Machine learning

• 

  Subject Areas on Research

  • A Conference-Friendly, Hands-on Introduction to Deep Learning for Radiology Trainees. 
  • A Hybrid Human-Machine Learning Approach for Screening Prostate Biopsies Can Improve Clinical Efficiency Without Compromising Diagnostic Accuracy. 
  • A Machine Learning Methodology for Identification and Triage of Heart Failure Exacerbations. 
  • A Machine-Learning-Based Drug Repurposing Approach Using Baseline Regularization. 
  • A Novel Classification of Intrahepatic Cholangiocarcinoma Phenotypes Using Machine Learning Techniques: An International Multi-Institutional Analysis. 
  • A Predictive Model for Selective Targeting of the Warburg Effect through GAPDH Inhibition with a Natural Product. 
  • A Shared Vision for Machine Learning in Neuroscience. 
  • A contemporary review of machine learning in otolaryngology-head and neck surgery. 
  • A machine learning approach to identify distinct subgroups of veterans at risk for hospitalization or death using administrative and electronic health record data. 
  • A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features. 
  • A multimodality test to guide the management of patients with a pancreatic cyst. 
  • A proposal for developing a platform that evaluates algorithmic equity and accuracy. 
  • A protein corona primer for physical chemists. 
  • A specific microbiota signature is associated to various degrees of ulcerative colitis as assessed by a machine learning approach. 
  • A study of association of Oncotype DX recurrence score with DCE-MRI characteristics using multivariate machine learning models. 
  • A systematic study of the class imbalance problem in convolutional neural networks. 
  • Accurate detection of cerebellar smooth pursuit eye movement abnormalities via mobile phone video and machine learning. 
  • Active learning for computational chemogenomics. 
  • Adaptive Identification of Cortical and Subcortical Imaging Markers of Early Life Stress and Posttraumatic Stress Disorder. 
  • Advanced Editorial to announce a JCAMD Special Issue on Artificial Intelligence and Machine Learning. 
  • Advanced Modeling to Predict Pneumonia in Combat Trauma Patients. 
  • Agonists of G-Protein-Coupled Odorant Receptors Are Predicted from Chemical Features. 
  • An Interpretable Machine Learning Model to Classify Coronary Bifurcation Lesions. 
  • An investigation of machine learning methods in delta-radiomics feature analysis. 
  • An outcome model approach to transporting a randomized controlled trial results to a target population. 
  • Analyzing animal behavior via classifying each video frame using convolutional neural networks. 
  • Anomaly Detection of Calcifications in Mammography Based on 11,000 Negative Cases. 
  • Application of a machine learning algorithm to predict malignancy in thyroid cytopathology. 
  • Application of machine learning classifiers to X-ray diffraction imaging with medically relevant phantoms. 
  • Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology. 
  • Artificial Intelligence and Machine Learning in Cardiology. 
  • Artificial Neural Networks Predict 30-Day Mortality After Hip Fracture: Insights From Machine Learning. 
  • Artificial intelligence and the future of psychiatry: Insights from a global physician survey. 
  • Artificial intelligence for prediction of measurement-based patient-specific quality assurance is ready for prime time. 
  • Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study. 
  • Artificial intelligence in chemistry and drug design. 
  • Artificial intelligence-assisted phenotype discovery of fragile X syndrome in a population-based sample. 
  • Assessment of brain age in posttraumatic stress disorder: Findings from the ENIGMA PTSD and brain age working groups. 
  • Association of Childhood Lead Exposure With MRI Measurements of Structural Brain Integrity in Midlife. 
  • Association of an Electroencephalography-Based Risk Score With Seizure Probability in Hospitalized Patients. 
  • Atomic connectomics signatures for characterization and differentiation of mild cognitive impairment. 
  • AutoAudio: Deep Learning for Automatic Audiogram Interpretation. 
  • Automated Detection of P. falciparum Using Machine Learning Algorithms with Quantitative Phase Images of Unstained Cells. 
  • Automated problem list generation and physicians perspective from a pilot study. 
  • Automated segmentation of the canine corpus callosum for the measurement of diffusion tensor imaging. 
  • Automatic localization of the subthalamic nucleus on patient-specific clinical MRI by incorporating 7 T MRI and machine learning: Application in deep brain stimulation. 
  • Automatic white matter lesion segmentation using contrast enhanced FLAIR intensity and Markov Random Field. 
