Alexander Fry

Title: Innovations of Alexander Fry in Anomaly Detection

Introduction

Alexander Fry is an accomplished inventor based in Seattle, WA (US). He has made significant contributions to the field of anomaly detection in physical systems. His innovative approach combines physics rules with machine learning techniques to enhance the reliability of sensor data.

Latest Patents

Fry holds a patent for a "Method for Physical System Anomaly Detection." This method generates a set of candidate physics models from a set of physics rules and a process graph representing the system. The models assign physics rules to portions of the process graph that represent sensors. If the error between the models and sensor data exceeds a predetermined error tolerance, the candidate physics models are rejected. Supervised learning is employed to train a machine learning model to predict the error between the physics models and the sensor data. The predicted error and predicted sensor measurements from the physics models are then utilized to detect anomalies through unsupervised learning on a distribution of error between the predicted sensor measurements and the sensor data.

Career Highlights

Fry's career is marked by his dedication to advancing technology in anomaly detection. His work at Tignis, Inc. has positioned him as a key player in the development of innovative solutions that address complex challenges in physical systems.

Collaborations

Some of Fry's notable coworkers include Jonathan L. Herlocker and Matt McLaughlin. Their collaboration has contributed to the success of various projects within Tignis, Inc.

Conclusion

Alexander Fry's innovative methods in anomaly detection demonstrate his expertise and commitment to improving technology in physical systems. His contributions are paving the way for more reliable and efficient systems in various applications.