Meet our Team

Peter Gemeiner

Core competences

  • Machine Learning
  • Visual Data Processing
  • Software­entwicklung

Peter is an accomplished professional with a strong background in machine learning, data science, and software development. Holding a Ph.D. from the Vienna University of Technology, his expertise spans multiple domains, including artificial intelligence, cloud computing, and software development. His background includes a history of research collaborations with institutions like Imperial College London and University of Valencia. He has earned numerous professional certifications, such as AWS Machine Learning Specialty and AWS DevOps Engineer Professional, showcasing his commitment to continuous learning and industry-relevant skills.

His career is marked by a series of impactful roles in both freelance and corporate settings. As a Python Developer and Machine Learning Engineer, he has contributed to innovative e-commerce plugins, REST API development, and innovative machine learning applications. His past roles include working as a Project Manager at the Austrian National Library, where he led the development of a new REST API. Additionally, he has been involved in cutting-edge projects related to computer vision, deep learning, and algorithm development, collaborating with organizations like Touch4IT, Ciuvo, and the AIT Austrian Institute of Technology.

Peter’s skill set is diverse and includes machine learning techniques such as supervised and unsupervised learning, deep learning models like CNNs, RNNs and LLMs, and frameworks like TensorFlow, PyTorch, and Keras. He is proficient in cloud technologies, particularly AWS, and has experience with deployment tools like Terraform, Docker, and GitLab CI/CD. His programming expertise extends to Python, C/C++, Java, and front-end technologies, reflecting his adaptability in various tech environments.

Beyond his technical abilities, Peter has received several prestigious awards, including the “Fred Margulies Preis” for his Ph.D. thesis and honors for his Master’s thesis.
Fluent in four languages, Peter demonstrates strong communication skills alongside his analytical and problem-solving capabilities. His extensive experience in managing and deploying AI solutions, coupled with his proactive, positive and goal-driven attitude, makes him a highly valuable asset in any technical or research-driven environment.

Sample Projects

VisualSearch Recommendations

eCommmerce plugin that enables searching for visually similar products, helping customers to find and compare multiple items that are related in color and style.

Visual Assistance Systems for Seniors

Visual Assistance App: enabling visual assistance for seniors by helping position determination using Computer Vision and Deep Learning.

VisualSearch Recommendations

For VisualSearch, the focus was on developing and deploying e-commerce plugins to enhance recommendation functionalities in web shops. The plugins were successfully launched in the store and happily adopted by customers, demonstrating their practical use in improving e-commerce experiences.  Key elements included:

– computation of visual embeddings from appearances of e-commerce products
– building and maintaining a search index using using these embeddings
– providing a cloud-based API for Shopware and Prestashop plugins

Peter acted as the project manager and key data scientist on this project, leveraging AWS services, Python, and deep learning frameworks like Keras, the project integrated cloud-based solutions using CloudFormation, Lambda, and Gateway for scalable and efficient deployment, using SQL and DynamoDB for data management.

Visual Assistance System for Seniors

For the Austrian Institute of Technology (as a part of the national research project LARAH), a prototype of an assistance system for visual indoor localization for disabled and elderly persons was developed. By leveraging Computer Vision and Deep Learning, innovative algorithms for position determination were implemented. The project resulted in a functional prototype, including two Android applications for real-time localization. Key elements included:

– visual recognition of persons using Deep-Learning models
– visual reconstruction and localization of indoor environments using Structure-from-Motion and Machine Learning algorithms

Peter acted as the project manager and key data scientist on this project, developed under Python using Deep Learning models like TensorFlow, custom developed Structure-from-Motion software and custom camera calibration software. Additionally, as a part of the project, two Android apps were modified and integrated together onto the Robot Operating System on the mobile platform.

Electrical Switch Monitoring

For a public transportation network, the objective was to optimize the monitoring of their legacy electrical switchboards. These decade-old items that are often located in very remote areas are prone to failures and tracking of errors was not possible. The objective was to enable real-time tracking and the recognition of upcoming failures from changed switching behavior. The key elements were:

  • Development of specific hardware configuration with custom housings (3D printed) to mount instead of regular switchboard covers;
  • Camera control and initial image generation on Raspberry Pi integrated in housing;
  • Initial scan of switch layout and labels;
  • Identification of switching operations and registration of new positions;
  • Identification of irregular switching patterns (delays, other irregularities) to indicate upcoming failures for predictive maintenance;
  • Update of central database and cloud solution with last state and observed switching patterns;

The solution was implemented using Python on Raspberry Pi devices, backbone and cloud processing were done using a LAMP stack, using PyTorch, TensorFlow and OpenCV.

Customer Interaction Evaluation

For a project management SaaS solution where customers were matched with freelancers,, a customAI solution was established with the purpose to improve experiences for all parties. Key purposes were to create an early warning system to help customer service intervene in case of issues:

  • Identification of unusual patterns (delays indicating inaction, intense exchanges);
  • Flagging of language transgressions on both sides (use of inappropriate language, aggression);
  • Matching of final ratings with evaluation of flow and dialogue quality to foster a more honest rating culture;
  • Language style matching to improve future matching of freelancers to clients;

The solution was implemented using Python on a LAMP stack, with self-developed machine learning libraries.