Archives: “Machine Learning & AI”

Showing 31 - 40 of 63 posts

Golnaz Vahedi, PhD

https://www.coltonconsortium.org/directory/golnaz-vahedi-phd/

Professor, Department of Genetics, Perelman School of Medicine, University of Pennsylvania Dr. Golnaz Vahedi is a Professor of Genetics at the Perelman School of Medicine, University of Pennsylvania, Co-Director of […]

Rebecca Haberman, MD

https://www.coltonconsortium.org/directory/rebecca-haberman-md/

Assistant Professor, Department of Medicine, NYU Grossman School of Medicine / NYU Langone Health Associate Director, Psoriatic Arthritis Center, NYU Grossman School of Medicine / NYU Langone Health Assistant Director, […]

Aristotelis Tsirigos, PhD

https://www.coltonconsortium.org/directory/aristotelis-tsirigos-phd/

Professor, Department of Medicine, NYU Grossman School of Medicine / NYU Langone Health Professor, Department of Pathology, NYU Grossman School of Medicine / NYU Langone Health Co-Director, Division of Precision […]

Danielle Mowery, PhD, FAMIA

https://www.coltonconsortium.org/directory/danielle-mowery-phd-famia/

Assistant Professor, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania Dr. Danielle Mowery is an Assistant Professor of Informatics in the Department of Biostatistics, Epidemiology […]

Early Detection and Diagnosis of Autoimmune Diseases Using Foundation AI Models

https://www.coltonconsortium.org/projects/early-detection-and-diagnosis-of-autoimmune-diseases-using-foundation-ai-models/

Applying self-supervised AI to multi-modal electronic health records — integrating clinical notes, labs, and imaging — this project builds scalable diagnostic models to detect autoimmune diseases earlier and more precisely.

The Epidermis as a Novel Therapeutic Target in Pemphigus Vulgaris

https://www.coltonconsortium.org/projects/the-epidermis-as-a-novel-therapeutic-target-in-pemphigus-vulgaris/

By profiling genetic and molecular drivers of pemphigus vulgaris across patient skin and blood, this project uncovers genotype-driven inflammatory loops and actionable targets for personalized therapy.

Developing a Multi-parameter Prognostic Prediction Model for Disability Ranks and Progression of Patients with Multiple Sclerosis at the Early Stages of the Disease

https://www.coltonconsortium.org/projects/developing-a-multi-parameter-prognostic-prediction-model-for-disability-ranks-and-progression-of-patients-with-multiple-sclerosis-at-the-early-stages-of-the-disease/

Integrating clinical, imaging, and biological data from large real-world MS cohorts, this project builds a machine learning model to predict disability progression early and enable personalized treatment decisions.

Shedding Light on the Invisible: A New Paradigm for Predicting Multiple Sclerosis Disease Progression Using Novel MRI Tools for Probing Pathology in Normal Appearing Tissues

https://www.coltonconsortium.org/projects/shedding-light-on-the-invisible-a-new-paradigm-for-predicting-multiple-sclerosis-disease-progression-using-novel-mri-tools-for-probing-pathology-in-normal-appearing-tissues/

Applying advanced quantitative MRI to detect pathology invisible to current clinical tools, this project builds an AI model to predict MS progression and enable earlier, more personalized diagnosis and treatment.