnosis. | We are looking for partners to help us connect Ada’s patient-entered data and assessment outcomes with upstream and downstream clinical data including clinical diagnoses, lab results and genetic testing, with the goal of enabling compliant, scalable, real-world validation and optimization of model performance across thousands of rare diseases. Also registries, partners interested in bidirectional data exchange and diagnostic platforms or services to whom Ada could send appropriate patients. | TA3: Sustainable Platform for AI Diagnostic Development |
| Dustin Baldridge | Washington University in St. Louis | dbaldri@wustl.edu | St. Louis, MO | Rare and ultra-rare disease molecular diagnosis and scalable infrastructure for collection of clinical and patient-derived data | Development and implementation of innovative at-home data collection devices. | TA1: Massive-Scale Rare Disease Dataset |
| Contact | Organization Name | Location | Description of Research Focus Area | Description of Teaming Partner | Technical Areas | |
| Brent Coffey | Accenture Federal Services | coffeybrent@gmail.com | Rockville, MD | Accenture brings AI and cloud integration capabilities to support rare disease data platforms, leveraging advanced analytics, machine learning (ML), and natural language processing (NLP) to unify and analyze complex datasets. Our expertise in integrating diverse data sources such as EHRs, genomics, clinical trial data, and patient registries supports seamless interoperability. Cloud-based solutions provide scalable, secure platforms for real-time collaboration. | Academic Medical Center, Research Institutes, and other rare disease specialist and data providers. | TA3: Sustainable Platform for AI Diagnostic Development |
| Arjun Bansal | Log10 | arjun@log10.io | San Diego, CA | Log10’s research focus has been on significantly reducing AI errors while learning continuously from expert feedback. Unlike traditional AI solutions that struggle with hallucinations and don't retain corrections, Log10's algorithms achieve accuracy comparable to fine-tuned models with minimal samples. For medical professionals currently spending several hours on diagnosis, Log10 has helped reduce this time to under an hour for a neuropsychiatric use case. | As experts in AI and with background in biology we are looking to partner with teams with access to data that can be leveraged to define rare disease cohorts as well as clinical expertise in the diagnostic process of rare disease. We are excited to extend and apply our AI algorithms to a diagnostic decision support system for rare diseases with dataset partners. | TA3: Sustainable Platform for AI Diagnostic Development |
| Ramona Walls | Critical Path Institute | rwalls@c-path.org | Tucson, Arizona, USA with subsidiary in Amsterdam, the Netherlands, AZ | We at the Critical Path Institute (C-Path) know individuals and families can't wait 10+ years for new treatments to be developed and approved. At C-Path, we don't make the drugs, but we accelerate their development and help get them to market faster. We provide the GPS for drug developers to avoid the traffic jams and obstacle, helping them be more successful and more efficient, and avoid problems that can delay lifesaving medicines from reaching people that need them. | C-Path has expertise and experience in data platform development, consortium formation and management, standards and ontology development and use, disease modeling, and regulatory science in common and rare disease. We are looking to partner with similarly driven organizations that want to create innovation solutions that leverage ALL the data to drive transformation in detection, diagnosis, and treatment of rare diseases. | TA1: Massive-Scale Rare Disease Dataset |
| Cornelius Boerkoel | Alamya Health | cornelius.boerkoel@alamyahealth.com | Clever, MO | Alamya Health is a public benefit corporation composed of mission-driven physicians and scientists focused on integration of multi-modal data to end the diagnostic odyssey for individuals with rare disease. Our technologies reduce the costs and time to diagnosis by algorithmically combining data types (genome, epigenome, transcriptome, phenomena) and are intended to be readily and sustainably deployable in non-traditional settings. | We would like to partner with TA1 organizations with undiagnosed and diagnosed rare disease patients and clinical experts to advance and evaluate our multi-modal algorithms. We would like to partner with TA3 organizations that can assist with transparent benchmarking of diagnostic efficacy. Also, we would like to partner with patient and advocacy groups to help realize our diagnostic accessibility goals. | TA3: Sustainable Platform for AI Diagnostic Development |
