Mahsa Salehi

Dr

Accepting PhD Students

PhD projects

https://supervisorconnect.it.monash.edu/

20082021

Research activity per year

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Personal profile

Research interests

Mahsa's current research interests are in Data mining and Machine learning, specifically:

  • Multi-dimensional time series analysis
  • Online learning
  • Learning from non-stationary distributions
  • Deep learning
  • Anomaly detection with applications in cybersecurity

Biography

Mahsa is a Lecturer in Machine Learning, Faculty of Information Technology, Monash University. She was a postdoctoral researcher at IBM Research Australia before joining Monash. She received her PhD in Computer Science from the Department of Computing and Information Systems at the University of Melbourne in collaboration with the Machine Learning research group in NICTA. 

Grants:

  • 2019-2021, ARENA Grant, AU$220,000
  • 2018, Sector Growth - Future Industries Grant, AU$50,000
  • 2018, FIT Industry Seed Grant, AU$60,000 (including $30,000 from Emotiv Research)
  • 2018, FIT Multidisciplinary Seed Grant, AU$24,000
  • 2018, Advancing Women’s Research Success Grant, AU$11,000
  • 2012-2014 Microsoft, Microsoft Imagine Cup Grant, US$75,000

Awards:

  • Selected amongst the “Top 200 Most Qualified Young Researchers in Computer Science and Mathematics” by the Scientific Committee of the Heidelberg Laureate Forum Foundation (HLFF) to participate in the 4th Heidelberg Laureate Forum, 2016
  • IBM Manager’s Choice Award, IBM Research Australia, 2016
  • Winner of Australian 2012 Microsoft Imagine Cup Final (our team placed the 1st in Australia and among the top 20 in the World), 2012
  • Melbourne School of Engineering (MSE) Teaching Excellence Award, University of Melbourne, 2014
  • Excellence in Tutoring Award in the Department of Computing and Information Systems, University of Melbourne, 2014
  • Emotiv Founders Scholarship, Emotiv Research, 2014

 

Monash teaching commitment

Mahsa has experience as the Chief Examiner and Lecturer for the following units in the Faculty of IT:

  • FIT1043 Introduction to Data Science 
  • FIT5145 Introduction to Data Sicnece

Education/Academic qualification

Computer Science, Doctor of Philosophy, University of Melbourne

Award Date: 3 Aug 2016

Software Engineering, Master of Science, Amirkabir University of Technology

Award Date: 31 Aug 2009

Information Technology, Bachelor of Science, Amirkabir University of Technology

Award Date: 31 Aug 2008

Computer Engineering, Bachelor of Science, Amirkabir University of Technology

Award Date: 31 Aug 2006

Research area keywords

  • Time Series Analysis
  • Machine Learning
  • Anomaly Detection
  • Deep Learning
  • Multidimensional/non-linear learning
  • Outlier Detection
  • Spatio-Temporal Data Analysis

Network

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