Martin Krasser

Martin Krasser has a diverse work experience in the field of software engineering and machine learning. Martin has recently worked as a Machine Learning Engineer at MerlinOne, where they developed a multimodal neural search engine for the digital asset management system. This engine supports various search modes and includes face recognition in image searches.

Before their role at MerlinOne, Martin worked as a Freelancer, focusing on machine learning and deep learning projects. Martin also took a sabbatical during this time to deepen their knowledge in mathematics, statistics, and traditional machine learning and deep learning. Martin received certifications in online courses and conducted self-study of numerous books and papers.

Prior to their freelance work, Martin was a Distributed Systems Engineer at Red Bull Media House, where they were responsible for the global distribution of their in-house digital asset management platform. Martin also developed Eventuate, a toolkit for event sourcing and event collaboration at a global scale.

Martin also worked as a Software Engineer and Architect at agido GmbH and fine.lines.software GmbH, where they developed an online sports betting web application and a distributed workflow management system, respectively. In both roles, they utilized event sourcing and had expertise in distributed systems.

In addition, Martin has held positions at Typesafe (now Lightbend), Eligotech BV, Talend, InterComponentWare AG, and LION bioscience. These roles involved software development, architecture, and engineering, with a focus on various domains such as online gambling, eHealth integration, and pharmaceutical research.

Overall, Martin Krasser has extensive experience in software engineering, distributed systems, and machine learning, with a strong background in event sourcing and event collaboration.

Martin Krasser attended Karl-Franzens-Universität Graz from 1992 to 1997, where they obtained a Mag. rer. nat. degree in Chemistry. In terms of additional certifications, they completed various courses and specializations in machine learning and related topics on Coursera, including "Bayesian Methods for Machine Learning (with Honors)," "Deep Learning Specialization," "Sequence Models," "Convolutional Neural Networks," "Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization," "Neural Networks and Deep Learning," "Structuring Machine Learning Projects," "Bayesian Statistics: From Concept to Data Analysis (with Honors)," "Inferential Statistics," "Machine Learning," "Principles of Reactive Programming," and "Functional Programming Principles in Scala." Martin also obtained the certification "Full Stack Deep Learning - Spring 2021" from Full Stack Deep Learning.