What are we working on? Privacy-protecting Data Sharing, Distributed Analytics and AI Model Evaluation, Biomedical Data Index, and more.
Cho's lab examines how to share and analyze sensitive data with privacy, and better understand the risks of modern data sharing practices. His DP5 grant focuses on computational methods for enhancing privacy in biomedical data sharing, and recent work has included privacy-preserving federated analytics for precision medicine and secure genome-wide association analysis.
Associate Professor of Biomedical Informatics and Data Science; Director of Graduate Admissions, Human Genome Sciences
Associate Professor of Biomedical Informatics and Data Science and Surgery
Patients have different preferences toward the sharing of their electronic health records (EHRs) for research. We study ways in which we can share these records with researchers while protecting patient privacy. Several solutions exist, ranging from transforming data to make it less re-identifiable, to policies that regulate how data can be used for various purposes.
Waldemar von Zedtwitz Professor of Medicine and Biomedical Informatics and Data Science; Deputy Dean for Biomedical Informatics; Chair, Department of Biomedical Informatics and Data Science
Associate Professor of Biomedical Informatics and Data Science; Director of Graduate Admissions, Human Genome Sciences
Associate Professor of Biomedical Informatics and Data Science and Surgery
Associate Professor Adjunct of Biomedical Informatics and Data Science; Director of Artificial Intelligence and Data Science, Emergency Medicine
Robert T. McCluskey Professor of Biomedical Informatics and Data Science; Vice Chair for Research and Development, Department of Biomedical Informatics and Data Science; Associate Dean for Biomedical Informatics, Yale School of Medicine; Director, CBB MS Program , Biomedical Informatics & Data Science; Professor, Computer Science
Xu's lab has created DataMed, a biomedical data discovery index. Its goal is to discover data sets across data repositories or data aggregators. In the future it will allow searching outside these boundaries. DataMed supports the NIH-endorsed FAIR principles of Findability, Accessibility, Interoperability and Reusability of datasets with current functionality assisting in finding datasets and providing access information about them.
Robert T. McCluskey Professor of Biomedical Informatics and Data Science; Vice Chair for Research and Development, Department of Biomedical Informatics and Data Science; Associate Dean for Biomedical Informatics, Yale School of Medicine; Director, CBB MS Program , Biomedical Informatics & Data Science; Professor, Computer Science
Chen has helped develop foundational BioNLP models and introduced innovative BioNLP methods for data curation, information retrieval, and information extraction. Chen has also researched downstream BioNLP applications, helping to develop LitCovid (a hub for tracking scientific literature about COVID-19), LitSense (a machine learning tool that finds, recommends and curates biomedical publications), and LitSuggest (a sentence-level search tool for biomedical literature).
Assistant Professor of Biomedical Informatics and Data Science
Cheung's latest project will explore how new data and metadata standards can be used to harmonize diverse environmental health information. Integrating a variety of data types can help researchers investigate drinking water contaminants and their associated impact on human health. To extract and integrate these data types, his team will AI techniques such as NLP. They also plan to build an environmental exposure knowledge graph, and engage with users to evaluate the impact of their project.
Professor of Biomedical Informatics & Data Science; Professor, Biostatistics
Associate Professor of Biomedical Informatics and Data Science and Surgery
In the last few years, Xu's team developed CLAMP (Clinical Language, Annotation, Modeling & Processing Toolkit), a comprehensive clinical NLP tool utilized by over 650 organizations to extract key information from biomedical textual data. From its humble origins as a student project, this endeavor has flourished into a revolutionary product that has found widespread application in tackling real-world challenges.
Robert T. McCluskey Professor of Biomedical Informatics and Data Science; Vice Chair for Research and Development, Department of Biomedical Informatics and Data Science; Associate Dean for Biomedical Informatics, Yale School of Medicine; Director, CBB MS Program , Biomedical Informatics & Data Science; Professor, Computer Science
Rodríguez Martínez's lab develops domain-specific protein language models, comparing their performence to general models on immunology-related tasks.
Associate Professor of Biomedical Informatics and Data Science
Researchers are inundated with an ever-growing corpus of publications, including scientific articles, books, and working papers. This biomedical knowledge poses a significant challenge in terms of efficient data navigation and exploration. To address this, Xu's lab is building BIKE (Biomedical Knowledge Explorer), a visual analytics tool leveraging a large language model approach to explore the semantic embedding space of massive publications.
