room :Carnegie 212
Director of Innovative Engineering, Department of Neurology
Director, Kata, Department of Neurology
Physics Driven Animation, Neuro-motor Rehabilitation Using Interactive Experiences, Virtual and Augmented Reality for Medicine
Omar Ahmad, Ph.D. is the Director of Innovative Engineering in the Department of Neurology at the Johns Hopkins School of Medicine and also the Director and Chief Creative Engineer of the Kata design group in the Department of Neurology. He is responsible for Kata’s creative output and all Kata personnel. His strategic vision focuses on four fundamental aspects:
- Fostering the best entertainment content possible through artistic/emotional mastery, scientific research, and creative technological innovation
- Creating visceral user experiences based upon neuroscience and physics based animations for engendering movements beneficial for motor recovery and brain repair
- Overall vision for all Kata products, bridging the gap between academia and commercial-quality engineering
- Applying the creative output of Kata to medical therapeutics on the clinical floor and iterating through design with physicians and clinical staff
Dr. Ahmad is the inventor of a number of physics-based real-time technologies and algorithms to create visceral user experiences that engender specific movements for greater brain plasticity after brain injury. He is responsible for maintaining tangible product output for KATA, so that it exceeds, in quality and innovation, what would be expected from academic or commercial production in isolation.
He received his BS, MSE, and PhD degrees in Computer Science, from the Johns Hopkins University, GWC Whiting School of Engineering.
room:340 Malone Hall
Assistant Research Scientist
Translational research, Clinical outcome prediction, Surgical performance assessment,Big data analytics
Narges received her PhD in Computer Science from Johns Hopkins University in 2015, her BSc in Computer Engineering and MSc in Artificial Intelligence from Tehran Polytechnic University, Iran. Her work is broadly focused on providing new solutions for analyzing dense human motion time-series in surgical procedures as well as sparse events in medical interventions. Her passion is to develop essential infrastructures for better quality of care in hospitals across the world.
location :Malone Hall 337
Assistant Research Professor, Department of Computer Science
Object Detection and Pose Estimation, Human Activity Recognition, Computational Perception in Robotics, Medical Image Analysis and their Applications
Haider Ali is an Assistant Research Professor in the Department of Computer Science at the Johns Hopkins University. He was an Associate Research Scientist at Center for Imaging Science (CIS), JHU from 2017-2018. Prior to joining the Johns Hopkins University, he was a Senior Research Scientist at the Institute of Robotics and Mechatronics (RM) of the German Aerospace Center (DLR), Germany. He received his Ph.D. in Computer Science at Vienna University of Technology, Austria in 2010.
Muyinatu A. Lediju Bell
Assistant Professor, Electrical and Computer Engineering
Joint Appointment, Biomedical Engineering
Ultrasound imaging, Photoacoustic imaging, Image quality improvements, Advanced beamforming methods, Light delivery systems, Medical robotics, Image-guided surgery,Technology development, Medical device design, Clinical translation
Muyinatu A. Lediju Bell leads a highly interdisciplinary research program that integrates optics, acoustics, robotics, electronics, and mechanics, as well as signal processing and medical device design, to engineer and deploy innovative biomedical imaging systems that simultaneously address unmet clinical needs and significantly improve the standard of patient care. As the director of the Photoacoustic and Ultrasonic Systems Engineering (PULSE) Lab, Dr. Bell develops theories, models, and simulations to investigate advanced beamforming techniques for improving ultrasonic and photoacoustic image quality. In parallel, she designs and builds novel light delivery systems for photoacoustic imaging and incorporates medical robots to improve operator maneuverability and enable standardized procedures for more personalized medicine. The technologies developed in her lab are then interfaced with patients to facilitate clinical translation. These technologies have applications in neurosurgical navigation, cardiovascular disease, women’s health, and cancer detection and treatment.
Dr. Bell obtained a PhD in Biomedical Engineering from Duke University (2012) and a BS in Mechanical Engineering with a minor in Biomedical Engineering from the Massachusetts Institute of Technology (2006). In addition, she spent a year abroad as an academic visitor at the Institute of Cancer Research and Royal Marsden Hospital in the United Kingdom (2009-2010). Prior to joining the faculty, Dr. Bell was a postdoctoral fellow with the Engineering Research Center for Computer-Integrated Surgical Systems and Technology at Johns Hopkins University. She published over 40 scientific journal articles and conference papers, holds a patent for SLSC beamforming, and is the recipient of numerous awards, grants, and fellowships, including the prestigious NIH K99/R00 Pathway to Independence Award. She was selected by MIT Technology Review as one of the top 35 Innovators Under 35 in the year 2016.
Affiliations: Malone Center for Engineering in Healthcare, Laboratory for Computational Sensing and Robotics (LCSR), Carnegie Center for Surgical Innovation
Selected Awards and Honors:
2016, MIT Technology Review, Innovator Under 35 (TR35)
2015, Best Paper Award Honorable Mention, IEEE International Conference on Advanced Robotics
2015, NIH K99/R00 Pathway to Independence Award
2013, Ford Foundation Postdoctoral Fellowship
2012, UNCF-Merck Postdoctoral Research Fellowship
2011, UNCF-Merck Graduate Research Dissertation Fellowship
2009, Whitaker International Fellowship
Assistant Professor of Radiology and Radiological Science
Thermal imaging; Elasticity; Ultrasound imaging; Image-guided therapy; Image-guided intervention
Emad M. Boctor, PhD, is an Assistant Professor in the Johns Hopkins Medicine Division of Medical Imaging Physics within the Department of Radiology and Radiological Science. He holds a joint appointment in the Whiting School of Engineering’s Department of Computer Science. Dr. Boctor earned a B.Sc. in biomedical engineering from Egypt’s Cairo University before earning master’s and doctoral degrees in computer science from Johns Hopkins University.
After joining the Hopkins faculty, he launched the Medical UltraSound Imaging and Intervention Collaboration (MUSiiC) Research Laboratory, which develops innovative ultrasound technologies for medical applications. He is also affiliated with the JHU Laboratory for Computational Sensing and Robotics (LCSR) and Computer Integrated Surgical Systems and Technology (CISST) Engineering Research Center, a National Science Foundation-designated center.
Dr. Boctor has authored and co-authored more than 25 journal articles, as well as the book, Enabling Technologies for Ultrasound Imaging in Computer-Assisted Intervention. He holds several patents, with more pending.