| Bailey Farrow | University of Chicago | farrow@uchicago.edu | Chicago, IL | Data for the Common Good (D4CG) is dedicated to lowering barriers for researchers to access data on rare diseases. By building communities, creating highly-specialized interoperable data standards, and aggregating and curating large amounts of clinical data from people with rare diseases, D4CG aims to have long-standing international impact on rare disease detection, research, and outcomes. (I am Dr. Sam Volchenboum's program director.) | Data for the Common Good builds communities dedicated to rare disease data collection and dissemination. We would benefit from working with groups who have a similar mission, especially those who work on data standards, platform development, and creating novel methods for data extraction from existing systems. We want to find partners who work on multimodal data sets and are interested in creating tools to bring together disparate types of data into a platform for downstream analyses. | TA3: Sustainable Platform for AI Diagnostic Development |
| Julie McMurry | Monarch Initiative | julie@tislab.org | Chapel Hill, NC | We focus on semantic interoperability, data harmonization, and AI/ML in various data models and knowledge graphs for rare disease discovery. We co-founded the Monarch Initiative, which has created the Exomiser variant prioritization tool, and the Human Phenotype and Mondo disease ontologies. We also co-founded the National Covid Cohort Collaborative, the largest publicly available HIPAA-limited dataset in the US, and lead the All of Us Center for Linkage and Acquisition of Data. | We are interested in partnering with rare disease registries, PAGs, pharma, and other technology partners. | TA1: Massive-Scale Rare Disease Dataset |
| Eric Vilain | University of California Irvine | Evilain@uci.edu | Irvine, CA | As home to CTSA, GREGoR, & UDN grant sites, & a NORD Center of Excellence, the University of California Irvine is a leader in scaling & innovating methods for rare disease discovery, variant disambiguation, & data sharing. We have contributed to the solve of 100s of cases that were diagnosis-negative on WES. We have developed a novel informed consent chatbot, a dashboard for tracking physical & digital research assets, integrate long-read, PacBio hi-fi sequencing into our discovery pipelines. | We seek partners who collaborate transnationally & or have robust biobanks & networks of patients & families. We also seek partners in the multiomic space. Having partners in the clinical-diagnostic space would be a strong enhancement to our activities. We want to use AI for patient self-phenotyping & image analysis for objective phenotyping. With these traditional & innovative phenotypic datasets, we seek to mine them for alternative targets for gene discovery with AI tools. | TA1: Massive-Scale Rare Disease Dataset |
| Euan Ashley | Stanford University | euan@stanford.edu | Stanford, CA | Rare disease, genomics, artificial intelligence | large scale infrastructure expertise | TA2: Novel Diagnostic Indicators and Population-Scale Discovery |
| Jason Colquitt | Across Healthcare | jason@acrosshealthcare.com | Carrollton, GA | Across Healthcare's Matrix is a powerful SaaS platform designed to support rare disease communities by enabling secure and scalable data collection, management, and analysis. It serves as a comprehensive registry solution, natural history study platform, and resource for research collaboration. Compliant with regulations like GDPR, HIPAA, and 21 CFR Part 11, Matrix empowers over 100 rare disease organizations worldwide, fostering patient insights and advancing therapeutic development. | Across Healthcare and our Matrix platform focus on being the best data platform to accomplish the goals within TA1 and TA2. We aim to collaborate with disruptive innovators to accelerate and deliver on TA3 and Model Benchmarking as well as collaboration with other rare disease data sources to scale the data set beyond our initial 280 rare disorders. With over 25 years of real-world healthcare data expertise, we are excited to create impact through partnership on this project. | TA2: Novel Diagnostic Indicators and Population-Scale Discovery |
