Park Lab: Program for Hospital and Intensive Care Informatics
Location and Contact Information
- Email sp3291@cumc.columbia.edu
Principal Investigator
Our mission is to derive actionable information from healthcare data to improve efficiency and quality of care in hospitalized or critically ill patients. Using monitoring devices and digitized patient data, we implement statistical and machine learning decision support tools to detect physiologic state changes and identify opportunities for early intervention to change outcome.
Ongoing projects include:
- Detecting delayed cerebral ischemia in subarachnoid hemorrhage patients
- Goal-directed perfusion in subarachnoid hemorrhage patients leveraging autoregulation indices and adjunctive neuromonitoring
- Intracranial pressure waveform analysis to detect ventriculitis
- Automated time-varying measures to predict shunt dependency in acute hydrocephalus
- Noninvasive monitoring to risk-stratify chronic subdural hemorrhage patients
- Neuromonitoring to optimize post-operative care of pediatric single ventricle heart surgery patients
- Noninvasive estimation of intracranial pressure
Publications
- Complete bibliography is listed on the National Library of Medicine website.
- Please click here to download a list of selected publications as a PDF file.
Alumni
Isaac Lee, BS
Kalijah Terilli
Lab Members
Murad Megjhani, PhD
- Associate Research Scientist in the Department of Neurology
Murad Megjhani earned his PhD in Electrical and Computer Engineering from the University of Houston. His research focuses on utilizing signal processing and machine learning to improve health outcomes, especially in neurological conditions. He has developed predictive models for real-time disease detection and management, specifically targeting delayed cerebral ischemia. His efforts have resulted in multiple publications in esteemed medical journals. Currently, he is engaged in the development of deep learning models for predicting intracranial pressure waveforms.
Dan Nametz, BS
- Research Assistant in the Department of Neurology
Daniel Nametz earned his Bachelors of Science in Computer Science in 2021 from Fordham University. As a Research Assistant in Park Lab, he focused on recruitment for studies, data collection and analysis. He now manages two NIH funded grants, including a multicenter effort utilizing a Federated Learning framework.
Tammam Alalqum, BS
- Research Assistant
Tammam Alalqum (He/Him) graduated from University of California, Berkeley in 2023 with a double major in Cognitive Science and Economics. With an unwavering passion for computational neuroscience, he joined the Park Lab as a research assistant. Tammam envisions a future in graduate school, where he will continue to delve into the captivating realm of neuroscience, furthering his understanding of the human mind.
Giselle Grassi, BA
- Research Assistant
Giselle Grassi (She/Her) earned her Bachelors in Neuroscience from Princeton University in 2024 where she first became passionate about conducting research on the applications of Artificial Intelligence in clinical neuroscience. Giselle is motivated to explore the growing roles of brain computer interfaces in disease pathology as well as AI alignment and ethics in the healthcare industry. Her position as a research assistant in The Park Lab allows her to develop her computational understanding of neuroscience and machine learning techniques as well as provides an engaging hands-on clinical environment. Giselle is looking to continue her studies and envisions pursuing an MD/PhD in her future.
Soon Bin Kwon, PhD
- Postdoctoral Research Scientist in the Department of Neurology
Soon Bin Kwon earned his BS degree in Biomedical Engineering at Boston University. He continued his studies in Biomedical Engineering and completed a PhD degree at Seoul National University, in Korea. After a year as a research professor at Seoul National University Hospital, he joined the lab as a Postdoctoral Research Scientist. His current studies focus on waveform analysis and multi-modal classification algorithms.
Jiayu Yao, PhD
- Postdoctoral Research Scientist in the Data Science Institute
Jiayu Yao obtained her PhD in Computer Science at Harvard University. She previously worked as a joint postdoc at Gladstone Institutes and Stanford University and is currently a Columbia University Data Science Institute postdoc advised by Soojin Park and Shalmali Joshi. Her research focuses on identifying and tackling challenges along the machine learning for healthcare pipeline, with the goal of bridging the gap between machine learning methodology and clinical applications. Specifically, her current research includes developing pipelines for data processing and exploration, developing models that are robust under real-life limitations (e.g. data heterogeneity, data scarcity and domain knowledge constraints), as well as developing quantitative and qualitative assessments of models for specific downstream desiderata (e.g. statistical power, human-interpretability).
Kayla Schiffer-Kane
- PhD Student, Biomedical Informatics
Kayla Schiffer-Kane earned her BA at Barnard College, where she studied Medical Humanities and Computer Science. During her time at Barnard, her research focused on using patient-generated data to support shared decision-making with care providers for poorly understood chronic diseases. She then transitioned to industry, working at Veeva Analytics studying contemporary healthcare trends and their impact on consumer drug patterns. Currently, she is a PhD student in the Department of Biomedical Informatics at Columbia University. She is a member of the Student Editorial Board for the Journal of Biomedical Informatics. She is also the Student Representative for the American Medical Informatics Association’s Knowledge Discovery and Data Mining working group. Her research focus is on the use of machine learning-driven clinical decision support systems in critical care. She is interested in exploring how disease prediction models align with clinical decision-making.
Vivian Li
- PhD Student, Biomedical Informatics
Vivian Li is a first-year Biomedical Informatics PhD student rotating in Park Lab. She graduated from Vanderbilt University in 2023 majoring in computer science and math. She is interested in the application of machine learning for healthcare settings and is currently exploring methods for real-time monitoring of intracranial pressure.