Robert T. McCluskey Professor of Biomedical Informatics and Data Science; Vice Chair for Research and Development, Department of Biomedical Informatics and Data Science; Associate Dean for Biomedical Informatics, Yale School of Medicine; Director, CBB MS Program , Biomedical Informatics & Data Science; Professor, Computer Science
Many health care algorithms are data-driven, but if the data aren’t representative of the full population, it can create biases against those who are less represented. To address this rapidly growing problem, the Agency for Healthcare Research and Quality (AHRQ) and the National Institute on Minority Health and Health Disparities (NIMHD) recently convened a diverse panel of experts, co-chaired by Ohno-Machado. The panel identified core guiding principles for eliminating algorithmic bias.
Waldemar von Zedtwitz Professor of Medicine and Biomedical Informatics and Data Science; Deputy Dean for Biomedical Informatics; Chair, Department of Biomedical Informatics and Data Science
As biomedical research is increasingly data-driven, vast amounts of data have been generated and stored in databases. But these databases are silos that have heterogeneous formats and interfaces, making data integration/interoperability challenging. To transform these unconnected databases into connected knowledgebases, ontologies and semantic web are key technologies that enable systems-level data integration and knowledge-driven querying. Contact Dr. Cheung if you are interested in this topic.
Professor of Biomedical Informatics & Data Science; Professor, Biostatistics
Professor of Biomedical Informatics & Data Science; Vice Chair for Education, Biomedical Informatics & Data Science; Professor, Biostatistics
Professor of Biomedical Informatics and Data Science; Chief Research Information Officer, Yale New Haven Health System
Senior Research Scientist of Biomedical Informatics and Data Science; Associate Director for Bioinformatics, Yale Center for Medical Informatics
Professor of Pediatrics (Emergency Medicine) and of Emergency Medicine; Chief Health Information Officer, YNHHS; Chief Health Information Officer, Yale School of Medicine & Yale New Haven Health, Yale School of Medicine; Vice Chair of Clinical Systems, Biomedical Informatics & Data Science
Assistant Professor of Biomedical Informatics & Data Science; Director, Chiropractic Program, Department of Veterans Affairs; Chiropractic Section Chief, Physical Medicine & Rehabilitation, VA Connecticut Healthcare System
Senior Research Scientist of Biomedical Informatics and Data Science; Associate Director for Bioinformatics, Yale Center for Medical Informatics
Associate Professor Adjunct of Biomedical Informatics and Data Science; Director of Artificial Intelligence and Data Science, Emergency Medicine
Assistant Professor of Biomedical Informatics and Data Science; Affiliated Faculty, Yale Center for Genomic Health
Cho's lab examines how computational biologists can engineer scalable and integrative tools that offer new ways to explore the growing collection of single-cell omics data, and how researchers can harness the wealth of knowledge embedded in molecular interaction networks to empower biomedical research.
Associate Professor of Biomedical Informatics and Data Science; Director of Graduate Admissions, Human Genome Sciences
Kim has a track record of successful bioinformatics projects in distributed computing and secure environments, which involve development of software tools for family GWAS, statistical and machine learning models in collaboration with Dr. Mike Levin’s team at Imperial College London, all supported by NIH grants.
Assistant Professor of Biomedical Informatics and Data Science
Senior Research Scientist of Biomedical Informatics and Data Science; Associate Director for Bioinformatics, Yale Center for Medical Informatics
Rodríguez Martínez focuses on understanding T and B cell function in the context of complex diseases, such as cancer and autoimmune diseases. Her research integrates mechanistic and AI models, emphasizing interpretable deep learning methods to uncover the rules behind model predictions. In this field, her team has developed interpretable models to predict T cell receptor binding and investigated B cell development.
Associate Professor of Biomedical Informatics and Data Science
Chen's K99-funded project combines medical imaging analysis and natural language processing for computer assisted eye disease diagnosis.
Assistant Professor of Biomedical Informatics and Data Science
Elizabeth Mears and House Jameson Professor of Radiology and Biomedical Imaging, Therapeutic Radiology and of Biomedical Informatics & Data Science; Director, PET Center; Vice Chair Scientific Research, Radiology & Biomedical Imaging; Director, Yale Biomedical Imaging Institute
Papademetris has been involved in cardiac image analysis, image-guided epilepsy neurosurgery, image-guided prostate biopsy, developing methods for real-time fMRI, vascular image analysis and general neuroimaging. He used model-based approaches (biomechanical & physiological models) and data-driven statistical/machine learning approaches. These projects spanned most imaging modalities (MRI/CT/Ultrasound/PET/SPECT/Optical) and body parts (brain/head/heart/vasculature/prostate/abdomen/hindlimbs).