| Roya Khosravi-Far | InnoTech Precision Medicine, Inc. | roya@innotechprecisionmed.com | Greater Boston Area, MA | InnoTech Precision Medicine pioneers AI-enabled multiomics diagnostics, integrating microfluidics, biochemical multiplexing of different classes of biomarkers and machine learning for early, accurate and rapid disease detection. Our liquid biopsy-based platform simultaneously detects genotypic and phenotypic biomarkers, with AI-enhancing signal processing, risk stratification, and predictive analytics. | InnoTech partners with major medical centers for multiomics validation, Google Health, IBM Watson, and Tempus for AI-driven diagnostics, and Illumina, Roche, and Qiagen for assay development and commercialization. FDA, CMS, and CLIA support regulatory pathways. Funding from ARPAH, NSF, NIH, and VCs enables scaling. Public health groups like WHO and ACS drive adoption across oncology, infectious diseases, and chronic conditions. | TA2: Novel Diagnostic Indicators and Population-Scale Discovery |
| James Cummings | Consumer | cummings216@gmail.com | New York, NY | Patient & caregiver-centric expert, focusing on rare communities as the ideal setting to employ the heightened consumer behavior for access to the previously unanalyzed 60 zettabytes of collected health data. As the only custodian and constant provenance of the data, consumer behavior (empowered by 21st Cures and FHIR) is the only pipe to allowing LLMs to help the limited human bandwidth to unlock the massive data collection. This paradigm behavior is far more powerful than technology; | Looking for partners with experience with: 1. Severe rare congenital genetic research registries an patient advocacy groups. 2. Data piping, aggregation and harmonization 3. Bulk FHIR extract into foundational and specialty LLMs | TA1: Massive-Scale Rare Disease Dataset |
| Steven Charlap | SOAP health | scharlap@soap.health | Boca Raton, FL | Eliminating misdiagnosis and improving early disease detection with embodied conversational AI and AI/ML. | Supplement our efforts | TA2: Novel Diagnostic Indicators and Population-Scale Discovery |
| Elizabeth Estes | Open Source Imaging Consoritum | eestes@osicild.org | Saugatuck, MI | The Open Source Imaging Consortium (OSIC) | The Open Source Imaging Consortium (OSIC) is a global collaboration dedicated to making radical progress in diagnosing, treating, and understanding fibrosing lung diseases via AI and imaging biomarkers. OSIC hosts the world’s largest and most diverse comprehensive database of fibrosing lung - imaging and clinical data, with over 200 members contributing, using and learning together. OSIC is a 501c3, dedicated to sharing our learnings with the world. Our learnings will be shared with the world. | TA3: Sustainable Platform for AI Diagnostic Development |
| Farid Vij | Citizen Health | farid@citizen.health | San Francisco, CA | Citizen Health is an AI-powered platform that builds directly relationships with patients and aggregates medical records, genetic/genomic information, imaging studies and additional multi-modal data to characterize the longitudinal journey for each individual patient. These datasets provide critical insights into rare disease progression, clinical endpoints, treatment windows, and IND and Phase 1/2 filings, while serving as comparator arms and long-term follow up for trials advancing toward BLAs | We are keen to work with others who have expertise in areas including neurodevelopmental and neuromuscular conditions, cardiomyopathies, hematological and metabolic conditions, among others. We believe that the power of our data requires clinicians and researchers who deeply understand how this data can be used to inform disease understanding and clinical care, as well as innovators who can drive novel analytics on top of the data to enable endpoint selection, trial design and discovery. | TA2: Novel Diagnostic Indicators and Population-Scale Discovery |
| Lynn Brielmaier | ALS Problem Solvers | lynnbr2@att.net | Indianapolis, IN | Clinical trials and EAP programs for ALS, a rare disease | doers | TA2: Novel Diagnostic Indicators and Population-Scale Discovery |