  • Big data: More than big data sets. 
  • Biomarkers of cavernous angioma with symptomatic hemorrhage. 
  • Brain-wide Electrical Spatiotemporal Dynamics Encode Depression Vulnerability. 
  • Brief Report: Can a Composite Heart Rate Variability Biomarker Shed New Insights About Autism Spectrum Disorder in School-Aged Children? 
  • Can subjective pain be inferred from objective physiological data? Evidence from patients with sickle cell disease. 
  • Capturing spike train temporal pattern with wavelet average coefficient for brain machine interface. 
  • Causal network inference from gene transcriptional time-series response to glucocorticoids. 
  • Clinical Data Prediction Model to Identify Patients With Early-Stage Pancreatic Cancer. 
  • Clinical applications of machine learning in the diagnosis, classification, and prediction of heart failure. 
  • Clinical risk factors and inflammatory biomarkers of post-traumatic acute kidney injury in combat patients. 
  • Combining adult with pediatric patient data to develop a clinical decision support tool intended for children: leveraging machine learning to model heterogeneity. 
  • Combining multiple contrasts for improving machine learning-based classification of cervical cancers with a low-cost point-of-care Pocket colposcope. 
  • Commentary: Machine Learning Applications of Surgical Imaging for the Diagnosis and Treatment of Spine Disorders: Current State of the Art. 
  • Computational advances in combating colloidal aggregation in drug discovery. 
  • Computational approach to radiogenomics of breast cancer: Luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms. 
  • Computationally guided high-throughput design of self-assembling drug nanoparticles. 
  • Continuous-time probabilistic models for longitudinal electronic health records. 
  • Contrasting Roles of Transcription Factors Spineless and EcR in the Highly Dynamic Chromatin Landscape of Butterfly Wing Metamorphosis. 
  • Corticosteroid Randomization after Significant Head Injury and International Mission for Prognosis and Clinical Trials in Traumatic Brain Injury Models Compared with a Machine Learning-Based Predictive Model from Tanzania. 
  • Deep Learning to Classify Radiology Free-Text Reports. 
  • Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing. 
  • Deep learning for the dynamic prediction of multivariate longitudinal and survival data. 
  • Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI. 
  • Determining clinical course of diffuse large B-cell lymphoma using targeted transcriptome and machine learning algorithms. 
  • Developing an Algorithm to Detect Early Childhood Obesity in Two Tertiary Pediatric Medical Centers. 
  • Developing and Validating Methods to Assemble Systemic Lupus Erythematosus Births in the Electronic Health Record. 
  • Development and Assessment of an Interpretable Machine Learning Triage Tool for Estimating Mortality After Emergency Admissions. 
  • Development and Performance of the Pulmonary Embolism Result Forecast Model (PERFORM) for Computed Tomography Clinical Decision Support. 
  • Development and Validation of Machine Learning Models to Predict Admission From Emergency Department to Inpatient and Intensive Care Units. 
  • Development and Validation of Machine Learning-Based Race-Specific Models to Predict 10-Year Risk of Heart Failure: A Multicohort Analysis. 
  • Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes. 
  • Development and validation of a machine learning, smartphone-based tonometer. 
  • Development and validation of an ensemble machine learning framework for detection of all-cause advanced hepatic fibrosis: a retrospective cohort study. 
  • Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): A retrospective, single-site study. 
  • Development of Algorithms for Automated Detection of Cervical Pre-Cancers With a Low-Cost, Point-of-Care, Pocket Colposcope. 
  • Development of a Machine Learning Model Using Electronic Health Record Data to Identify Antibiotic Use Among Hospitalized Patients. 
  • Development of realistic multi-contrast textured XCAT (MT-XCAT) phantoms using a dual-discriminator conditional-generative adversarial network (D-CGAN). 
  • Diagnosis and prognosis of Alzheimer's disease using brain morphometry and white matter connectomes. 
  • Digital pathology and computational image analysis in nephropathology. 
  • Do no harm: a roadmap for responsible machine learning for health care. 
  • Does Infant-Directed Speech Help Phonetic Learning? A Machine Learning Investigation. 
  • Drug properties and host factors contribute to biochemical presentation of drug-induced liver injury: a prediction model from a machine learning approach. 
  • Dry eye is matched by increased intrasubject variability in tear osmolarity as confirmed by machine learning approach. 