Secondary Appointment: Department of Electrical and Computer Engineering
Affiliations: Computer Integrated Surgical Systems and Technology (CISST) Engineering Research Center; Encephalitis Center
Jeremy D. Brown
John C. Malone Assistant Professor, Department of Mechanical Engineering
Haptic Feedback, Surgical robotics Upper-limb Prosthetics, Rehabilitation Robotics
Jeremy’s research focuses on the interface between humans and robots with a specific focus on medical applications and haptic feedback. In particular, he seeks to develop novel haptic interfaces to upper-limb prosthetics, minimally-invasive surgical robotics, and rehabilitation robots. His research sits at the intersection of engineering, biomechatronics, medicine, and psychophysics and uses methods from human perception, motor control, neurophysiology, and biomechanics.
He is a graduate of the Atlanta University Center’s Dual Degree Engineering Program, earning B.S. degrees in Applied Physics and Mechanical Engineering from Morehouse College and the University of Michigan, respectively. He also attained his M.S. and PhD degrees in Mechanical Engineering at the University of Michigan. He is currently a Postdoctoral Research Fellow at the University of Pennsylvania, where he is part of the Haptics Research Group in Penn’s General Robotics, Automation, Sensing, and Perception (GRASP) laboratory. His appointment at Hopkins will begin January 2017.
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Principal Scientist, Intelligent Systems Center at the Johns Hopkins Applied Physics Laboratory
Machine learning, deep learning, machine vision, object detection and recognition, deep reinforcement learning, medical image diagnostics
Phil Burlina holds joint faculty positions at the Johns Hopkins University School of Medicine Wilmer Eye Institute, the Malone Center for Healthcare Engineering and the Department of Computer Science. He is a principal scientist with the Johns Hopkins University Intelligent Systems Center at the Applied Physics Laboratory. His research spans several areas of machine intelligence including machine learning, deep learning, machine vision, object detection and recognition, deep reinforcement learning, medical image diagnostics, and addressing problems of making AI work in the wild such as zero/one/adaptive shot learning and unsupervised learning. His interests are in the development AI algorithms that are impactful for problem areas in medicine, robotics, and autonomous navigation.
Professor, Bloomberg School of Public Health
Neuroimaging, statistical methodology, data science, open education
Brian Caffo, PhD received his doctorate in statistics from the University of Florida in 2001 before joining the faculty at the Johns Hopkins Department of Biostatistics, where he became a full professor in 2013. He has pursued research in statistical computing, generalized linear mixed models, neuroimaging, functional magnetic resonance imaging, image processing and the analysis of big data. He created and led a team that won the ADHD-200 prediction competition and placed twelfth in the large Heritage Health prediction competition. He was the recipient the Presidential Early Career Award for Scientist and Engineers, the highest award given by the US government for early career researchers in STEM fields. He co-created and co-directs the SMART (www.smart-stats.org) group focusing on statistical methodology for biological signals. He also co-created and co-directs the Data Science Specialization, a popular MOOC mini degree on data analysis and computing having over three million enrollments. Dr. Caffo is the director of the graduate programs in Biostatistics and is the recipient of the Golden Apple teaching award and AMTRA mentoring awards.
Christopher G. Chute
Bloomberg Distinguished Professor Of Health Informatics
Professor Of Medicine
Chief Health Research Information Officer, Johns Hopkins Medicine
Clinical Data Representation, Ontology
Dr. Chute is the Bloomberg Distinguished Professor of Health Informatics, Professor of Medicine, Public Health, and Nursing at Johns Hopkins University, and Chief Research Information Officer for Johns Hopkins Medicine. He received his undergraduate and medical training at Brown University, internal medicine residency at Dartmouth, and doctoral training in Epidemiology at Harvard. He is Board Certified in Internal Medicine and Clinical Informatics, and a Fellow of the American College of Physicians, the American College of Epidemiology, and the American College of Medical Informatics. His career has focused on how we can represent clinical information to support analyses and inferencing, including comparative effectiveness analyses, decision support, best evidence discovery, and translational research. He has had a deep interest in semantic consistency, harmonized information models, and ontology. He became founding Chair of Biomedical Informatics at Mayo in 1988, retiring from Mayo in 2014, where he remains an emeritus Professor of Biomedical Informatics. He has been PI on a large portfolio of research including the HHS/Office of the National Coordinator (ONC) SHARP (Strategic Health IT Advanced Research Projects) on Secondary EHR Data Use, the ONC Beacon Community (Co-PI), the LexGrid projects, Mayo’s CTSA Informatics, and several NIH grants including one of the eMERGE centers from NGHRI, which focus upon genome wide association studies against shared phenotypes derived from electronic medical records. He has been active on many HIT standards efforts and currently chairs the World Health Organization (WHO) ICD-11 Revision.
room:Malone Hall, Suite 160
Executive Director, Johns Hopkins University Information Security Institute (JHUISI)
Associate Research Scientist, Department of Computer Science
Information Security, Distributed Systems, Fault-Tolerant Computing, Scheduling Optimization
Anton (Tony) Dahbura received the BSEE, MSEE, and PhD in Electrical Engineering and Computer Science from the Johns Hopkins University in 1981, 1982, and 1984, respectively.
From 1983 until 1996 he was a researcher at AT&T Bell Laboratories, was an Invited Lecturer in the Department of Computer Science at Princeton University, and served as Research Director of the Motorola Cambridge Research Center in Cambridge, Massachusetts.
Since 1996 he has led several entrepreneurial efforts in the areas of printing professional baseball operations and commercial real estate.
In January, 2012 he was named Executive Director of the Johns Hopkins University Information Security Institute in Baltimore.
From 2000-2002 he served as Chair of the Johns Hopkins University Engineering Alumni and in 2004 was the recipient of the Johns Hopkins Heritage Award for his service to the University. He chaired The Johns Hopkins Computer Science Department Advisory Board from 1998 until 2012 and also served on the Johns Hopkins University Whiting School of Engineering National Advisory Council during that time.
John C. Malone Associate Professor, Department of Computer Science
Machine Learning, Natural Language Processing, Social Media, Health Informatics
Mark Dredze is an Associate Professor in Computer Science at Johns Hopkins University, with affiliations in the Malone Center for Engineering in Healthcare, the Applied Physics Lab, the Human Language Technology Center of Excellence, the Center for Language and Speech Processing, and the Center for Population Health Information Technology. He holds an appointment in Health Science Informatics in the School of Medicine.
