My name is David Brunner and I completed my studies "Secure Information Systems" with a Bachelor and subsequent Master of Science in Engineering at the University of Applied Sciences Upper Austria (Campus Hagenberg). In the course of his Bachelor studies, I gained insight into a broad range of security related topics and completed an extended internship at the Siemens Headquarters in Munich, focused on penetration testing of Android devices and web-applications. While working on my Master's degree I became interested in Artificial Intelligence and started working towards my Master's thesis on the "Generation of Training Data from 3D Models for Deep Learning based Object Detection", part of which was done during a stay at the University of Hertfordshire in Hatfield, England. I also worked as a research assistant in the R&D of the University of Applied Sciences Upper Austria, researching techniques for Code Obfuscation. After concluding my studies, I decided to fully transition into Artificial Intelligence and secured a position as Researcher and Data Scientist at the Software Competence Center Hagenberg (SCCH), working on projects revolving around Privacy-Preserving Machine Learning and Computer Vision. During my four years at SCCH, I kept my eyes out for a PhD position that would allow me to work on Machine Learning methods for supporting ethical endeavours. PLFDoc nearly fits into this picture, with its goal to foster animal welfare through advances in computer science, with a focus on Artificial Intelligence. Within PLFDoc, I currently work on Computer Vision methods for social behavior recognition in pigs, researching techniques like Pose Estimation, Multi-object Tracking and Action Detection.

My name is Peter Helf, and I have a background in computer science with two master’s degrees, “Software Architecture and Design” and “AI Engineering”. Furthermore, I have 4 years of experience working at the Messerli Research Institute of the University of Veterinary Medicine, Vienna, working on software implementations for various research projects.

The monitoring of farrowing on pig farms is required to ensure the health and welfare of the sows. Automation of this process makes it easier for farmers to attend to the needs of sows. For this a Computer Vision (CV) approach is developed which predicts the time of farrowing. Videos of the sows recorded in the period around farrowing are used to perform phenotyping of relevant traits. Performance of farrowing prediction based on CV algorithms will be compared with the other sensor technology e.g. accelerometers. The video recordings of the sows are supplemented with synthetic data generated using state of the art 3D scanning algorithms. The usage of synthetic data allows for more generalisable models applicable to different pig breeds and farm environments.

My name is Elisabeth, and I grew up in the countryside of Upper Austria. After completing high school with a focus on mechanical engineering, I decided to pursue a bachelor's degree in “Medical and Bioinformatics” at the University of Applied Sciences Upper Austria Campus Hagenberg. Due to my interest in the intersection of life sciences and engineering this degree program seemed perfect for me. Following my master’s degree in “Data Science and Engineering”, also in Hagenberg, I found a unique opportunity to delve deeper into an interdisciplinary field for my Ph.D. My research focuses on explainable artificial intelligence in precision livestock farming, a fascinating area that combines various disciplines.

My work emphasizes developing explainable methods that enhance animal welfare and ensure that domain experts, such as farmers and veterinarians, can understand the reasoning behind my system's outputs. Currently, I am working on an explainable farrowing prediction system to detect nest-building behavior and predict the time remaining until the onset of farrowing.

My name is Habeeb Muraina and I am a PhD candidate focusing on using sensor-based technologies to improve animal welfare. With expertise in Animal Physiology and Bioclimatology, I studied how environmental and physiological factors affect livestock welfare and I contributed to the development of innovative welfare monitoring tools.

My current project, "Sensor-Based Monitoring of Brushing and Drinking Behavior in Cattle", uses advanced computer vision to study cattle behavior. The project involves developing a camera system that automatically collects and analyses data on cattle brushing and drinking behaviors, then, understand how these behaviors change under different conditions and social interactions. This research aims to improve animal welfare on commercial farms by providing valuable insights into behavior patterns and resource access.

Through my work, I am advancing our ability to monitor cattle behavior with minimal human intervention, offering practical solutions to enhance livestock management and welfare.