Mehrzad Lavassani
Senior Researcher/Project Leader
Mehrzad is visionary project leader for the national strategic innovation program PiiA - Process Industrial IT and Automation. She is responsible for PiiA's Internationalisation, Project Support and Sustainability Portfolio, and leads the Swedish digitalisation consortium's activities in collaboration with the International Energy Agency's IETS Technology Collaboration Programme. Her focus is on mission-oriented digitalisation in the industry. Her goal is to ensure the impact of innovation and accelerate sustainable transition.
She is a senior researcher in Industrial AIoT at RISE, and prior to joining SIP-PiiA, Data-driven solutions' program manager. Her primary focus has been the design and development of cross-disciplinary solutions and projects in national and European collaborations to aid digital transformation in industries, addressing value-added data-driven innovation in industrial applications for smart manufacturing.
Mehrzad is an IEEE CertifAIEd™ Authorized Lead Assessor in Ethical AI. Her ambition is to integrate the ethical dimension of AI into the design and development of AI-enabled services and products to harness the benefits of AI responsibly and with consequential societal considerations.
Mehrzad received her PhD from Mälardalen University in Computer Science and Engineering, and Techn. Lic. in Computer and System Sciences from Mid Sweden University. She has extended experience in data analytics, communication networks, and AI-Networks in industrial IoT applications.
- 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