His research in natural language processing and machine learning has focused on graphical models, semi-supervised learning, information extraction, large-scale learning, and speech processing. His recent work includes health information applications, including information extraction from social media, biomedical, and clinical texts.
He earned his BS (’03) from Northwestern University; his MS (’04) from Yeshiva University; and his PhD (’09) from the University of Pennsylvania.
Research Scientist, Human Language Technology Center of Excellence, Health Sciences Informatics, School of Medicine
Ayse P. Gurses
Associate Professor, Department of Anesthesiology and Critical Care Medicine
Director, Center for Health Care Human Factors, Armstrong Institute for Patient Safety and Quality
Human Factors Engineering, Cognitive Engineering, User-centered Design, Patient Safety, Sociotechnical Systems Approach to Technology Design and Implementation, Care Coordination
Ayse P. Gurses, PhD, MS, MPH is an associate professor in the Armstrong Institute for Patient Safety and Quality and the Johns Hopkins School of Medicine. She is the founding Director of the Center for Health Care Human Factors in the Johns Hopkins Medicine. She has joint appointments in the Bloomberg School of Public Health and the Whiting School of Engineering at the Johns Hopkins University. Ayse is passionate about three critical and related public health topics: patient safety, health IT design and implementation, and occupational health and safety of healthcare workers. Formally trained in industrial and systems engineering and public health, she uses a combination of engineering and public health methodologies in her work. Ayse has been a principal or co-principal investigator on several federally funded research grants and contracts, totaling over $7M. Her current research efforts focus on improving patient-centered care and medication safety among elderly, health information technology design and implementation, improving coordination and teamwork in pediatric trauma, patient safety in the cardiac operating room, improving safety of transitions of care, increasing compliance with evidence-based guidelines to reduce infections, and clinician working conditions.
Ayse is the author of 50 peer-reviewed articles and 10 book chapters, and gave over 65 invited talks. She serves as the Scientific Editor of Applied Ergonomics, a top-level journal in the field of human factors engineering. She is also the Editor for the Sociotechnical Systems Analysis Department of the IIE Transactions in Healthcare Systems Engineering.
Ayse is the recipient of multiple awards including the Liberty Mutual Award for the Best Paper in the journal ‘Ergonomics,’ the International Ergonomics Association/ Liberty Mutual Best Paper Award in Occupational Safety and Ergonomics, and the University of Wisconsin-Madison Forward under 40 award. Nominated by the Human Factors and Ergonomics Society, Ayse is also a recipient of an Early Career Impact Award from the Federation of Associations in Behavioral and Brain Sciences (FABBS).
Gregory D. Hager
Director, Malone Center for Engineering in Healthcare
Mandell Bellmore Professor of Computer Science
Computer Vision, Robotics, Medical Robotics, Human-Machine Systems
Gregory D. Hager is the Mandell Bellmore Professor of Computer Science at Johns Hopkins University and Founding Director of the Malone Center for Engineering in Healthcare. Professor Hager received his BA in Mathematics and Computer Science Summa Cum Laude at Luther College (1983), and his MS (1986) and PhD (1988) from the University of Pennsylvania. He was a Fulbright Fellow at the University of Karlsruhe, and was on the faculty of Yale University prior to joining Johns Hopkins. Professor Hager’s research interests include collaborative and vision-based robotics, time-series analysis of image data, and medical applications of image analysis and robotics. He is also the former Chair of the Computing Community Consortium, a board member of the Computing Research Association, and is a member of the governing board of the International Federation of Robotics Research. Professor Hager has served on the editorial boards of IEEE TRO, IEEE PAMI, and IJCV. He is a fellow of the IEEE for his contributions to Vision-Based Robotics and a Fellow of the MICCAI Society for his contributions to imaging and his work on the analysis of surgical technical skill.
Secondary Appointments: Mechanical Engineering, Electrical & Computer Engineering
Director of Research, Emergency Medicine Residency
Assistant Professor of Emergency Medicine
Emergency medicine; Predictive analytics; Machine learning; Acute kidney injury
Dr. Jeremiah Hinson is an Assistant Professor of Emergency Medicine at the Johns Hopkins University School of Medicine. He earned his M.D. from Albert Einstein College of Medicine in Bronx, New York and completed an emergency medicine residency at Johns Hopkins. He also holds a Ph.D. in Molecular and Cellular Pathology from the University of North Carolina at Chapel Hill. Dr. Hinson is an active emergency medicine clinician, serving as an attending physician in the Emergency Departments of both Johns Hopkins Hospital and Johns Hopkins Bayview Medical Center.
His research interests include emergency department operations, acute kidney injury and infectious disease. He is particularly focused on the improvement of patient outcomes using data-driven methods. Dr. Hinson is part of a uniquely diverse team within the Department of Emergency Medicine that includes experts from the fields of biomedical engineering, data science and mathematical ecology – all focused on the common goal of improving care delivery for emergency department patients. Their team has created important cross-disciplinary partnerships and developed novel tools that enhance the practice of emergency medicine, including through an improved approach to emergency department triage and more accurate identification of risk factors for acute kidney injury. Their team is currently focused on using similar methods to improve infectious disease management in the ED and to predict and prevent acute kidney injury, work for which Dr. Hinson was awarded a grant from the Emergency Medicine Foundation.
Dr. Hinson is also Director of Research for the Emergency Medicine Residency at Johns Hopkins. In this role he oversees research education for emergency physicians in training, and has developed a unique longitudinal curriculum that builds skills in evidence-based medicine and research methodology over four years of residency training.
John C. Malone Assistant Professor, Department of Computer Science
Human-Robot Interaction, Human-Computer Interaction, Robotics
Chien-Ming Huang’ research focuses on building intuitive, interactive technologies to provide social, physical, and behavioral support for people. He is particularly passionate about using novel technologies to help special needs populations such as children with autism spectrum disorders. Dr. Huang completed his postdoctoral research at Yale University in 2017. He received his Ph.D. in Computer Science at the University of Wisconsin–Madison in 2015, his M.S. in Computer Science at the Georgia Institute of Technology in 2010, and his B.S. in Computer Science at National Chiao Tung University in Taiwan in 2006.
Co-Director, Radiology Artificial Intelligence Lab (RAIL)
Associate Professor, Johns Hopkins Radiology
Associate Professor (Courtesy) Carey School of Business
Artificial Intelligence, Deep Learning, Neuroradiology, Stroke, Aneurysms, AVMs, Neurovascular Wall Anatomy.