  • Encoding primitives generation policy learning for robotic arm to overcome catastrophic forgetting in sequential multi-tasks learning. 
  • Enhancement of Risk Prediction With Machine Learning: Rise of the Machines. 
  • Environmental and clinical data utility in pediatric asthma exacerbation risk prediction models. 
  • Evaluating High-Dimensional Machine Learning Models to Predict Hospital Mortality Among Older Patients With Cancer. 
  • Expression of socially sensitive genes: The multi-ethnic study of atherosclerosis. 
  • FlowKit: A Python Toolkit for Integrated Manual and Automated Cytometry Analysis Workflows. 
  • GENESIS: Gene-Specific Machine Learning Models for Variants of Uncertain Significance Found in Catecholaminergic Polymorphic Ventricular Tachycardia and Long QT Syndrome-Associated Genes. 
  • Generative adversarial networks to predict treatment response for neovascular age-related macular degeneration: interesting, but is it useful? 
  • Generative adversarial networks with mixture of t-distributions noise for diverse image generation. 
  • Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas. 
  • Guiding Clinical Decisions Through Predictive Risk Rules. 
  • Harmonizing Genetic Ancestry and Self-identified Race/Ethnicity in Genome-wide Association Studies. 
  • How far have we come? Artificial intelligence for chest radiograph interpretation. 
  • Human Gut Microbiota Predicts Susceptibility to Vibrio cholerae Infection. 
  • Human genetic and metabolite variation reveals that methylthioadenosine is a prognostic biomarker and an inflammatory regulator in sepsis. 
  • Identifying predictors of antimicrobial exposure in hospitalized patients using a machine learning approach. 
  • Identifying treatment effects of an informal caregiver education intervention to increase days in the community and decrease caregiver distress: a machine-learning secondary analysis of subgroup effects in the HI-FIVES randomized clinical trial. 
  • Impact of train/test sample regimen on performance estimate stability of machine learning in cardiovascular imaging. 
  • Improved Detection of Invasive Pulmonary Aspergillosis Arising during Leukemia Treatment Using a Panel of Host Response Proteins and Fungal Antigens. 
  • Improving Acute GI Bleeding Management Through Artificial Intelligence: Unnatural Selection? 
  • In-Depth Characterization and Validation of Human Urine Metabolomes Reveal Novel Metabolic Signatures of Lower Urinary Tract Symptoms. 
  • Increasing a microscope's effective field of view via overlapped imaging and machine learning. 
  • Incremental Benefits of Machine Learning-When Do We Need a Better Mousetrap? 
  • Individual-Specific, Beat-to-beat Trending of Significant Human Blood Loss: The Compensatory Reserve. 
  • Integrating a Machine Learning System Into Clinical Workflows: Qualitative Study. 
  • Intelligence and creativity share a common cognitive and neural basis. 
  • Interleukin 1 receptor (IL-1R1) activation exacerbates toxin-induced acute kidney injury. 
  • Investigating Predictors of Cognitive Decline Using Machine Learning. 
  • Kidney transplant in the COVID era: Cautious optimism and continued vigilance. 
  • Knowledge-based planning for intensity-modulated radiation therapy: A review of data-driven approaches. 
  • Large-scale assessment of consistency in sleep stage scoring rules among multiple sleep centers using an interpretable machine learning algorithm. 
  • Low-dimensional learned feature spaces quantify individual and group differences in vocal repertoires. 
  • MSIseq: Software for Assessing Microsatellite Instability from Catalogs of Somatic Mutations. 
  • Machine Learning Algorithm Predicts Cardiac Resynchronization Therapy Outcomes: Lessons From the COMPANION Trial. 
  • Machine Learning Algorithms to Predict Mortality and Allocate Palliative Care for Older Patients With Hip Fracture. 
  • Machine Learning Applied to Registry Data: Development of a Patient-Specific Prediction Model for Blood Transfusion Requirements During Craniofacial Surgery Using the Pediatric Craniofacial Perioperative Registry Dataset. 
  • Machine Learning Approach to Classify Cardiovascular Disease in Patients With Nonalcoholic Fatty Liver Disease in the UK Biobank Cohort. 
  • Machine Learning Assisted Approach for Finding Novel High Activity Agonists of Human Ectopic Olfactory Receptors. 
  • Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas. 
  • Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation. 
