+ designed anomaly detection and root cause analysis algorithms of AppD Cognition Engine, which achieved both false alarm rate and miss alarm rate below 20%, and the accuracy for root cause analysis above 60% Cognition Engine is AppD's first machine learning feature into production. Here are some videos about AppD Cognition Engine: https://www.youtube.com/watch?v=i63oqSyCPvo https://www.youtube.com/watch?v=_QSD_hBh3dc + designed and implemented AppD automated transaction diagnostics capabilities, which are capable of drilling down root cause to code level (private-beta before GA release) + introduced fast weekly baseline and designed an algorithm of learning fast weekly baselines for baselining rapidly changing applications / systems like K8s + designed and implemented algorithms for machine log summarizing, event (log, error message, stack trace) based anomaly detection, decision tree root cause analysis. Descriptions about my work on log summarizing and root cause analysis can be found from links