Zach Levin
- Medical Student
Zach Levin is a second-year medical student at Columbia VP&S in the Park Lab. He graduated from Brown University in 2022 with a bachelor's in Neuroscience. His past research used machine learning and transcriptomic tools to predict outcomes in patients with Normal Pressure Hydrocephalus. Zach currently uses transcranial Doppler (TCD) to identify physiological changes and predict outcomes in patients with subarachnoid hemorrhage and chronic subdural hematomas.
Zoe Zhou, MS
- Data Analyst; Graduate Student, Fu Foundation School of Engineering and Applied Science
Zoe Zhou earned her Master of Science in Business Analytics from Columbia University. As a data analyst in Dr. Park's lab, she manages a multicenter federated learning project, develops and maintains data pipelines, workflows, technical documentation, and provides technical support. She supports data acquisition, storage, and analysis of critical care data types. Looking ahead, Zoe commits to further supporting the development of cross-site disease prediction models.
Brandon Lau
- Software Engineer, Biomedical Informatics
Brandon Lau earned his Bachelors of Science in Computer Science from the University of Buffalo. As a software engineer across two different teams in Columbia, he has a diverse background in software engineering, cloud architecture, and healthcare technologies. With his expertise in EHR integrations, HL7 FHIR standards, and medical data processing, Brandon has focused on building scalable cloud solutions that streamline medical workflows and enhance patient data management. He is committed to creating impactful, user-centered solutions that improve healthcare efficiency and system reliability.
Benjamin Ranard, MD, MSHP
- Assistant Professor of Medicine, Division of Pulmonary, Allergy, and Critical Care Medicine
Dr. Ranard is an intensivist specializing in the care of patients in the Medical Intensive Care Unit (MICU) and a physician-scientist. He earned both his medical degree and a Master of Science in Health Policy Research from the University of Pennsylvania Perelman School of Medicine. He then completed his internal medicine residency at Duke University, where he participated in the Learning Health System Training Program and chaired the Graduate Medical Education Patient Safety and Quality Council. In 2022, Dr. Ranard completed his Pulmonary and Critical Care Medicine fellowship at Columbia University where he also served as Chief Fellow and a Patient Safety Research Fellow. During his fellowship he joined Dr. Park’s Program for Hospital and Intensive Care Informatics (PHICI), where he contributed to phenotyping and modeling patients with COVID-19. He is now working with PHICI to apply machine learning to other patient care issues such as predicting patient deterioration. His interests include informatics, learning health systems, and machine learning, and he is committed to creating, implementing, and evaluating clinical decision support systems to improve patient care. His current work focuses on developing machine learning-based predictive models and exploring their potential applications in patient care.
Bennett Weinerman, MD
- Assistant Professor in the Pediatric Critical Care, Department of Pediatrics
Dr. Weinerman is a Pediatric Critical Care physician. He completed his pediatric residency at the Children’s Hospital of Philadelphia and his fellowship in Pediatric Critical Care at Columbia University, Morgan Stanley Children’s Hospital in New York City. His research applies to non-invasive monitoring modalities to better predict patient physiology and outcomes. He leverages routine patient monitoring systems to provide insight into patients who are at high risk for clinical deterioration and for those patients in the pediatric cardiac intensive care unit.
Son H. McLaren, MD
- Assistant Professor of Pediatrics (in Emergency Medicine), Department of Emergency Medicine, Division of Pediatric Emergency Medicine
Dr. McLaren is an Assistant Professor of Pediatrics (in Emergency Medicine) at Columbia University Irving Medical Center. She is a graduate of the Weill Cornell Medical College and earned a Master of Science degree from Columbia University Mailman School of Public Health. She completed her residency in pediatrics at NewYork-Presbyterian Hospital / Weill Cornell Medicine and fellowship training in pediatric emergency medicine at NewYork-Presbyterian Morgan Stanley Children’s Hospital / Columbia University Irving Medical Center. Dr. McLaren’s primary research focuses on respiratory viral infections and the use of novel diagnostic tests to improve clinical outcomes in young infants and vulnerable children. She is also interested in the use of bedside physiologic monitors to improve clinical decision making in the emergency department.
Eugene Kim, MD
- Assistant Professor in the Department of Emergency Medicine
Dr. Kim is an Assistant Professor of Emergency Medicine at Columbia University Irving Medical Center, where he serves as the Assistant Director of Informatics and Analytics. He completed his residency in Emergency Medicine at Mount Sinai Hospital and a fellowship in Clinical Informatics at Beth Israel Deaconess Medical Center. He is interested in the use of electronic health records and physiologic data to improve quality of care in the emergency department.
Yunseo Ku, PhD
- Visiting Associate Professor of Neurological Sciences (in Neurology)
Yunseo Ku received his B.S. degree in electrical engineering and his M.S. and Ph.D. degrees in biomedical engineering from Seoul National University, Seoul, South Korea, in 2004, 2008, and 2017, respectively. From 2008 to 2018, he conducted research on healthcare wearables at Samsung Advanced Institute of Technology, Suwon, South Korea. He is currently an associate professor with the department of biomedical engineering, college of medicine, Chungnam National University, Daejeon, South Korea. His research interests include medical wearables for biosignal-based diagnosis and treatment, and machine learning algorithms for early diagnosis and prognosis of diseases. In September 2023, he joined the Park laboratory at Columbia University Irving Medical Center as a visiting professor. His current work focuses primarily on data-driven approaches utilizing noninvasive signals in neurocritical care.