Dr. Zahra Abbasalinejadkolaei

Assistant Professor

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Biography

Zahra Abbasalinejadkolaei is a PhD student at Linköping University, specializing in Machine Learning and statistical modeling to enhance roadwork environments. Her research focuses on analyzing repeated measures data, including vehicle path, speed, and acceleration specifically in roadwork contexts. She aims to analyze trends in driver behavior under varying time conditions and develop predictive models that guide dynamic traffic monitoring, ultimately enhancing the design of adaptive, risk-sensitive roadwork systems. In addition to her research, she serves as a Teaching Assistant in Probability Statistics and Mathematical Statistics, helping students grasp essential concepts in estimation, inference, and the application of statistical methods to real-world problems. Her research is funded by the Swedish Transport Administration and she is a member of the Traffic Safety Traffic Systems research group at the Swedish National Road Transport Research Institute (VTI).

Research Interests

Experience

PhD Student

2020-01-01 — Present

Linköping University • Linköping, Sweden

Conducting research in Machine Learning and statistical modeling for roadwork safety.

Teaching Assistant

2020-01-01 — Present

Linköping University • Linköping, Sweden

Teaching Probability Statistics and Mathematical Statistics.

Requirements for Linköping University

Master Program
Requirements
GPA Requirement
Required:3
IELTS
Listening
Required:5.5
Reading
Required:5.5
Writing
Required:5.5
Speaking
Required:5.5
Overall
Required:6.5
TOEFL
Listening
Required:20
Reading
Required:20
Writing
Required:20
Speaking
Required:20
Total
Required:90
Prerequisites
Bachelor's degree with a major relevant to the program At least 30 ECTS credits in mathematics/applied mathematics and/or application of mathematics
Application Checklist
  • Certificates and diplomas from previous university studies
  • Transcript of records
  • Proof of English proficiency
  • Copy of passport/ID
  • Syllabus for relevant courses
Specialization Notes

Requirements are standardized across the Faculty of Science and Engineering (Institute of Technology) for international Master's programs.