| Steven Charlap | SOAP health | scharlap@soap.health | Boca Raton, Florida, FL | SOAP Health has developed and partially patented the first AI powered primary care provider (PCP) able to do what a PCP does other than a physical exam. It collects Subjective patient data and Objective medical record data, reconciles and does an Assessment of the data, and develops a care Plan | Anyone working on eliminating misdiagnoses and improving early disease detection. | TA3: Sustainable Platform for AI Diagnostic Development, TA2: Novel Diagnostic Indicators and Population-Scale Discovery, TA1: Massive-Scale Rare Disease Dataset |
| Mark Kiel | Genomenon | kiel@genomenon.com | Ann Arbor, MI | Genomenon specializes in rare disease data curation, AI/ML algorithm development, and genetic variant interpretation at scale across the entire genome. Our team of 100+ AI and curation experts has developed rapidly scalable frameworks to curate large-scale datasets including clinical data. With the entirety of the biomedical literature as a substrate, we have developed AI/ML techniques to automate the production of comprehensive variant and patient landscapes for every published rare disease. | Genomenon seeks partners with access to large-scale clinical datasets, experience in developing clinical diagnostic algorithms, and champions of improved rare disease diagnostics. Ideal partners include organizations with patient data in the form of EHR including extensive rare disease datasets that require indexing, annotation, curation, and interpretation. | TA1: Massive-Scale Rare Disease Dataset |
| Shaun Kahler | KBI Data | shaun@kbi-data.com | Knoxville, TN | KBI Data specializes in Python and SQL-based data analytics for healthcare applications. Our work primarily involves the ETL, data architecture, optimization, and visualization of healthcare data, as well as creating optimized data models for AI-based solutions. | We are looking for partners seeking data management and analytics expertise for healthcare applications. | TA3: Sustainable Platform for AI Diagnostic Development |
| Autri Dutta | Genomenon, Inc. | adutta@genomenon.com | Ann Arbor, MI | Genomenon specializes in rare disease data curation, AI/ML algorithm development, and genetic variant interpretation at scale across the entire genome. Our team of 100+ AI and curation experts has developed rapidly scalable frameworks to curate large-scale datasets, including clinical data. With the entirety of the biomedical literature as a substrate, we have developed AI/ML techniques to automate the production of comprehensive variant and patient landscapes for every published rare disease. | Genomenon seeks partners with access to large-scale clinical datasets, experience in developing clinical diagnostic algorithms, and champions of improved rare disease diagnostics. Ideal partners include organizations with patient data in the form of EHR, including extensive rare disease datasets that require indexing, annotation, curation, and interpretation. | TA3: Sustainable Platform for AI Diagnostic Development |
| Gabor Marth | University of Utah | gmarth@genetics.utah.edu | US, UT | Rare disease diagnostics as part of the national Undiagnosed Diseases Network. | TA1 partners. | TA3: Sustainable Platform for AI Diagnostic Development |
| F Sessions Cole | Undiagnosed Diseases Network/Harvard Medical School | fcole@wustl.edu | St. Louis, MO | The Undiagnosed Diseases Network has been solving medical mysteries through team science. | Other institutions and programs with experience and data from undiagnosed and rare disease patients; possibly sequencing vendors | TA3: Sustainable Platform for AI Diagnostic Development |
| Mark YANDELL | University of Utah | myandell@genetics.utah.edu | Salt Lake City, UT | AI tools for rare disease diagnosis. | Commercial and clinical partners | TA2: Novel Diagnostic Indicators and Population-Scale Discovery |
| Elizabeth Rountree | COMBINEDBrain | elizabeth.rountree@combinedbrain.org | Brentwood, TN | The Consortium for Outcome Measures and Biomarkers for Neurodevelopmental Disorders (COMBINEDBrain) is a non-profit organization representing 110 patient advocacy groups. We recently completed an innovative pilot study (Project FIND-OUT) which developed and tested an algorithm for identifying infants at risk of genetic neurodevelopmental disorders. We have developed a virtual platform for families to access whole genome sequencing from home/non-clinical setting. | We are interested in partners that have access to large datasets and AI/ML platforms so that we can expand our algorithm and virtual platform to include all rare diseases, not only neurodevelopmental disorders. | TA1: Massive-Scale Rare Disease Dataset |
