Fatima Ahmedi

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.

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.

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.

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.