XIAYAN JI | University of Pennsylvania (original) (raw)

Papers by XIAYAN JI

Anomaly detection is essential for preventing hazardous outcomes for safety-critical applications... more Anomaly detection is essential for preventing hazardous outcomes for safety-critical applications like autonomous driving. Given their safety-criticality, these applications benefit from provable bounds on various errors in anomaly detection. To achieve this goal in the semi-supervised setting, we propose to provide Probably Approximately Correct (PAC) guarantees on the false negative and false positive detection rates for anomaly detection algorithms. Our method (PAC-Wrap) can wrap around virtually any existing semisupervised and unsupervised anomaly detection method, endowing it with rigorous guarantees. Our experiments with various anomaly detectors and datasets indicate that PAC-Wrap is broadly effective. CCS CONCEPTS • Security and privacy → Intrusion/anomaly detection and malware mitigation; • Theory of computation → Sample complexity and generalization bounds; • Computing methodologies → Semi-supervised learning settings.

ArXiv, 2022

Accurately detecting and tracking multi-objects is important for safety-critical applications suc... more Accurately detecting and tracking multi-objects is important for safety-critical applications such as autonomous navigation. However, it remains challenging to provide guarantees on the performance of state-of-the-art techniques based on deep learning. We consider a strategy known as conformal prediction, which predicts sets of labels instead of a single label; in the classification and regression settings, these algorithms can guarantee that the true label lies within the prediction set with high probability. Building on these ideas, we propose multi-object detection and tracking algorithms that come with probably approximately correct (PAC) guarantees. They do so by constructing both a prediction set around each object detection as well as around the set of edge transitions; given an object, the detection prediction set contains its true bounding box with high probability, and the edge prediction set contains its true transition across frames with high probability. We empirically d...

2021 IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)

Medical professionals spend extensive time collecting, validating, reviewing, and analyzing medic... more Medical professionals spend extensive time collecting, validating, reviewing, and analyzing medical device data. These devices use vendor-specific applications with lengthy troubleshooting times, causing extended downtimes where medical professionals have to manually document patient data in the electronic health record (EHR). Manual logging of this data creates delays and leaves it vulnerable to errors, manipulation, and omissions. In this paper, we present VitalCore, a medical device integration platform that supports access to medical device data in real-time. We deploy VitalCore in three applications at Penn Medicine: Medical Device Dashboard, Ventilation Alert, and Anomaly Detector. In the Medical Device Dashboard, we reduced, by up to six times, the amount of time required of medical professionals, clinical engineers, and IT analysts by simplifying the troubleshooting workflow, thus decreasing downtimes and increasing clinical productivity. In Ventilation Alert, we demonstrated the ability to assist medical professionals by alerting them to newly ventilated patients. In Anomaly Detector, we showed that we could predict anomalous patterns in our data with 93% accuracy.

Anomaly detection is essential for preventing hazardous outcomes for safety-critical applications... more Anomaly detection is essential for preventing hazardous outcomes for safety-critical applications like autonomous driving. Given their safety-criticality, these applications benefit from provable bounds on various errors in anomaly detection. To achieve this goal in the semi-supervised setting, we propose to provide Probably Approximately Correct (PAC) guarantees on the false negative and false positive detection rates for anomaly detection algorithms. Our method (PAC-Wrap) can wrap around virtually any existing semisupervised and unsupervised anomaly detection method, endowing it with rigorous guarantees. Our experiments with various anomaly detectors and datasets indicate that PAC-Wrap is broadly effective. CCS CONCEPTS • Security and privacy → Intrusion/anomaly detection and malware mitigation; • Theory of computation → Sample complexity and generalization bounds; • Computing methodologies → Semi-supervised learning settings.

ArXiv, 2022

Accurately detecting and tracking multi-objects is important for safety-critical applications suc... more Accurately detecting and tracking multi-objects is important for safety-critical applications such as autonomous navigation. However, it remains challenging to provide guarantees on the performance of state-of-the-art techniques based on deep learning. We consider a strategy known as conformal prediction, which predicts sets of labels instead of a single label; in the classification and regression settings, these algorithms can guarantee that the true label lies within the prediction set with high probability. Building on these ideas, we propose multi-object detection and tracking algorithms that come with probably approximately correct (PAC) guarantees. They do so by constructing both a prediction set around each object detection as well as around the set of edge transitions; given an object, the detection prediction set contains its true bounding box with high probability, and the edge prediction set contains its true transition across frames with high probability. We empirically d...

2021 IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)

Medical professionals spend extensive time collecting, validating, reviewing, and analyzing medic... more Medical professionals spend extensive time collecting, validating, reviewing, and analyzing medical device data. These devices use vendor-specific applications with lengthy troubleshooting times, causing extended downtimes where medical professionals have to manually document patient data in the electronic health record (EHR). Manual logging of this data creates delays and leaves it vulnerable to errors, manipulation, and omissions. In this paper, we present VitalCore, a medical device integration platform that supports access to medical device data in real-time. We deploy VitalCore in three applications at Penn Medicine: Medical Device Dashboard, Ventilation Alert, and Anomaly Detector. In the Medical Device Dashboard, we reduced, by up to six times, the amount of time required of medical professionals, clinical engineers, and IT analysts by simplifying the troubleshooting workflow, thus decreasing downtimes and increasing clinical productivity. In Ventilation Alert, we demonstrated the ability to assist medical professionals by alerting them to newly ventilated patients. In Anomaly Detector, we showed that we could predict anomalous patterns in our data with 93% accuracy.