As the CEO of logsight, my responsibilities center around executing the company's strategy and bringing our vision to life. This means cultivating a culture of continuous growth and innovation, where our teams are encouraged to explore new ideas, push boundaries, and drive customer success through technological transformation. I focus on empowering our teams and individuals to actively contribute to our mission, fostering an environment where every voice is heard, respected, and valued. Additionally, I am responsible for maintaining our financial health by ensuring we have the necessary capital and resources to sustain our growth and achieve our strategic objectives. My role is about guiding the company forward while enabling both our people and our customers to thrive.

During my post-doctoral work, I have been deeply involved in industry projects with large enterprises, managing every aspect of the project lifecycle—from initial research to production deployment. This experience has honed my ability to lead complex initiatives and effectively translate research findings into scalable, real-world solutions.In parallel, I collaborated with a group of like-minded colleagues to lay the groundwork for founding our spin-off company, logsight.I have authored 30 peer-reviewed publications, which have collectively received over 600 citations, underscoring the significant impact and recognition of my contributions to the academic community.In addition, I lecture on topics such as distributed systems, artificial intelligence, cloud operations, and big data workflow optimization. I also support young researchers and PhD candidates through mentoring and consulting, helping to foster the next generation of innovators in these fields.

During my PhD work, I have been actively engaged in industry projects with large enterprises, overseeing the entire project lifecycle—from initial research to production deployment. This experience has allowed me to lead and manage complex initiatives, ensuring the transition of research outcomes into practical, scalable solutions.I have contributed 30 peer-reviewed publications to the international research community, which have collectively garnered over 600 citations.In addition to my research work, I held lectures on distributed systems, artificial intelligence, cloud operations, and big data workflow optimization. I was deeply committed to education and mentorship, having supervised over 30 Bachelor’s and Master’s theses, guiding students through their academic and research journeys.

Development of NLP and AI methods to localize errors across a mesh of connected devices.

Development of anomaly detection methods based on NLP and AI for log data with a focus on model generalization across private cloud and network systems.

Developed a platform to detect, analyze, and resolve system anomalies in cloud and edge computing environments.

Developed a predictive maintenance system for industrial pumps using machine learning, AI, and time series analysis. The solution was designed to forecast maintenance intervals and predict critical machine failures at least one day in advance, effectively preventing major mechanical damage. By analyzing operational data from the pumps, the model successfully anticipated failures, allowing for timely interventions that minimized downtime and extended equipment life, ultimately enhancing efficiency and reliability in industrial operations.

Developed AI-driven self-healing mechanisms for virtual network functions, enabling autonomous detection and diagnosis of system errors. It detects anomalies in the system, identifies root causes and provides actionable recommendations for self-stabilizing measures to human operators. Depending on a configurable level of automation, these corrective actions can be executed automatically or with human oversight, offering operators flexible control over the degree of system autonomy in the self-healing process.

Implemented an automated quality control method for manufactured solder paste circuit boards. The project focused on early detection of production errors using an image inspection method. Additionally, an automated configuration of production machines was implemented, which reduced human error and minimized production inconsistencies. Through these improvements, we optimized the production line, significantly reducing the rate of product returns. The project ultimately led to a 20% decrease in defective components, resulting in substantial cost savings and a major boost in production efficiency.

ECG Time Series Analysis: Anomaly Detection, Morphological ClassificationBuilding a Medical Data Analysis platform for ECG analysis.

- Supported medical staff with their day to day work