Ferdinand Hui is a CAST certified neurointerventional surgeon who trained with Dr. Jacques Dion at Emory University, one of the pioneers in the field of neuroendovascular surgery. Dr. Hui received undergraduate and medical training at the University of Virginia, and is an internationally recognized expert in the treatment of stroke with over 110 peer reviewed publications and book chapters. He joins Hopkins after seven years on faculty at the Cleveland Clinic, and has a strong interest in both technology and business development.
As of 2018, Dr. Hui has focused his energies towards artificial intelligence, recognizing the centrality of data science in the next phase of healthcare development, leading to the foundation of the RAIL group with colleagues Dr. Haris Sair and Dr. Paul Yi.
Director of the Brain, Learning, Animation, and Movement (
John C. Malone Professor of Neurology, Neuroscience, & Physical Medicine and Rehabilitation
Dr. John Krakauer is a neurologist and neuroscientist with an interest in the healthy and damaged motor system. He was an Associate Professor of Neurology and Co-Director of the Motor Performance Laboratory at Columbia University up until 2010. He is now the Director of the Center for Motor Learning and Brain Repair at Johns Hopkins University where he studies motor learning and control in patients after stroke and their relationship to functional recovery. There is a critical need to establish whether motor learning itself is affected after stroke and to determine which forms of motor learning should be the focus of rehabilitation strategies. He has made a number of observations/contributions to the study of motor learning in healthy subjects and motor recovery after stroke that suggest new directions for the treatment of impairment early after stroke.
Harold P. Lehmann
Director and Professor, Division of Health Sciences Informatics
Biostatistics; Evidence-based medicine; Decision making; Informatics; Bayesian
Dr. Harold Lehmann is a professor of health sciences informatics and of pediatrics at the Johns Hopkins University School of Medicine. He holds joint appointments in health policy and management and in international health at the Johns Hopkins Bloomberg School of Public Health. His areas of clinical expertise include adolescent medicine and general pediatrics. Dr. Lehmann serves as the director of research and training for the Division of Health Sciences Informatics.
He received his B.A. from Columbia College. He earned his M.D. from the Columbia University College of Physicians and Surgeons and his Ph.D. from Stanford University.
Dr. Lehmann’s research interests include medical informatics, evidence-based medicine and decision making, biostatics, decision analysis and Bayesian communications.
Dr. Lehmann is a fellow of the American College of Medical Informatics and the American Academy of Pediatrics. He is a member of the Society for Medical Decision Making and the American Medical Informatics Association.
Assistant Professor, Department of Emergency Medicine
Assistant Director For Research, Department of Emergency Medicine
Joint Appointment Civil Engineering
Consultant, Operations Integration, Johns Hopkins Health System
Systems Engineering in Healthcare, Optimization of Hospital Resources and Patient Outcomes
Dr. Levin is an Associate Professor in the Department of Emergency Medicine and holds joint appointments in the Department Civil Engineering (Whiting School of Engineering). He also works as a member of the Department of Operations Integration to forward operational, quality, and financial improvement initiatives within the Johns Hopkins Health System. He is Director and Leadership Council Chair of the Systems Institute. Upon finishing his Ph.D. in biomedical engineering at Vanderbilt University, Dr. Levin joined the Hopkins faculty in 2008. Dr. Levin’s research focuses on the use and development of systems engineering tools to study and improve the effectiveness, safety and efficiency of health care delivery. Research is directed toward determining how scarce health care resources may be managed and deployed to best care for patient populations. This includes an emphasis on systems engineering techniques aimed at improving quality of care, access to care, and medical decision-making.
Assistant Research Scientist
Surgical Education, Surgical Data Science, Simulation Training – Virtual Reality, Crowdsourcing, Human Motor and Cognitive Learning
Anand Malpani was born in 1989 in Mumbai, India. He received his B.Tech. in Electrical Engineering at Indian Institute of Technology (IIT) Bombay in 2010. He undertook a summer research project in medical imaging at the Institut de Recherche en Communications et Cybernétique de Nantes under the guidance of Vincent Ricordel in 2009. He joined the Ph.D. program in Computer Science at Johns Hopkins University in 2010. His dissertation under the guidance of Gregory D. Hager, focused on surgical education and simulation-based training with the goal of automated surgical coaching. During this work, he developed tools that combine crowdsourcing and machine learning to deliver coaching interventions in collaboration with multiple surgical faculty at the Johns Hopkins School of Medicine. He was awarded the Intuitive Surgical Student Fellowship in 2013, and the Link Foundation’s Modeling, Training and Simulation Fellowship in 2015. He was a summer research intern in the Simulation team at Intuitive Surgical Inc. (Sunnyvale, CA) in 2015.
He started as an Assistant Research Scientist at the Malone Center for Engineering in Healthcare in March 2017. His focus is to develop technologies that assist surgical trainees in their skill learning processes. He is working towards the realization of a fully automated virtual coach to make trainees proficient in fundamental surgical skills, and to do so outside of the operating room in a virtual reality simulation-based training environment.
Co-Director, Advanced Lipid Disorders Center
Assistant Professor of Medicine
Cardiovascular Risk Assessment; Dyslipidemia; Mobile Health Technology; Primary and secondary prevention of coronary heart disease; Individuals with a family history of heart disease; Improved strategies for management of cardiovascular risk factors; Preventive Cardiology
Dr. Seth S. Martin received his Bachelor of Science degree from Washington and Lee University, graduating summa cum laude and Phi Beta Kappa. He went on to receive his medical degree from the University of Pennsylvania and completed his internal medicine training at Duke University.
Dr. Martin then completed a cardiology fellowship at the Johns Hopkins University School of Medicine. He was designated the Henry R. Kravis & Marie-Josée Cardiovascular Fellow and also served as a Pollin Cardiovascular Prevention Fellow. Dr. Martin was awarded the Howard S. Silverman Research Award for originality and creativity in medical research. Additionally, he was recognized as an Up and Coming Future Star of Cardiology by the American College of Cardiology. During his fellowship, Dr. Martin obtained a Master of Health Science degree at the Johns Hopkins Bloomberg School of Public Health.
