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Biomedical Informatics & Data Science
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Current Research Areas

What are we working on? Privacy-protecting Data Sharing, Distributed Analytics and AI Model Evaluation, Biomedical Data Index, and more.

Research Faculty by Area

Organizations on this page

  • AI Ethics

    • Mary-Anne "Annie" Hartley, MD, PhD, MPH

      Hartley's lab is building the Massive Online Open Validation and Evaluation (MOOVE) platform, which allows doctors to validate the real-world performance of the medical large language model Meditron in terms of helpfulness, harmlessness, bias, trust, and safety. In return for this rigorous validation, participants can get their own chatbot, adapted to their preferences and specialty.

      Assistant Professor of Biomedical Informatics and Data Science; Affiliated Faculty, Yale Institute for Global Health

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    • Lucila Ohno-Machado, MD, MBA, PhD

      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

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  • Biomedical Data Index

    • Hua Xu, PhD

      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; Assistant Dean for Biomedical Informatics, Yale School of Medicine

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  • Biomedical Imaging & Computer Vision

    • Qingyu Chen, PhD

      Chen's K99-funded project combines medical imaging analysis and natural language processing for computer assisted eye disease diagnosis.

      Associate Research Scientist of Biomedical Informatics and Data Science

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    • Xenophon Papademetris, PhD

      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; Director: Image Processing and Analysis, Bioimaging Sciences, Radiology and Biomedical Imaging

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    • Nallakkandi Rajeevan, PhD

      Senior Research Scientist of Biomedical Informatics and Data Science; Associate Director for Bioinformatics, Yale Center for Medical Informatics

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  • Biostatistics

  • Blockchain

    • Tsung-Ting Kuo

      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.

      Staff Affiliate - Other

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    • Lucila Ohno-Machado, MD, MBA, PhD

      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

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  • Clinical Informatics

    • Allen Hsiao, MD, FAAP, FAMIA

      Professor of Pediatrics (Emergency Medicine) and of Emergency Medicine; 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

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    • Anthony J. Lisi, DC

      Assistant Professor of Biomedical Informatics & Data Science; Director, Chiropractic Program; Chiropractic Section Chief, Physical Medicine & Rehabilitation

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    • Nallakkandi Rajeevan, PhD

      Senior Research Scientist of Biomedical Informatics and Data Science; Associate Director for Bioinformatics, Yale Center for Medical Informatics

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    • Andrew Taylor, MD, MHS

      Associate Professor of Emergency Medicine and of Bioinformatics & Data Science; Director of Artificial Intelligence and Data Science, Emergency Medicine

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  • Computational Biology

    • Hyunghoon Cho, PhD

      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.

      Assistant Professor of Biomedical Informatics and Data Science

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    • Jihoon Kim

      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.

      Instructor of Biomedical Informatics and Data Science

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    • Nallakkandi Rajeevan, PhD

      Senior Research Scientist of Biomedical Informatics and Data Science; Associate Director for Bioinformatics, Yale Center for Medical Informatics

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    • María Rodríguez Martínez, PhD

      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.

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  • Data Analysis & Machine Learning

  • Data Organization & Clinical Data Warehouse

    • Allen Hsiao, MD, FAAP, FAMIA

      Professor of Pediatrics (Emergency Medicine) and of Emergency Medicine; 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

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    • Daniella Meeker, PhD

      Associate Professor of Biomedical Informatics & Data Science; Chief Research Information Officer, Yale School of Medicine and Yale New Haven Health System

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    • Lucila Ohno-Machado, MD, MBA, PhD

      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

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  • Decision Support

    • Cynthia Brandt, MD, MPH

      Professor of Biomedical Informatics & Data Science; Vice Chair for Education, Biomedical Informatics & Data Science; Professor, Biostatistics

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    • Mary-Anne "Annie" Hartley, MD, PhD, MPH

      Hartley's lab is working with Médecins Sans Frontières (Doctors Without Borders) to build AI algorithms into Antibiogo: a mobile application designed to provide laboratory decision support for the interpretation of antibiograms that detect and quantify antimicrobial resistance.

      Assistant Professor of Biomedical Informatics and Data Science; Affiliated Faculty, Yale Institute for Global Health

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    • Mark Iscoe, MD, MHS

      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

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    • Anthony J. Lisi, DC

      Assistant Professor of Biomedical Informatics & Data Science; Director, Chiropractic Program; Chiropractic Section Chief, Physical Medicine & Rehabilitation

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    • Andrew Taylor, MD, MHS

      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 of Emergency Medicine and of Bioinformatics & Data Science; Director of Artificial Intelligence and Data Science, Emergency Medicine

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  • Distributed Analytics & AI Model Evaluation

  • Humanitarian AI

    • Mary-Anne "Annie" Hartley, MD, PhD, MPH

      Hartley's research is focused on developing and validating novel data-driven tools designed to improve healthcare in low-resource settings, with a special interest in Africa. Her lab collaborates with international NGOs and clinical partners, such as D-tree, WHO and MSF, to create and validate needs-based digital global health technology using novel approaches in data science and informatics.

      Assistant Professor of Biomedical Informatics and Data Science; Affiliated Faculty, Yale Institute for Global Health

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  • Large Language Models (LLM)

    • Mary-Anne "Annie" Hartley, MD, PhD, MPH

      Hartley's lab is collaborating with a team at EPFL and the International Committee of the Red Cross to build Meditron, currently the world's best-performing open-source large language model (LLM) for medicine. Meditron performs within 1% of multi-billion dollar models like GPT-4 on tasks of medical reasoning. Meditron is adapted from Meta’s Llama 2 model and is open source, meaning that its underlying code is available for the public to evaluate and adapt.

      Assistant Professor of Biomedical Informatics and Data Science; Affiliated Faculty, Yale Institute for Global Health

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    • Hua Xu, PhD

      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; Assistant Dean for Biomedical Informatics, Yale School of Medicine

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  • Medical Informatics

  • Medical Software Development

    • Xenophon Papademetris, PhD

      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; Director: Image Processing and Analysis, Bioimaging Sciences, Radiology and Biomedical Imaging

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  • Natural Language Processing (NLP)

    • Qingyu Chen, PhD

      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).

      Associate Research Scientist of Biomedical Informatics and Data Science

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    • Kei-Hoi Cheung, PhD

      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

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    • Hua Xu, PhD

      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; Assistant Dean for Biomedical Informatics, Yale School of Medicine

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  • Network Analysis

  • Privacy-protecting Data Sharing

    • Hyunghoon Cho, PhD

      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.

      Assistant Professor of Biomedical Informatics and Data Science

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    • Lucila Ohno-Machado, MD, MBA, PhD

      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

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  • Public Health Informatics

  • Semantic Web & Ontology

    • Kei-Hoi Cheung, PhD

      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

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