Mehrzad Lavassani
Senior Researcher/Project Leader
Mehrzad Lavassani is project leader for the strategic innovation program PiiA - Process Industrial IT and Automation. PiiA is one of 17 strategic innovation programs in Sweden, funded by Vinnova, Energimyndigheten and Formas.
Her current focus is mission-oriented digitalisation in the industry to ensure the impact of innovation and accelerate sustainable transition. She is participating in PiiA strategic active projects and leads the Swedish digitalisation consortium's activities in collaboration with IEA-IETS.
Prior to joining SIP-PiiA, Mehrzad was research scientist and Data-Driven Solutions program manager at the Applied Digitalisation in the Industrial Systems department. Her primary focus has been the design and development of cross-disciplinary projects and solutions to aid the digital transformation in industries. She has been coordinating and collaborating in several European and national projects addressing data-driven innovation in the entire chain of data generation, system development and AI-enabled smart manufacturing, i.e. predictive maintenance, health diagnostics and robotics.
Mehrzad's research background is in computer and system sciences, with extended experience in industrial data analytics and communication networks, and AI-Networks in industrial IoT applications. Through her interdisciplinary research, she is interested in finding innovative solutions as much as addressing the gap between the state of the art and state of practice to enable innovation in process industries.
- Evolving Industrial Networks : Data-Driven Network Traffic Modelling and Monito…
- Reliable Information Exchange in IIoT : Investigation into the Role of Data and…
- Data-driven Method for In-band Network Telemetry Monitoring of Aggregated Traff…
- Modeling and Profiling of Aggregated Industrial Network Traffic
- From brown-field to future industrial networks, a case study
- Future industrial networks in process automation : Goals, challenges, and futur…
- Combining Fog Computing with Sensor Mote Machine Learning for Industrial IoT
- PixVid : Capturing Temporal Correlated Changes in Time Series