Shaping the Future of Health Through Data, Computation and Systems-level Insights
Situated at the intersection of methodology innovation and patient care, the Department of Quantitative and Systems Health Sciences harnesses complex biomedical and clinical data through advanced quantitative and systems approaches to empower discovery, optimize health systems and deliver precision medicine.
The department brings together experts in computational science, biomedical informatics, biostatistics, bioinformatics, bioimaging, epidemiology, and health services research to address complex challenges in health and medicine.
The department translates methodology innovations to end-to-end research support and discoveries into meaningful improvements in medicine and healthcare. Through research, collaboration and education, the department prepares the next generation of scientists to lead this rapidly evolving field.
Areas of Focus
Advance computational methods, multi-modal AI and multi-agent AI frameworks in biomedicine and practical quantitative methods for population-scale inference.
Integrate large health datasets with social, spatial and environmental context to strengthen healthcare systems and help ensure equal access to high-quality disease prevention, diagnosis and treatment.
Explore translational methodology, AI-empowered core services and practice integration, with the goal of achieving measurable impact in elevating patient care and improving health outcomes.
Study how interdisciplinary research teams form, collaborate and succeed, while building partnerships that apply evidence-based team science to complex, systemic health challenges.
Foster the responsible adoption of AI by promoting algorithmic safety, protecting data privacy and supporting transparent clinical AI deployment.
Education
The Department of Quantitative and Systems Health Sciences prepares the next generation of academic and clinical leaders in quantitative and systems sciences for the future of health.
The department offers training through degree programs in health data science, health outcomes, AI in medicine, biomedical informatics, biomedical engineering and related fields, and provides access to hands-on research assistantships.
Trainees work alongside experts in medicine, population health, biomedical engineering, computer science, informatics and statistics through collaborative research projects, seminars and mentorship.
Patient Care
The Department of Quantitative and Systems Health Sciences advances healthcare delivery through systems engineering, digital health technologies and data-driven clinical decision-making.
The department collaborates with clinical teams to develop multimodal AI tools that integrate medical imaging, clinical text, physiologic signals and other data to support disease detection, diagnosis, risk assessment and treatment decisions.
The department is involved in the development and evaluation of patient-centered remote monitoring systems, wearable technologies and mobile health applications that enable continuous measurement, timely intervention and personalized care.
The department partners directly with clinical teams to integrate and evaluate predictive risk stratification algorithms, clinical decision support systems, and automated clinical documentation workflows, using data from routine care to continually improve healthcare delivery.
Research
The department leads methodological and translational research that develops and applies quantitative approaches to complex biological, clinical and operational challenges in healthcare.
The department develops specialized research core services, which includes study design consultation, advanced data engineering, high-performance computing and application development.
The department builds dynamic, multi-scale computational simulation models of patient trajectories and hospital operations to test interventions and optimize outcomes.
The department applies decision science and quantitative modeling to guide the design and implementation of cancer interventions. This includes helping to improve screening, diagnosis and treatment for cervical, breast and colorectal cancer.
The department develops AI and quantitative imaging methods for imaging modalities to reveal disease characteristics. The goal is to combine imaging, clinical, molecular, physiological and population-level data to help improve diagnosis, predict outcomes and support personalized care.
The department seeks to link high-resolution environmental exposure data with health-system and other real-world data to study respiratory disease and other health conditions influenced by environmental factors.
The department seeks to advance the science of data and information management in biomedical discovery and healthcare delivery.
By developing care models that reliably address the 4Ms (What Matters, Medication, Mentation, and Mobility), the department aims to help improve health outcomes and care experiences for older adults.
The department uses large-scale data to study how social and environmental context shapes infectious-disease risk, including viral respiratory illness and asthma burden.
The department grounds the design of AI-enabled care in the needs, values and agency of patients, families and care teams, supporting shared decision-making and ensuring that technology strengthens human relationships and experiences in healthcare.
The department studies the structures, processes and technologies that enable interdisciplinary teams to collaborate effectively and leverages the findings to strengthen scientific partnerships and accelerate progress on complex health challenges.