  • Machine Learning Methods Improve Prognostication, Identify Clinically Distinct Phenotypes, and Detect Heterogeneity in Response to Therapy in a Large Cohort of Heart Failure Patients. 
  • Machine Learning Uncovers Food- and Excipient-Drug Interactions. 
  • Machine Learning and Statistical Models to Predict Postpartum Hemorrhage. 
  • Machine Learning for Predicting Discharge Disposition After Traumatic Brain Injury. 
  • Machine Learning for Predicting In-Hospital Mortality After Traumatic Brain Injury in Both High-Income and Low- and Middle-Income Countries. 
  • Machine Learning for Treatment Assignment: Improving Individualized Risk Attribution. 
  • Machine Learning for Urodynamic Detection of Detrusor Overactivity. 
  • Machine Learning in Medical Imaging: All Journeys Begin With a Single Step. 
  • Machine Learning-Based Multiparametric Magnetic Resonance Imaging Radiomics for Prediction of H3K27M Mutation in Midline Gliomas. 
  • Machine learning and modeling: Data, validation, communication challenges. 
  • Machine learning approach to differentiation of peripheral schwannomas and neurofibromas: A multi-center study. 
  • Machine learning approach to measurement of criticism: The core dimension of expressed emotion. 
  • Machine learning derived segmentation of phase velocity encoded cardiovascular magnetic resonance for fully automated aortic flow quantification. 
  • Machine learning does not improve upon traditional regression in predicting outcomes in atrial fibrillation: an analysis of the ORBIT-AF and GARFIELD-AF registries. 
  • Machine learning for phenotyping opioid overdose events. 
  • Machine learning in 'big data': handle with care. 
  • Machine learning methods for credibility assessment of interviewees based on posturographic data. 
  • Machine learning predicts stem cell transplant response in severe scleroderma. 
  • Machine learning risk prediction of mortality for patients undergoing surgery with perioperative SARS-CoV-2: the COVIDSurg mortality score. 
  • Machine learning-based prediction of future breast cancer using algorithmically measured background parenchymal enhancement on high-risk screening MRI. 
  • Machine-Learning Approach to Differentiation of Benign and Malignant Peripheral Nerve Sheath Tumors: A Multicenter Study. 
  • Machine-learning phenotypic classification of bicuspid aortopathy. 
  • Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry. 
  • Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes. 
  • Mapping risk of ischemic heart disease using machine learning in a Brazilian state. 
  • Metabarcoding and machine learning analysis of environmental DNA in ballast water arriving to hub ports. 
  • Metabolic liver disease - what's in a name? 
  • Microbiome composition and implications for ballast water classification using machine learning. 
  • Microelectrode Recordings Validate the Clinical Visualization of Subthalamic-Nucleus Based on 7T Magnetic Resonance Imaging and Machine Learning for Deep Brain Stimulation Surgery. 
  • Modeling false positive error making patterns in radiology trainees for improved mammography education. 
  • More Than Incremental: Harnessing Machine Learning to Predict Breast Cancer Risk. 
  • Moving Away From Error-Related Potentials to Achieve Spelling Correction in P300 Spellers. 
  • Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges. 
  • Multi-omics driven predictions of response to acute phase combination antidepressant therapy: a machine learning approach with cross-trial replication. 
  • Multiple ocular diseases detection based on joint sparse multi-task learning. 
  • Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set. 
  • Novel deep neural network based pattern field classification architectures. 
  • Numerical Models and In Vitro Assays to Study Odorant Receptors. 
  • On Deep Learning for Medical Image Analysis. 
  • Performance of a Machine Learning Classifier of Knee MRI Reports in Two Large Academic Radiology Practices: A Tool to Estimate Diagnostic Yield. 
  • Pharmacogenomics-Driven Prediction of Antidepressant Treatment Outcomes: A Machine-Learning Approach With Multi-trial Replication. 
  • Plasma kallikrein predicts primary graft dysfunction after heart transplant. 
  • Predicting Emergency Visits and Hospital Admissions During Radiation and Chemoradiation: An Internally Validated Pretreatment Machine Learning Algorithm. 
  • Predicting Parkinson's disease trajectory using clinical and neuroimaging baseline measures. 
  • Predicting emerging chemical content in consumer products using machine learning. 
  • Predicting in-hospital length of stay: a two-stage modeling approach to account for highly skewed data. 
  • Predicting outcomes of pelvic exenteration using machine learning. 
