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] Lu Wins Award for Most Influential Paper in Real-Time Systems
• [Paper] Cross-trial Prediction of Depression Remission Using Problem-solving Therapy: A Machine Learning Approach [Journal of Affective Disorders]
• [News] Model Predicts Deterioration of Hospitalized Patients with Cancer (Healio)
• [News] That new Fitbit does more than count steps. It may save your life one day. (Diagnostic and Interventional Cardiology)
• [News] Research using Fitbit and machine learning to predict surgical outcomes (featured in the Next Byte podcast)
• [Paper] Personalized Surgical Transfusion Risk Prediction Using Machine Learning to Guide Preoperative Type and Screen Orders [Anesthesiology]
• [Paper] Predicting Physician Burnout Using Clinical Activity Logs: Model Performance and Lessons Learned [Journal of Biomedical Informatics]
• [Paper] Continuous Real-time Prediction of Surgical Case Duration Using a Modular Artificial Neural Network [British Journal of Anaesthesia]
• [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]

• News Archive