| Mwisa Chisunka | John Snow Labs | mwisa@johnsnowlabs.com | Lewes, DE | We provide state-of-the-art software, models & data to help organizations put AI to good use. We are the industry's primary provider of healthcare-specific LLM & NLP models, providing peer-reviewed, state-of-the-art accuracy and broad industry adoption on solutions for information extraction, de-identification, linking & QA on multi-modal clinical data. We are the team behind the open-source Spark NLP (130+ million downloads, 100,000+ models) and LangTest (100+ test types for Responsible AI). | We specialize in AI-driven clinical document understanding, information extraction, and question answering to analyze unstructured longitudinal data, and noisy clinical data. For TA1, we seek health system partners with rare disease patient data to co-develop detection algorithms using Generative AI models. For TA3, we seek partners to populate our Patient Journeys platform with rare disease data, enabling interoperable data sharing and advancing diagnostic models. | TA3: Sustainable Platform for AI Diagnostic Development |
| ARTURO LOAIZA-BONILLA | Massive Bio, Inc. | arturolb@massivebio.com | New York, NY | Massive Bio leverages an AI-driven platform to match oncology and rare disease patients with clinical trials, integrating multi-modal data (patient-consented data, EHR, genomics, imaging) to generate real-world evidence and actionable insights. We focus on accelerating precision diagnostics, optimizing treatment pathways, and expanding access for underserved populations worldwide. | Massive Bio seeks collaborators with large-scale rare disease cohorts, robust data sources, or novel diagnostic tools to enhance our AI-driven trial matching and real-world analytics. We welcome advanced biomarker, digital health, or data architecture expertise to boost diagnostic accuracy and patient outcomes in rare diseases. We anticipate to become the patient-centric digital engine for such joint innovations. | TA1: Massive-Scale Rare Disease Dataset |
| Angelos Stavrou | Thinksense Inc | angelos@thinksense.com | Arlington, Virginia, VA | ThinkSense Inc. provides biosensing technologies leveraging smartphones, wearables, and IoT devices to establish and predict personalized levels of physical and cognitive wellness. In addition, we offer solutions for collecting and curating digital biomarkers measured passively and actively for predictive analytics. | We are looking for performers with access to clinical trial capabilities and/or access to clinical data and charts. | TA3: Sustainable Platform for AI Diagnostic Development |
| Eddy Agbo | Fyodor Biotechnologies | eddy.agbo@fyodorbio.com | Baltimore, MD | Fyodor Biotechnologies develops innovative diagnostic solutions for rare diseases like sickle cell disease (SCD) and infectious diseases impacting low-resource settings. We integrate genomics, proteomics, AI, and microfluidics to create rapid, accessible diagnostics. We also support virtual hospital initiatives to improve healthcare access in underserved regions. We aim to transform global health through impactful solutions for underserved populations. | We're looking to team with groups that are strong in AI and AR modeling. | TA2: Novel Diagnostic Indicators and Population-Scale Discovery |
| Shadi Farhangrazi | S. M. Discovery Holdings | farhangrazi@smdiscovery.com | We are a US company based in Colorado and registered in Delaware and also have offices in the UK, CO | Rare Diseases diagnostics and therapeutics and AI/ML development | We are looking for potential teaming partners who are interested in rare pediatric diseases or clinical partners with large databases. We work closely with our partners. This past year we established 8 international and US research collaborations and our AI/ML platform was shortlisted for an international award. | TA2: Novel Diagnostic Indicators and Population-Scale Discovery |
| Arfaan Rampersaud | Columbus Nanoworks | arfaan@columbusnanoworks.com | Columbus, OH | We were funded by the National Cancer Institute to work on inflammatory breast cancer (IBC), a rare cancer. We used fluorescent NV-center nanodiamonds and developed a robust test for quantum-based sensing of cancer biomarkers associated with IBC. We are experts in fluorescent NV-center nanodiamonds, bioconjugation, optics and cancer research. We have oncology collaborators and are developing a consortium of university-based IBC centers to help us screen biomarkers using an array format. | We expect to collect a large amount of data from array analysis and plan to use AI to help us analyze these complex results. We can take care of all the laboratory work and sampling. | TA2: Novel Diagnostic Indicators and Population-Scale Discovery |