  • Predicting protein-ligand interactions based on bow-pharmacological space and Bayesian additive regression trees. 
  • Predicting response to Cognitive Processing Therapy for PTSD: A machine-learning approach. 
  • Predicting the Individual Treatment Effect of Neurosurgery for Patients with Traumatic Brain Injury in the Low-Resource Setting: A Machine Learning Approach in Uganda. 
  • Prediction Models - Development, Evaluation, and Clinical Application. 
  • Prediction of 30-Day All-Cause Readmissions in Patients Hospitalized for Heart Failure: Comparison of Machine Learning and Other Statistical Approaches. 
  • Predictors of Contemporary under-5 Child Mortality in Low- and Middle-Income Countries: A Machine Learning Approach. 
  • Predictors of real-time fMRI neurofeedback performance and improvement - A machine learning mega-analysis. 
  • Presurgical detection of brain invasion status in meningiomas based on first-order histogram based texture analysis of contrast enhanced imaging. 
  • Prevalence and predictors of C. difficile infections in hospitalized patients with major surgical procedures in the USA: Analysis using traditional and machine learning methods. 
  • Principal component reconstruction (PCR) for cine CBCT with motion learning from 2D fluoroscopy. 
  • Prospective and External Evaluation of a Machine Learning Model to Predict In-Hospital Mortality of Adults at Time of Admission. 
  • Quantifying Risk for Anxiety Disorders in Preschool Children: A Machine Learning Approach. 
  • Radiomics analysis using stability selection supervised component analysis for right-censored survival data. 
  • Random forest modeling can predict infectious complications following trauma laparotomy. 
  • Random forest prediction of Alzheimer's disease using pairwise selection from time series data. 
  • Recent developments in pediatric retina. 
  • Recommendations for Reporting Machine Learning Analyses in Clinical Research. 
  • Reinforcement Learning Methods in Public Health. 
  • Reply: Titration of Guideline-Directed Medical Therapy Improves Patient-Centered Outcomes in Heart Failure With Reduced Ejection Fraction. 
  • Response to Letter. 
  • SAGES consensus recommendations on an annotation framework for surgical video. 
  • Segmentation of the Proximal Femur from MR Images using Deep Convolutional Neural Networks. 
  • Selection of Informative Examples in Chemogenomic Datasets. 
  • Sensor-Array Optimization Based on Time-Series Data Analytics for Sanitation-Related Malodor Detection. 
  • Single Cell Analysis of Stored Red Blood Cells Using Ultra-High Throughput Holographic Cytometry. 
  • Solvation Free Energy Calculations with Quantum Mechanics/Molecular Mechanics and Machine Learning Models. 
  • Subject Matter Knowledge in the Age of Big Data and Machine Learning. 
  • Synthetic breast phantoms from patient based eigenbreasts. 
  • System for High-Intensity Evaluation During Radiation Therapy (SHIELD-RT): A Prospective Randomized Study of Machine Learning-Directed Clinical Evaluations During Radiation and Chemoradiation. 
  • TCR Repertoires of Thymic Conventional and Regulatory T Cells: Identification and Characterization of Both Unique and Shared TCR Sequences. 
  • Teaching yourself about structural racism will improve your machine learning. 
  • Testing the Relative Performance of Data Adaptive Prediction Algorithms: A Generalized Test of Conditional Risk Differences. 
  • The Role of Machine Learning in Cardiovascular Pathology. 
  • The Trials and Tribulations of Assembling Large Medical Imaging Datasets for Machine Learning Applications. 
  • The User Experience of AI. 
  • The World Health Organization Adult Attention-Deficit/Hyperactivity Disorder Self-Report Screening Scale for DSM-5. 
  • The optimal number of lymph nodes to evaluate among patients undergoing surgery for gallbladder cancer: Correlating the number of nodes removed with survival in 6531 patients. 
  • The role of machine and deep learning in modern medical physics. 
  • The role of machine learning in clinical research: transforming the future of evidence generation. 
  • Towards precision medicine: Accurate predictive modeling of infectious complications in combat casualties. 
  • Training and Interpreting Machine Learning Algorithms to Evaluate Fall Risk After Emergency Department Visits. 
  • Transfer learning for fluence map prediction in adrenal stereotactic body radiation therapy. 
  • Transient-evoked otoacoustic emission signals predicting outcomes of acute sensorineural hearing loss in patients with Ménière's disease. 