After completing the fellowship, Dr. Martin joined the Johns Hopkins Cardiology faculty. He is a core faculty member with the Ciccarone Center for the Prevention of Heart Disease and co-directs the Advanced Lipid Disorders Center. Dr. Martin also serves as an Associate Faculty member in the Welch Center for Prevention, Epidemiology, and Clinical Research. He has a longstanding interest in preventive cardiology, in particular cardiovascular risk assessment, lipidology, and mobile health technology.
Dr. Martin cares for patients with both acute and chronic illnesses and serves on the Hopkins Bedside Medicine Faculty. He is a Firm Faculty Clinical Coach and Educator with the Janeway Firm of the Osler Medical Residency. He also participates in teaching medical students and public health students.
Dr. Martin has published more than 150 articles in leading cardiology and medicine journals, as well as 12 book chapters. He contributed to an update to preventive cardiology published in Braunwald’s Heart Disease: A Textbook of Cardiovascular Medicine. He serves as Associate Editor for the American College of Cardiology’s CardioSource Dyslipidemia Clinical Community and as an Associate Editor for the Journal of Clinical Lipidology.
Diego A. Martinez
Assistant Professor, Department of Emergency Medicine and Division of Health Sciences Informatics
Machine and statistical learning, Bayesian statistics Mathematical programming and discrete-event simulation, with applications in medical decision-making, healthcare epidemiology, healthcare operations, and health policy
Diego A. Martinez is an assistant professor of emergency medicine and health sciences informatics at the Johns Hopkins University School of Medicine.
Dr. Martinez’s research focuses on systems design and optimization, with applications in medical decision-making, infectious disease dynamics, healthcare operations, and health policy. The guiding principle for his research is that a well-designed health system must be based on a sound mathematical model. His research utilizes a wide range of techniques including statistical and machine learning, Markov models, Bayesian inference, network science, optimization, and game theory.
Dr. Martinez obtained his BS (2010) in industrial engineering, summa cum laude, at the University of La Frontera, Chile, and his MS (2011) and PhD (2015) at the University of South Florida, where he worked with Jose Zayas-Castro in operations research and health systems. From 2015 to 2017, Dr. Martinez was a postdoctoral research fellow with the Johns Hopkins Center for Data Science in Emergency Medicine. He joined the Johns Hopkins faculty in 2017.
Location :Armstrong Institute, 750 E. Pratt St, 15th Floor
Assistant Director, Johns Hopkins Armstrong Institute for Patient Safety and Quality at Johns Hopkins Bayview
Head of Clinical Innovation at Johns Hopkins Armstrong Institute for Patient Safety and Quality
Assistant Professor of Medicine, Johns Hopkins University School of Medicine Division of Gastroenterology and Hepatology
Applying quality and safety focus to the development of technology solutions; current projects involve: examining quality and safety clinical outcomes; using technology to augment care coordination and communication; applying machine learning to aid in improved diagnosis; development of mobile/digital solutions to aid in clinical decision support; application of robotics in the clinical environment
Dr. Mathews leads efforts in clinical innovation at the Johns Hopkins Armstrong Institute for Patient Safety and Quality. He is focused on transforming hospitals and health systems into highly reliable organizations that excel in delivering high quality care. He works closely on strategic initiatives at AI including the development of learning healthcare systems, expansion of governance structures for quality and safety, and collaborations with strategic partners including Johns Hopkins University Applied Physics Lab and Ernst & Young. In addition, he works clinically in the Division of Gastroenterology at Johns Hopkins where he is immersed in quality and safety efforts. He earned his medical degree at the Johns Hopkins School of Medicine and completed his clinical training in Internal Medicine and Gastroenterology at Johns Hopkins. Prior to his medical training he also worked briefly in investment banking at Goldman Sachs in New York City. He is a graduate of the University of Virginia where he studied Economics as a Jefferson Scholar.
Associate Professor Of Medical Physics And Director Of Clinical Informatics, Department of Radiation Oncology and Molecular Radiation Sciences
Learning health systems in radiation oncology, Radiation dosimetry and treatment planning, Image guidance and adaptive radiotherapy
Dr. McNutt PhD is an Associate Professor and Director of Clinical Informatics in the Department of Radiation Oncology and Molecular Radiation Sciences. He has joint appointments in the Departments of Health Science Informatics and Computer Science in the Malone Center of Healthcare Engineering. Dr. McNutt’s primary research goal is to develop a learning health system in the context of radiation oncology for decision support and personalized radiotherapy.
Dr. McNutt has pioneered the development of a local level learning health system in radiation oncology with the Oncospace program for the last ten years. The system has been used to personalize radiation therapy by 1) predicting patient specific treatment plan quality (achievable radiation dose) and automating planning using the relationships between normal anatomy and tumor volumes based on the knowledgebase of prior patients; 2) predicting patients at risk of developing high grade treatment toxicities (e.g. xerostomia or dysphagia) or significant weight loss to guide care management; 3) predicting disease outcomes to guide intent and aggressiveness of treatment.
A primary component is creating a medical environment that allows us to measure how well we perform clinically for our patients. This requires incorporating medical IT infrastructure into the clinical setting and using it to measure the patient experience through clinician assessments, patient reported quality of life instruments and direct measures of patient condition and quantify it for analysis, trending and feedback. This has involved establishing workflows and human computer interfaces that enable the clinician to interact with the patients with minimal distraction while capturing the data. The effort has resulted in perpetual prospectively collected knowledgebases for head and neck, prostate, pancreas, and thoracic cancers that allow efficient analysis of clinical data to look at toxicity and outcome trends and how they correlate with the full 3D delivered radiation dosimetry within normal anatomy and targeted tumors.
Dr. McNutt also maintains research efforts in tradition medical physics applications to radiotherapy. He has worked to accelerate the treatment planning process developing a GPU based convolution/superposition dose computation algorithm for external beam radiation therapy that has been included in commercial systems for small animal radiation therapy planning as well as in quality assurance for treatment delivery in clinical systems.
Prior to his experience at Johns Hopkins, Dr. McNutt was the Director of Research and Advance Development for the Philips Radiation Oncology Systems developing the Pinnacle radiation therapy planning system. His work included intensity modulated radiotherapy planning and optimization, model based image segmentation and adaptive radiotherapy. This industrial work was done in a commercial software development environment following the FDA quality system regulations for medical device manufacturing and taught him best practices in software development and design to meet customer and user interface requirements.
Dr. McNutt maintains a strong interest in teaching post-doctoral fellows and students. Throughout his career he has mentored several post-docs and students in Medical Physics and Healthcare Informatics. He is also active teaching clinical residents in radiation oncology as well as students through lectures in Computer Science and Health Informatics.
