PersonnelMs. Lauryne Rodrigues

Ms. Lauryne Rodrigues

Ms. Lauryne Rodrigues


  • BSc. Industrial Engineering, Estacio de Sa University, Brazil. 2016


Lauryne Rodrigues obtained a degree of Bachelor of Industrial Engineering at Estácio Sá University (UNESA) in Brazil. During her studies degree, Lauryne undertook three exchange semesters in Canada, at Dalhousie University, it was sponsored by the National Council for Scientific and Technological Development (CNPq). This networking opportunity sparked her interest in statistics, data investigation and simulation. During January 2011, Lauryne experience one of Brazil’s worst natural disasters, in Nova Friburgo, an heavy rain triggered flood and landslides, in consequence, streets were blocked, neighbourhoods were isolated and first responders were not able to help emergence situations.

Consequently, this experience was foundational in her decision to pursue a career in emergency response logistics; the event presented to her the complexes and challenges of critical accidents. After a period of working in the industry, she decided to pursue a Master’s degree. In 2019, she was introduced to the MARS team and admitted at Dalhousie University. And her studies have focus on network modelling, logistics analysis and emergence response evaluation applied in natural disasters.

Research Interests

  • Network optimization models
  • Logistics Emergency preparedness and response
  • Simulation
  • Spatial Data Mining
  • Automatic identification system (AIS)
  • Geographic Information Systems (GIS)
  • Quality Control
  • Supply Chain and Lean systems

Lauryne research interests include network distribution and maritime emergency preparedness and response risk management due to natural disasters, focus on maritime transportation. She is also concentrating her studies in trajectory data mining and analysis explicitly spatial data, through Automatic Identification Systems. Additionally, she expects to increase and narrow her knowledge in data analyses applied to optimization problems in statistics with a focus on quality control and systems improvement.