Mojgan Pourhassan

me (Mojgan Pourhassan) Home

Data scientist at AIML.

Education

2019 Professional Certificate in Data Science,
Harvard University (HarvardX)
2013-2017 PhD in Computer Science, Awarded a Dean's Commendation for Doctoral Thesis Excellence
School of Computer Science,
University of Adelaide,
Adelaide, Australia
2006-2009 M.S. in Computer Science, Artificial Intelligence
Department of Computer Engineering
Iran University of Science and Technology (IUST)
Tehran, Iran
2001-2006 B.S. in Computer Science, Software Engineering
Department of Computer Science, Faculty of Engineering
Shahid Beheshti University
Tehran. Iran

Research Interests

  • Data Analysis
  • Machine learning
  • Statistical Inference and Modeling
  • Parameterised Complexity Analysis
  • Randomized Search Algorithms
  • Multi-Objective Optimization
  • Evolutionary Computation

Publications

  1. Book Chapter: Neumann F, Pourhassan M, Roostapour V, Analysis of Evolutionary Algorithms in Dynamic and Stochastic Environments, To appear in: B. Doerr, F. Neumann (Eds.), Theory of Randomized Search Heuristics in Discrete Search Spaces. Springer, 2019. .
  2. Neumann F, Pourhassan M, Witt C, Improved runtime results for simple randomised search heuristics on linear functions with a uniform constraint, In Proceedings the Genetic and Evolutionary Computation Conference (GECCO’19), 2019.
  3. Pourhassan M, Roostapour V, Neumann F, Runtime Analysis of RLS and (1+1) EA for the Dynamic Weighted Vertex Cover Problem, Theoretical Computer Science, 2019.
  4. Roostapour V, Pourhassan M, Neumann F, Analysis of baseline evolutionary algorithms for the packing while travelling problem, In Proceedings of the ACM/SIGEVO Conference on Foundations of Genetic Algorithms (FOGA’19), 2019.
  5. Gao W, Pourhassan M, Roostapour V, Neumann F, Runtime Analysis of Evolutionary Multi-Objective Algorithms Optimising the Degree and Diameter of Spanning Trees, In proceedings of the 10th International Conference on Evolutionary Multi-Criterion Optimization, 2019.
  6. Pourhassan M, Shi F, Neumann F, Parameterized analysis of multi- objective evolutionary algorithms and the weighted vertex cover problem. Evolutionary Computation Journal, 2018.
  7. Pourhassan M, and Neumann F, Theoretical Analysis of Local Search and Simple Evolutionary Algorithms for the Generalized Travelling Salesperson Problem. Evolutionary Computation Journal, 2018.
  8. Pourhassan M, Roostapour V, Neumann F, Improved Runtime Analysis of RLS and (1+1) EA for the Dynamic Vertex Cover Problem, In proceedings of 2017 IEEE Symposium Series on Computational Intelligence (SSCI2017), 2017.
  9. Pourhassan M, Friedrich T, and Neumann F, On the use of the dual formulation for minimum vertex cover in evolutionary algorithms. In Proceedings of the 14th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (FOGA’17), pages 37-44, 2017.
  10. Pourhassan M, Shi F, and Neumann F, Parameterized Analysis of Multi-objective Evolutionary Algorithms and the Weighted Vertex Cover Problem. In Proceedings of the 14th Conference on Parallel Problem Solving from Nature (PPSN 2016), pages 729-739, 2016.
  11. Pourhassan M, and Neumann F, On the Impact of Local Search Operators and Variable Neighbourhood Search for the Generalized Travelling Salesperson Problem. In Proceedings of 17th Genetic and Evolutionary Computation Conference (GECCO’15), 2015.
  12. Pourhassan M, Gao W, and Neumann F, Maintaining 2-Approximations for the Dynamic Vertex Cover Problem using Evolutionary Algorithms. In Proceedings of 17th Genetic and Evolutionary Computation Conference (GECCO’15), 2015.
  13. Gao W, Pourhassan M, Neumann F, Runtime Analysis of Evolutionary Diversity Optimization and the Vertex Cover Problem. In Proceedings of 17th Genetic and Evolutionary Computation Conference (GECCO’15), 2015.
  14. Corus D, Lehre P.K, Neumann F and Pourhassan M, A Parameterised Complexity Analysis of Bi-Level Optimisation with Evolutionary Algorithms. Evolutionary Computation, 1-21, 2015
  15. Pourhassan M, Mozayani N, Incorporating Expert Knowledge in Q-Learning by means of Fuzzy Rules International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA 2009) Hong Kong, China, 2009
  16. Pourhassan M, Mozayani N, Estimating State Value By Means of Fuzzy Rules in Reinforcement Learning, 17th Iran Conference on Electronic Engineering, Tehran, Iran, 2009 (In Persian)
  17. Pourhassan M, Atabaki G, Minaee B, Classification by Means of Combining Association Rules and Decision Trees 2nd Iran Conference on Data Mining, Tehran, Iran, 2009 (In Persian)
  18. Shamsfard M, Mirshahvalad A, Pourhassan M, Rostampour S, A Persian Language Processor: Combining Morphology, Syntax and Semantic 15th Iran Conference on Electronic Engineering, Tehran, Iran, 2007 (In Persian)

Teaching

  • Feb 2018 - Present, University of Adelaide
    Position: Lecturer
    Courses:
      Algorithm Design and Data Structure
      Event Driven Computing
  • Aug-Oct 2016, University of Adelaide
    Position: Tutor for course EC (Evolutionary Computation)
  • Aug-Oct 2015, University of Adelaide
    Position: Lecturer for course IPE (Introduction to Programming for Engineers)
  • Aug-Oct 2014: University of Adelaide
    Position: Lecturer and tutor for course FCS (Foundations of Computer Science)

Contact Details

Office
School of Computer Science,
Ingkarni Wardli,
University of Adelaide,
Adelaide SA 5000,
Australia
Email
mojgan{dot}pourhassan{at}adelaide.edu.au