Casey Overby Taylor
Assistant Professor Divisions of, General Internal Medicine and Health Sciences Informatics in the Department of Medicine
translational bioinformatics, human-centered technology design research, clinical decision support, knowledge-based methods
Dr. Casey Overby Taylor is Assistant Professor in the Divisions of General Internal Medicine and Health Sciences Informatics in the Department of Medicine at the Johns Hopkins University. She completed pre-doctoral National Library of Medicine biomedical informatics training and National Human Genome Research Institute genome sciences training fellowships at the University of Washington. She also completed a post-doctoral National Library of Medicine informatics training fellowship at Columbia University. As an informatics researcher, Dr. Taylor’s interests intersect at public health genomics and biomedical informatics. She is currently developing applications that support translation of genomic research to clinical and population-based healthcare settings and delivering health information and knowledge to the public. Dr. Taylor is also developing knowledge-based approaches to use Big Data such as electronic health record data for population health.
email :Send Message
: Phipps B112
Co-Director, Radiology Artificial Intelligence Lab (RAIL)
Associate Professor, Johns Hopkins Radiology
Interim Director, Division of Neuroradiology
Machine Learning, Neuroradiology, Functional Brain Imaging, Quantitative Imaging, Cognitive Neuroscience
Following completion of a dual major in Biology and Psychology and certificate in Neuroscience at Duke University, Dr. Sair continued his medical training at Duke University School of Medicine, where he studied the use of functional brain imaging in Alzheimer’s Disease under the tutelage of Jeffrey Petrella, MD. He completed residency training at Temple University and fellowship training at the Massachusetts General Hospital, and joined the faculty of Johns Hopkins in 2010. Dr. Sair is internationally recognized for his research exploring the use of MRI-based functional connectivity of the brain and translating it to clinical use.
Dr. Sair has active collaborations with a wide network of physicians and scientists, exploring functional imaging in topics such as preoperative brain mapping, traumatic brain injury, neurodegenerative disorders, and clinical prognosis. Together with Drs. Ferdinand Hui and Paul Yi, Dr. Sair co-founded the Radiology Artificial Intelligence Lab.
John C. Malone Assistant Professor, Department of Computer Science
Machine learning, Computational health informatics, Probabilistic Methods, Time Series Models, Predictive modeling in healthcare.
Information extraction in domains with structured and unstructured data (e.g., text, sensing devices, electronic health records, smart rooms)
Saria’s interests span machine learning, computational statistics, and its applications to domains where one has to draw inferences from observing a complex, real-world system evolve over time. The emphasis of her research is on Bayesian and probabilistic graphical modeling approaches for addressing challenges associated with modeling and prediction in real-world temporal systems. In the last seven years, she has been particularly drawn to computational solutions for problems in health informatics (see her recent article on this topic) as she sees a tremendous opportunity there for high impact work.
Prior to joining Johns Hopkins, she earned her PhD and Masters at Stanford in Computer Science working with Dr. Daphne Koller. She also spent a year at Harvard University collaborating with Dr. Ken Mandl and Dr. Zak Kohane as an NSF Computing Innovation Fellow. While in the valley, she also spent time as an early employee at Aster Data Systems, a big data startup acquired by Teradata. She enjoys consulting and advising data-related startups. She is an investor and an informal advisor to Patient Ping.
She is originally from Darjeeling, India and (jokingly) adds that she can be bribed with good tea.
Secondary Appointments: Health Policy and Management, Bloomberg School of Public Health, Institute for Computational Medicine, Center of Population Health Information Technology, Center for Language and Speech Processing, Laboratory for Computational Sensing and Robotics, Armstrong Institute for Patient Safety & Quality.
John C. Malone Assistant Professor, Department of Computer Science
Causal inference and missing data, Graphical models, Longitudinal data analysis in medicine and epidemiology
Ilya Shpitser received his PhD from UCLA in 2008, under the supervision of Judea Pearl. From 2008 to to 2012 he was a Research Associate in the causal inference group at the Harvard School of Public Health. From 2013 to 2015, he was a Lecturer (Assistant Professor) of Statistics at the University of Southampton. His research includes all areas of causal inference and missing data, particularly using graphical models. Much of the recent applications of his work involved teasing out causation from association in observational medical data.
Assistant Professor, Departments of Civil Engineering and Applied Mathematics & Statistics
Multilevel and Multiobjective Optimization to Improve Patient Flow and Staffing, Simulation of Perioperative Care, Predicting Heart Failure Readmission after Discharge, Analysis of the Clinical Trials System, Predicting Early Discharge from Medical Units, Global vaccine development
Sauleh Siddiqui is an Assistant Professor of Civil Engineering with an appointment in Applied Mathematics & Statistics at Johns Hopkins University. He is also a Research Fellow at the and the German Economic Research Institute (DIW Berlin) and Visiting Lecturer at the Technical University of Berlin. He is an Associate Editor of the journal Optimization and Engineering and is currently serving as Chair for the Energy Cluster and Vice Chair for Linear and Conic Optimization for The Institute for Operations Research and Management Science. His research is on formulating and solving optimization and game theory models applicable to large-scale systems. Such systems arise when modeling problems in energy and environmental markets, public health, and healthcare. He also models engineering design and develops novel algorithms along with supporting mathematical theory.
He has received research funding from the World Bank, Johns Hopkins Health System, the Environment Energy Sustainability Health Institute, and the Norwegian Research Council. In addition, he has collaborated on projects with the International Council on Clean Transportation, Institute of Medicine, National Academy of Engineering, and Bikemore. These projects have included modeling energy and climate markets, patient flow and staffing in hospitals, global vaccination strategies, and urban transport networks.
He received an A.B. in Mathematics and Public Policy from Franklin & Marshall College and a Ph.D. in Applied Mathematics & Statistics, and Scientific Computation from the University of Maryland, College Park.