Professor of Biomedical Informatics & Data Science, and Radiology & Biomedical Imaging; Associate Director of Biomedical Imaging Data Sciences, Yale Biomedical Imaging Institute; Director of Medical Software and Medical Artificial Intelligence Certificate Program, Department of Biomedical Informatics & Data Science, Yale Biomedical Imaging Institute
Senior Research Scientist of Biomedical Informatics and Data Science; Associate Director for Bioinformatics, Yale Center for Medical Informatics
Professor of Pediatrics (Emergency Medicine) and of Emergency Medicine; Chief Health Information Officer, YNHHS; Chief Health Information Officer, Yale School of Medicine & Yale New Haven Health, Yale School of Medicine; Vice Chair of Clinical Systems, Biomedical Informatics & Data Science
Professor of Biomedical Informatics and Data Science; Chief Research Information Officer, Yale New Haven Health System
Waldemar von Zedtwitz Professor of Medicine and Biomedical Informatics and Data Science; Deputy Dean for Biomedical Informatics; Chair, Department of Biomedical Informatics and Data Science
Professor of Biomedical Informatics & Data Science; Vice Chair for Education, Biomedical Informatics & Data Science; Professor, Biostatistics
Collaborating with Andrew Taylor, Melissa Davis, and Beckman Coulter Diagnostics, Iscoe is creating an AI tool that predicts a UTI based on urinalysis and other results in a patient’s electronic medical record. The team hopes that by presenting the AI model’s predictions in real time they can augment clinicians’ clinical reasoning, leading to safer and more appropriate diagnoses and treatment decisions.
Assistant Professor of Emergency Medicine and Biomedical Informatics and Data Science
Assistant Professor of Biomedical Informatics & Data Science; Director, Chiropractic Program, Department of Veterans Affairs; Chiropractic Section Chief, Physical Medicine & Rehabilitation, VA Connecticut Healthcare System
Emergency medicine is a unique and exciting field for the application of predictive analytics. Providers must make numerous decisions (admission/discharge; ordering tests, medications, etc.) in a chaotic environment within a compressed time-frame that can lead to a variety of cognitive errors. Taylor's lab is focused on augmenting this decision process and lessening the cognitive burden of providers through integration of machine learning tools into clinical work-flows.
Associate Professor Adjunct of Biomedical Informatics and Data Science; Director of Artificial Intelligence and Data Science, Emergency Medicine
Professor of Biomedical Informatics & Data Science; Professor, Biostatistics
Associate Professor of Biomedical Informatics and Data Science; Director of Graduate Admissions, Human Genome Sciences
Associate Professor of Biomedical Informatics and Data Science and Surgery
Senior Research Scientist of Biomedical Informatics and Data Science; Associate Director for Bioinformatics, Yale Center for Medical Informatics
Associate Professor of Biomedical Informatics and Data Science
Associate Professor Adjunct of Biomedical Informatics and Data Science; Director of Artificial Intelligence and Data Science, Emergency Medicine
Kuo researches blockchain technology and how it can be combined with machine learning to construct distributed privacy-preserving modeling methods. He researches strategies to mitigate potential issues in real-world scenarios such as biomedical research network-of-networks, sites joining and leaving, hierarchical fault tolerance, and model misconduct detection, during privacy-preserving modeling processes.
Associate Professor of Biomedical Informatics and Data Science and Surgery
Waldemar von Zedtwitz Professor of Medicine and Biomedical Informatics and Data Science; Deputy Dean for Biomedical Informatics; Chair, Department of Biomedical Informatics and Data Science
Associate Professor of Biomedical Informatics and Data Science and Surgery
Papademetris' software work, directly linked to his image analysis research, has focused on the creation of tools for image analysis at Yale and as a consultant for industry. His early work used C++/Motif/OpenInventor on Silicon Graphics workstations, later using C++/Tcl/VTK as part of the creation of the original Yale BioImage Suite software package. More recently, he built web-based tools using a combination of JS and C++ to create server-less tools that can be run in a browser.
Professor of Biomedical Informatics & Data Science, and Radiology & Biomedical Imaging; Associate Director of Biomedical Imaging Data Sciences, Yale Biomedical Imaging Institute; Director of Medical Software and Medical Artificial Intelligence Certificate Program, Department of Biomedical Informatics & Data Science, Yale Biomedical Imaging Institute