Christian H.

APTA Technologies helps software-product owners develop a deep understanding of user behavior, identify the root causes of bottlenecks or breakdowns, and predict error or downtime occurrences, and monitor the impact on the business processes enabled by the software. Pando, our process analysis tool, extracts insights and knowledge from the application logs and visually presents the data to engineers, developers, and business process owners. Using these insights, root-cause analysis and predictive monitoring are set up. This helped our clients to detect incidents with significant lead time before user reports, and identified incidents without manual setup of alerting and thresholding rules. We build REST-APIs in Python using fastapi and frontends using Spring + VueJS while our core algorithms are implemented in C++ (or Pytorch Lightening).

As a Software Developer:

I develop machine learning tools for applications at the intersection of software engineering and security. I mostly develop novel algorithms and methods for anomaly detection, semi-supervised machine learning, and grammar inference. These techniques use online clustering, sketching, and locality sensitive hashing techniques in online settings, in part using Apache Kafka/ Flink. Within the security setting, I also consider adversarial learning scenarios and was part of the winning team of the adversarial malware learning challenge at KDD. I mostly used C++, Python, and PyTorch. I also closely work and supervise students of all levels.