Jeffrey H. Siewerdsen
John C. Malone Professor of Biomedical Engineering, Computer Science, Radiology And Neurosurgery
Director, Carnegie Center for Surgical Innovation
Medical imaging, Computed tomography (CT) and cone-beam CT, Multi-modality image registration, Image analysis, Image-guided interventions, Image-guided surgery, Patient safety
Dr. Siewerdsen leads a program for translational research in medical imaging and image-guided interventions at Johns Hopkins University. Primary research interests include the development of new imaging technologies, 3D imaging methods, and image registration, with a focus on CT and cone-beam CT combined with other modalities for improved diagnostic accuracy and high-precision intervention. Collaborations and areas of major clinical interest include Neurosurgery, Orthopaedic Surgery, Otolaryngology, Radiation Oncology, Interventional Radiology, and Diagnostic Radiology. His work includes early development of flat-panel x-ray detectors and pioneering work in cone-beam CT, including intraoperative C-arms, dedicated cone-beam CT systems for radiology, and the first cone-beam CT system for image-guided radiation therapy, now standard of care. He worked previously at William Beaumont Hospital Research Institute (Royal Oak MI) and the Ontario Cancer Institute / University of Toronto (Toronto ON) after receiving his PhD in Physics from the University of Michigan in 1998.
Assistant Professor Of Ophthalmology, The Johns Hopkins Wilmer Eye Institute
Surgical Education, Cornea and Cataract Surgery
Shameema Sikder, M.D., founding medical director of the Wilmer Eye Institute at Bethesda, is an assistant professor of ophthalmology. She specializes in corneal disorders, including Fuchs dystrophy and keratoconus, complex cataracts, and external eye diseases. Dr. Sikder’s clinical interests include surgical treatments for corneal diseases, such as endothelial keratoplasty (DMEK, DSAEK), complex cataracts, and treatments for keratoconus.
Dr. Sikder is the director of the Center of Excellence for Ophthalmic Surgical Education and Training (OphSET) at the Johns Hopkins Hospital. She has a particular interest in surgical education and is working on technologies that could be implemented at the international level to improve the level of ophthalmic surgical care.
Dr. Sikder received her M.D. degree from the University of Arizona. She completed her ophthalmology residency at the Wilmer Eye Institute and fellowship in cornea and refractive disease at the Moran Eye Institute in Salt Lake City, Utah, where she received the Claes Dohlman Fellow of the Year Award, recognizing the most distinguished cornea fellow in the nation. Dr. Sikder returned to Wilmer in 2011 and served as assistant chief of service (chief resident) and associate director of ocular trauma.
- University of Arizona, College of Medicine Excellence in Research Award
- University of Arizona, College of Medicine Research Distinction Award
- Claes Dohlman, M.D. Cornea Fellow of the Year Award (National)
- Mitchell Research Prize, Wilmer Resident Association
- Honor Society Member, Alpha Omega Alpha
- Intern of the Year, Tucson Hospitals Medical Education Program
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Assistant Research Scientist
Machine learning for healthcare; probabilistic graphical models; approximate posterior inference; statistical modeling
Hossein Soleimani is a research scientist at the computer science department at Johns Hopkins University. He received the Ph.D. degree from Pennsylvania State University, PA, in 2016. His research interests include machine learning, healthcare, probabilistic graphical models, and statistical modeling. He specializes in building intelligent clinical decision support tools and personalized risk prediction systems.
Russell H. Taylor
John C. Malone Professor, Department of Computer Science
Computer-Integrated Interventional Medicine, Medical Robotics, Medical Imaging and Modeling
Russell H. Taylor received his Ph.D. in Computer Science from Stanford in 1976. He joined IBM Research in 1976, where he developed the AML robot language and managed the Automation Technology Department and (later) the Computer-Assisted Surgery Group before moving in 1995 to Johns Hopkins, where he is the John C. Malone Professor of Computer Science with joint appointments in Mechanical Engineering, Radiology, and Surgery and is also Director of the Engineering Research Center for Computer-Integrated Surgical Systems and Technology (CISST ERC) and of the Laboratory for Computational Sensing and Robotics (LCSR). He is the author of over 400 peer-reviewed publications and book chapters, a Member of the National Academy of Inventors, a Fellow of the IEEE, of the AIMBE, of the MICCAI Society, and of the Engineering School of the University of Tokyo. He is also a recipient of numerous awards, including the Maurice Müller Award for Excellence in Computer-Assisted Orthopaedic Surgery the IEEE Robotics Pioneer Award, the MICCAI Society Enduring Impact Award, the IEEE EMBS Technical Field Award, and the Honda Prize.
Secondary Appointments: Mechanical Engineering, Radiology, Surgery
Assistant Research Professor of Computer Science
Imaging Systems; machine learning; augmented reality
Mathias Unberath is currently an Assistant Research Professor in the Department of Computer Science at Johns Hopkins University, and is affiliated with the Laboratory for Computational Sensing and Robotics and the Malone Center for Engineering in Healthcare. Previously, Mathias was a postdoctoral fellow in the Laboratory for Computational Sensing and Robotics at Johns Hopkins University working on various topics in the realm of image-guided surgery. He holds a BSc in Physics, a MSc in Optical Technologies, and a PhD in Computer Science from the Friedrich-Alexander-Universität Erlangen-Nürnberg from which he graduated summa cum laude in 2017. He was an ERASMUS scholar at the University of Eastern Finland in 2011 and DAAD fellow at Stanford University throughout 2014.
Mathias’ research consolidates efforts in computer vision, medical physics, and medicine to develop surgeon-centric, end-to-end computer-assisted solutions for image-guided surgery. Currently, he is particularly interested in advancing interventional image processing to tailor image acquisition and augmented reality environments to the clinical task.
room:Malone Suite 340
Assistant Research Professor
I’m a medical doctor with surgical training and an epidemiologist. My research interests overlap several disciplines including clinical trials, different areas of surgery, epidemiology, biostatistics, and machine learning. I work on research related to measuring surgical skill and competency, technology for acquisition of motor and cognitive skill by surgeons, methods for fair comparisons of surgical interventions to estimate treatment effectiveness, analytics for surgical processes, and robotic assistance for skill acquisition and surgical coaching.
John C. Malone Assistant Professor, Electrical and Computer Engineering
Functional Neuroimaging (fMRI, EEG), Machine Learning & Probabilistic Inference, Network Modeling of the Brain, Integration of Imaging, Genetics and Behavioral Data
Archana Venkataraman’s research lies at the intersection of multimodal integration, network modeling and clinical neuroscience. Her main objective is to combine analytical tools, such as probabilistic inference and network theory, with neuroscientific findings in order to characterize complex processes within the brain. Her methodology has yielded novel insights into autism, schizophrenia and epilepsy, with the long-term goal of informing patient treatment and care. Archana received her Bachelors, Masters and PhD in Electrical Engineering from MIT in 2006, 2007 and 2012 respectively. She was a postdoctoral associate at both MIT and Yale prior to arriving at Johns Hopkins. Archana is affiliated with the Malone Center for Engineering in Healthcare at JHU, which aims to improve the quality and efficacy of clinical interventions.