  • Tumor cell sensitivity to vemurafenib can be predicted from protein expression in a BRAF-V600E basket trial setting. 
  • Use of Machine Learning on Contact Lens Sensor-Derived Parameters for the Diagnosis of Primary Open-angle Glaucoma. 
  • Use of Mobile Health Apps and Wearable Technology to Assess Changes and Predict Pain During Treatment of Acute Pain in Sickle Cell Disease: Feasibility Study. 
  • Using Artificial Intelligence to Improve the Quality and Safety of Radiation Therapy. 
  • Using Machine Learning to Identify Heterogeneous Effects in Randomized Clinical Trials-Moving Beyond the Forest Plot and Into the Forest. 
  • Using machine learning to identify patterns of lifetime health problems in decedents with autism spectrum disorder. 
  • Utilizing Machine Learning for Pre- and Postoperative Assessment of Patients Undergoing Resection for BCLC-0, A and B Hepatocellular Carcinoma: Implications for Resection Beyond the BCLC Guidelines. 
  • Variables of importance in the Scientific Registry of Transplant Recipients database predictive of heart transplant waitlist mortality. 
  • Vibrational Properties of Metastable Polymorph Structures by Machine Learning. 
  • Weakly supervised instance learning for thyroid malignancy prediction from whole slide cytopathology images. 
  • What Counts as "Clinical Data" in Machine Learning Healthcare Applications? 
  • Y-Net for Chest X-Ray Preprocessing: Simultaneous Classification of Geometry and Segmentation of Annotations. 
  • fastJT: An R package for robust and efficient feature selection for machine learning and genome-wide association studies. 
• 

  Keywords of People

  • Bareja, Akshay, Assistant Professor of Medicine, Medicine, Endocrinology, Metabolism, and Nutrition
  • Bedoya, Armando Diego, Assistant Professor of Medicine, Biostatistics & Bioinformatics
  • Bryan, Jordan, Student, Statistical Science
  • Dunn, Timothy, Assistant Professor of Biomedical Engineering, Biomedical Engineering
  • Farsiu, Sina, Professor in the Department of Biomedical Engineering, Electrical and Computer Engineering
  • Gong, Neil, Assistant Professor of Electrical and Computer Engineering, Electrical and Computer Engineering
  • Hong, Chuan, Assistant Professor of Biostatistics & Bioinformatics, Biostatistics & Bioinformatics
  • Huettel, Scott, Professor in the Department of Psychology and Neuroscience, Duke Science & Society
  • Jelovsek, John E, Professor of Obstetrics and Gynecology, Obstetrics and Gynecology, Urogynecology
  • Le, Cat, Student, Electrical and Computer Engineering
  • Lee, Holden, Phillip Griffiths Assistant Research Professor, Mathematics
  • Lo, Joseph Yuan-Chieh, Professor in Radiology, Electrical and Computer Engineering
  • Lynch, Jerome Peter, Professor in the Department of Civil and Environmental Engineering, Pratt School of Engineering
  • Ma, Li, Associate Professor of Statistical Science, Biostatistics & Bioinformatics
  • Machanavajjhala, Ashwinkumar Venkatanaga, Associate Professor of Computer Science, Computer Science
  • Mandal, Sayan, Student, Electrical and Computer Engineering
  • McKee, Paul, Student, Psychology & Neuroscience
  • Mehta, Nikhil, Student, Electrical and Computer Engineering
  • Pearson, John, Assistant Professor of Biostatistics & Bioinformatics, Center for Cognitive Neuroscience
  • Pryer, Kathleen M., Professor of Biology, Duke Science & Society
  • Reker, Daniel, Assistant Professor of Biomedical Engineering, Biomedical Engineering
  • Rubin, Geoffrey D, George Barth Geller Distinguished Professorship for Research in Cardiovascular Diseases, Radiology
  • Tchapyjnikov, Dmitry, Adjunct Assistant Professor in the Department of Pediatrics, Pediatrics, Neurology
  • Wang, Yun, Assistant Professor in Psychiatry and Behavioral Sciences, Electrical and Computer Engineering
  • Wiggins, Walter, Assistant Professor of Radiology, Radiology, Neuroradiology
  • Xiao, Ran, Assistant Consulting Professor in the School of Nursing, School of Nursing
  • Zhang, Ruda, Assistant Research Professor of Mathematics, Mathematics