Cyber-Physical Systems Laboratory
The Cyber-Physical Systems Laboratory (CPSL) at Washington University performs cutting-edge research on AI and machine learning for healthcare, mobile health, real-time systems, Internet of Things, and cyber-physical systems that cross-cut computing and other disciplines.
What is Cyber-Physical Systems (CPS)? CPS is a holistic design methodology that co-designs the cyber and physical aspects of networked embedded systems. By breaking the barrier between cyber and physical designs, CPS will result in drastic improvement to networked embedded systems and new systems that we cannot build today. This talk elaborates on our perspective on CPS.
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Locations
- Lab: Jolley Hall 219A
Updates
- [News] Washington People: Chenyang Lu (WU Press)
- [News] Trackers Help Physicians Track Patient Health (WU Press)
- [News] Early Warning System Model Predicts Deterioration of Hospitalized Cancer Patients Based on Clinical Data (WU Press)
- [Editorial] Toward a Scientific and Engineering Discipline of Cyber-Physical Systems [TCPS]
- [Paper] Integrating Static and Time-Series Data in Deep Recurrent Models for Oncology Early Warning Systems [CIKM'21]
- [News] Dr. Chenyang Lu named Editor-in-Chief of ACM Transactions on Cyber-Physical Systems (03/2021)
- [News] Dr. Chenyang Lu named ACM Fellow (WU Press) (1/2021)
- [Talk] Digital Phenotyping with Wearables: Learning from Wearable Data to Predict Clinical Outcomes [Washington University School of Medicine]
- [Talk] Interdisciplinary Research: Advice and Opportunities [Guest lecture at UIUC]
- [Paper] Exploring Edge Computing for Multi-Tier Industrial Control [EMSOFT'20]
- [Paper] REACT: an Agile Control Plane for Industrial Wireless Sensor-Actuator Networks [IoTDI'20]
- [Paper] Adaptive Data Replication in Real-Time Reliable Edge Computing for Internet of Things [IoTDI'20]
- [News] PhD Student Openings in AI for Healthcare
- [Talk] Internet of Medical Things: Predicting Clinical Outcomes and Digital Phenotyping with Wearables and Machine Learning [Washington University School of Medicine]
- [Talk] Predicting Clinical Outcomes with Wearables: Machine Learning from Small Data [mHealth Research Core]
- [Talk] Machine Learning for Healthcare: From Wearables to Electronic Health Record [AI in Health, Washington University]
- [Paper] DeepAlerts: Deep Learning Based Multi-horizon Alerts for Clinical Deterioration on Oncology Hospital Wards [AAAI'20]