- 2016: Council of Early Career Investigators in Imaging (CECI2) Award
- 2013: CHDI Grant on Network Models for HD
- 2012: MIT Lincoln Labs Campus Collaboration Award
- 2011: NIH Advanced Multimodal Neuroimaging Training Program
- 2007: National Defense Science and Engineering Graduate Fellowship (NDSEG)
- 2007: Siebel Scholarship
- 2006: MIT Provost Presidential Fellowship
Assistant Research Scientist
Human motor skill learning and motor control, Brain repair and rehabilitation
Jing Xu received her PhD (2011) in Psychology at University of California, Berkeley. Her PhD work was done in the laboratories of Cognition and Action, directed by Prof. Richard B. Ivry, and Computational Cognitive Science, directed by Prof. Thomas Griffiths. Her research was focused on human learning and control of movement, and human categorization.
She joined Johns Hopkins University in 2011, and worked with Dr. John W. Krakauer as a post-doctoral fellow. She was the lead post-doc of a longitudinal, multi-center project aimed at uncovering the biomarkers of stroke patients’ motor recovery, using multiple advanced neuroscience and clinical assessment methods. Her research has established that the human hand strength and dexterity recovery after stroke are mediated by separate biological systems.
Xu joined the Malone Center as an Assistant Research Scientist in 2017. Her primary research interest is to understand how people learn a motor skill. She uses research tools such as behavioral experiments, fMRI, TMS, and computation modeling questions to answer important questions about cognitive-motor representations, learning and control principles, recovery mechanisms after brain injuries, and strategies to enhance motor skills.
Co-Director, Radiology Artificial Intelligence Lab (RAIL)
Resident Physician, Johns Hopkins Radiology
Artificial Intelligence, Deep Learning, Neuroradiology, Musculoskeletal Radiology
Paul Yi is Resident Physician in Radiology at Johns Hopkins University School of Medicine and Founding Co-Director of the Radiology Artificial Intelligence Lab (RAIL). Dr. Yi received his BA in Medical Sciences (Summa Cum Laude) and MD from Boston University through the Seven-Year Accelerated Medical Program. Prior to a career in Radiology, he completed a research fellowship in total joint arthroplasty at Rush University Medical Center with Dr. Craig Della Valle and 2 years of Orthopaedic Surgery residency training at the University of California, San Francisco (UCSF), where his prior research expertise was in the diagnosis of periprosthetic joint infection.
Dr. Yi’s current research interests include the development and application of artificial intelligence and deep learning towards medical imaging applications, with particular interest in neuroradiology and musculoskeletal radiology. Dr. Yi is the recipient of numerous national research awards, both within the fields of Radiology and Orthopaedic Surgery, including Cum Laude awards from the Radiological Society of North America (RSNA) and American Roentgen Ray Society (ARRS) and the Frank Stinchfield Award from the Hip Society. He has published over 50 articles in the peer-reviewed medical literature and has presented over 100 research presentations at the national or international level.
Bloomberg Distinguished Professor of Cognitive Science and Computer Science
Computational models of vision; Mathematical models of cognition; Artificial intelligence; Neural networks
Dr. Yuille is a mathematician and computer scientist interested in the biology of vision. His research spans several disciplines including computer vision, vision science, and neuroscience.
Alan Yuille received a BA degree in mathematics from the University of Cambridge in 1976. His PhD on theoretical physics, supervised by Prof. S.W. Hawking, was approved in 1981. He was a research scientist in the Artificial Intelligence Laboratory at MIT and the Division of Applied Sciences at Harvard University from 1982 to 1988. He served as an assistant and associate professor at Harvard until 1996. He was a senior research scientist at the Smith-Kettlewell Eye Research Institute from 1996 to 2002. He joined the University of California, Los Angeles, as a full professor with joint appointments in computer science, psychiatry, and psychology. He moved to Johns Hopkins University in January 2016 where he was appointed a Bloomberg Distinguished Professor. He holds joint appointments in the Departments of Cognitive Science and Computer Science. His research interests include computational models of vision, mathematical models of cognition, medical image analysis, and artificial intelligence and neural networks.
He directs the research group on Computational Cognition, Vision, and Learning (CCVL). He is affiliated with the Center for Brains, Minds and Machines, and the NSF Expedition in Computing, Visual Cortex on Silicon.
John C. Malone Professor of Biostatistics
Biostatistics; Environmental statistics; Epidemiologic statistics; Bayesian statistics; Hierarchical models; Longitudinal data analysis; Regression analysis; Time series analysis; International health
Scott L. Zeger is Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health and of Medicine at the School of Medicine. From 2008-2013, he was Vice Provost for Research to represent the university in all matters related to the research and scholarship of its faculty and students. In 2009, Dr. Zeger was interim Provost of the University. From 2012-2016, Dr. Zeger has was the inaugural director of Hopkins inHealth, a signature initiative of the of Johns Hopkins University, Health System and Applied Physics Laboratory to bring modern biological and data sciences to the practice of American medicine and public health.
Professor Zeger is author or co-author of 3 books and more than 200 scientific articles. Science Watch identified Dr. Zeger as one the 25 most cited mathematical scientists. He conducts statistical research on regression analysis for correlated responses as occur in surveys, time series, longitudinal or genetics studies. He has made substantive contributions to our understanding of the health effects of smoking and air pollution, infectious diseases in children, and other topics.
Professor Zeger has served as expert witness to the U.S. Department of Justice and several states in their civil suits against the tobacco industry and as a member of the Board of Scientific Advisors for the Merck Research Laboratory. He is a member of the Springer-Verlag editorial board for statistics and was the founding co-editor of the Oxford University Press journal Biostatistics. Dr. Zeger has been awarded an honorary doctorate from Lancaster University in England, elected Member of the National Academy of Sciences’ Institute of Medicine, Fellow of the American Association for the Advancement of Science and of the American Statistical Association. He and colleague Kung-Yee Liang were jointly awarded the 2015 Karl Pearson Prize by the International Statistical Institute. The Bloomberg School Student Assembly has awarded Dr. Zeger with Golden Apples for excellence in teaching. In 2018, he was the inaugural recipient of the Bloomberg School’s Faculty Mentoring Award.