| Cornelius Boerkoel | Alamya Health | cornelius.boerkoel@alamyahealth.com | Clever, MO | Alamya Health is a public benefit corporation composed of mission-driven physicians and scientists focused on integration of multi-modal data to end the diagnostic odyssey for individuals with rare disease. Our technologies reduce the costs and time to diagnosis by algorithmically combining data types (genome, epigenome, transcriptome, phenomena) and are intended to be readily and sustainably deployable in non-traditional settings. | We would like to partner with TA1 organizations with undiagnosed and diagnosed rare disease patients and clinical experts to advance and evaluate our multi-modal algorithms. We would like to partner with TA3 organizations that can assist with transparent benchmarking of diagnostic efficacy. Also, we would like to partner with patient and advocacy groups to help realize our diagnostic accessibility goals. | TA3: Sustainable Platform for AI Diagnostic Development |
| Ishanu Chattopadhyay | University of Kentucky | ishanu_ch@uky.edu | Lexington, KY | Our research focuses on developing AI/ML frameworks for scalable rare disease diagnostics, leveraging electronic health records (EHR), synthetic patient cohorts, and multimodal data. Key areas include: (1) novel diagnostic indicator discovery using the ZCoR platform, (2) high-fidelity digital twins via Large Health Models (LHMs) for patient trajectory modeling, and (3) interoperable, explainable AI platforms for clinical integration. https://www.nature.com/articles/s41591-022-02010-y | We seek teaming partners with access to rare disease patient data, expertise in genomics or phenotypic biomarkers, and experience in clinical validation. Collaboration with patient advocacy groups, healthcare systems, or technology providers specializing in secure data sharing and interoperability is highly desired. We value partners who bring complementary capabilities in scaling AI/ML applications, real-world deployment, and enhancing diagnostic access in underserved populations. | TA3: Sustainable Platform for AI Diagnostic Development |
| Adam Hansen | Geneial | adam@geneial.com | Houston, TX | Geneial focuses on privacy-preserving biomedical data networks to support rare disease discovery and diagnostics. We specialize in standardizing understructured datasets with AI/ML, engaging patients through longitudinal platforms, and integrating molecular diagnostic and ePRO data for advanced analytics. We also bring extensive experience in high-throughput Mendelian disease gene discovery. Our work bridges data collection, integration, and AI-powered insights to end the diagnostic odyssey. | We seek collaborations with healthcare systems, academic centers, diagnostic laboratories, and patient advocacy groups focused on advancing rare disease detection. Ideal partners bring data, expertise, or opportunities to strengthen data integration, enhance patient engagement, and support the development of innovative diagnostic pathways to reduce delays and improve care. | TA1: Massive-Scale Rare Disease Dataset, TA2: Novel Diagnostic Indicators and Population-Scale Discovery, TA3: Sustainable Platform for AI Diagnostic Development |
| Arjun Krishnan | University of Colorado Anschutz Medical Campus | arjun.krishnan@cuanschutz.edu | Aurora, CO | Our team specializes in AI/ML, bioinformatics, functional genomics, and rare disease research. We have expertise in 1) large-scale, multimodal data integration, harmonization, and curation, 2) application to translational science and rare disease genetics, including identifying biomarkers and enable actionable insights, and 3) building scalable infrastructures that enable other researchers to do the same. | Ideal teaming partners would bring clinical expertise in rare diseases, access to diverse patient datasets, and strong connections with underserved populations. Partners with expertise in privacy compliance (e.g., HIPAA, GDPR), rare disease advocacy, and AI model evaluation are valuable. Additional strengths include real-world validation, patient recruitment, and sustainability strategies to ensure long-term success and broad impact in rare disease diagnostics. | TA3: Sustainable Platform for AI Diagnostic Development |
