The project involves data recording and development of an
artificial intelligence based algorithmic prototype for generating ventilation
recommendations within the Ulm Cathedral.
Contribution:
Collecting and processing data from
multiple sensor networks and external sources, ensuring data quality and
consistency for analysis;
Performing exploratory data analysis, generating visualizations and insights to investigate trends and anomalies;
Developing and implementing predictive models to forecast key metrics and improve decision-making.
Development of predictive maintenance tools using machine learning technics, in order to monitor and prevent bearing damage of large engines.
Contribution:
Analyzing data from various sources and bearing types to compare damaged and undamaged bearings;
Visualizing the findings through plots as part of the investigative process;
Preliminary classification of bearing damage types.
The project involved the development and certification
of network-controlled I/O modules designed for defense applications, achieving
compliance with Safety Integrity Level 3 (SIL 3) standards.
Contribution:
Redaction of software requirements,
test cases, test plans and software design documentation;
Implementation of compliant source code for the modules;
Implementation of unit and module tests;
Implementation and execution of hardware tests on target hardware;
Leveraging Jenkins for CI/CD and Gerrit for code review, ensuring consistent integration and maintaining high-quality control;
Implementation and maintenance of an export tool from Jira to LaTeX to produce compliant documentation.
The process involved acquiring energy consumption data from ECON systems, correlating it with additional data for labeling, and evaluating it through an intelligent learning system responsible for continuous monitoring, ensuring effectiveness.
Contribution:
Retrieving data from ECON systems and external sources, ensuring comprehensive data collection from multiple inputs;
Cleaning and preprocessing the data;
Conducting preliminary analysis of the data to identify trends, patterns, and potential anomalies;
Visualizing the data through various plotting techniques to facilitate peak detection.
Building an IIoT platform that simplifies the extraction and processing of sensor data from production plants, enabling maintenance teams to make data-driven decisions and perform real-time monitoring through predictive maintenance.
Contribution:
Supporting platform development, focusing on data retrieval and reformatting;
Assisting with data engineering for customer clusters, ensuring effective organization and access;
Providing global debugging and maintenance to ensure the platform?s smooth operation.
Data Scientist
The project involves data recording and development of an
artificial intelligence based algorithmic prototype for generating ventilation
recommendations within the Ulm Cathedral.
Contribution:
Collecting and processing data from
multiple sensor networks and external sources, ensuring data quality and
consistency for analysis;
Performing exploratory data analysis, generating visualizations and insights to investigate trends and anomalies;
Developing and implementing predictive models to forecast key metrics and improve decision-making.
Development of predictive maintenance tools using machine learning technics, in order to monitor and prevent bearing damage of large engines.
Contribution:
Analyzing data from various sources and bearing types to compare damaged and undamaged bearings;
Visualizing the findings through plots as part of the investigative process;
Preliminary classification of bearing damage types.
The project involved the development and certification
of network-controlled I/O modules designed for defense applications, achieving
compliance with Safety Integrity Level 3 (SIL 3) standards.
Contribution:
Redaction of software requirements,
test cases, test plans and software design documentation;
Implementation of compliant source code for the modules;
Implementation of unit and module tests;
Implementation and execution of hardware tests on target hardware;
Leveraging Jenkins for CI/CD and Gerrit for code review, ensuring consistent integration and maintaining high-quality control;
Implementation and maintenance of an export tool from Jira to LaTeX to produce compliant documentation.
The process involved acquiring energy consumption data from ECON systems, correlating it with additional data for labeling, and evaluating it through an intelligent learning system responsible for continuous monitoring, ensuring effectiveness.
Contribution:
Retrieving data from ECON systems and external sources, ensuring comprehensive data collection from multiple inputs;
Cleaning and preprocessing the data;
Conducting preliminary analysis of the data to identify trends, patterns, and potential anomalies;
Visualizing the data through various plotting techniques to facilitate peak detection.
Building an IIoT platform that simplifies the extraction and processing of sensor data from production plants, enabling maintenance teams to make data-driven decisions and perform real-time monitoring through predictive maintenance.
Contribution:
Supporting platform development, focusing on data retrieval and reformatting;
Assisting with data engineering for customer clusters, ensuring effective organization and access;
Providing global debugging and maintenance to ensure the platform?s smooth operation.
Data Scientist