| Lisa Padilla | ExOcular DX Inc | lisa@exoculardx.com | Denver, CO | We developed initial data on extracellular vesicles isolated from human tears and are actively characterizing their surface protein composition. These surface proteins are being evaluated as potential biomarkers for the rare disease known as Neurofibromatosis (NF1) and other ocular disease states. We propose to develop a minimally invasive collection method (blood, urine, tears) allowing us to monitor key biomarkers expressed through tears. | We are interested in partnering with a company that has expertise in punctal plugs, specifically, we hope to develop a plug that will permit us to collect data and distribute therapeutics. We are also interested in smart contact lens technology. Finally, we are interested in a fluorescence machine with high-powered lasers that can provide up to five excitation wavelengths, a high-NA collection lens and up to 3 scattering channels and six fluorescence detection channels. | TA3: Sustainable Platform for AI Diagnostic Development |
| Samuel Volchenboum | University of Chicago | slv@uchicago.edu | Chicago, IL | Data for the Common Good (D4CG) is dedicated to lowering barriers for researchers to access data on rare diseases. By building communities, creating highly-specialized interoperable data standards, and aggregating and curating large amounts of clinical data from people with rare diseases, D4CG aims to have long-standing international impact on rare disease detection, research, and outcomes. D4CG is also leveraging cutting-edge tools for patient-directed data collection for commons development. | Data for the Common Good builds communities dedicated to rare disease data collection and dissemination. We would benefit from working with groups who have a similar mission, especially those who work on data standards, platform development, and creating novel methods for data extraction from existing systems. We want to find partners who work on multimodal data sets and are interested in creating tools to bring together disparate types of data into a platform for downstream analyses. | TA1: Massive-Scale Rare Disease Dataset |
| Mark Kiel | Genomenon | kiel@genomenon.com | Ann Arbor, MI | Genomenon specializes in rare disease data curation, AI/ML algorithm development, and genetic variant interpretation at scale across the entire genome. Our team of 100+ AI and curation experts has developed rapidly scalable frameworks to curate large-scale datasets, including clinical data. With the entirety of the biomedical literature as a substrate, we have developed AI/ML techniques to automate the production of comprehensive variant and patient landscapes for every published rare disease. | Genomenon seeks partners with access to large-scale clinical datasets, experience in developing clinical diagnostic algorithms, and champions of improved rare disease diagnostics. Ideal partners include organizations with patient data in the form of EHR, including extensive rare disease datasets that require indexing, annotation, curation, and interpretation. | TA3: Sustainable Platform for AI Diagnostic Development |
| Matthew Might | UAB | might@uab.edu | Birmingham, AL | AI/ML-driven precision diagnostics | Access to rare disease knowledge; meaningful patient engagement expertise | TA2: Novel Diagnostic Indicators and Population-Scale Discovery |
| Natalia Bk | Sustainable Healthcare Corps | we@sustainablehealthcare.life | New York, NY | Healthcare missed opportunities a-->z | Research integrity, solid foundation, collaboration spirit, security/NDA mindfullness | TA1: Massive-Scale Rare Disease Dataset |
| Penny Asbell MD, FACS, MBA, FARVO | University of Memphis | penny.asbell@gmail.com | Memphis TN, TN | Dry eye disease is a common finding in several rare diseases : Sjogren's Syndrome , ocular GVHD. Another rare disease related to the eye: keratoconus. Given my experience and expertise in dry eye and clinical trials , I could help with eye data related to these rare diseases. I am a clinician scientist with extensive experience in eye research and RCT. We have researched and published on ocular biomarkers and systemic markers that are related to eye disease. , | Happy to join group working on a rare disease that has eye component as part of the disease . | TA1: Massive-Scale Rare Disease Dataset |