Mahsa Salehi

Mahsa Salehi is a Lecturer in Machine Learning in the Faculty of Information Technology, Monash University. Her main research areas are:

• Multi-dimensional time series analysis
• Online learning
• Learning from non-stationary distributions
• Deep learning

Grants:

Awards:

Biography:

Mahsa was a postdoctoral researcher at IBM Research Australia before she joined Monash University. 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 (2012-2016). She received her BSc double degree in Software Engineering and Information Technology in 2006 and 2008, and her Master’s degree in Software Engineering in 2009, all of which were completed at Tehran Polytechnique University, Iran. 

Mahsa has experience as the Lecturer for the following unit in the Faculty of IT:

• FIT1043 Introduction to Data Science (Chief Examiner + Lecturer)
• FIT5145 Introduction to Data Sicnece (Lecturer)

My current research interests include multi dimensional time series analysis, learning from non-stationary distributions and deep learning.

Current PhD students:

• Milad Chenaghlou: Anomaly detection in data streams

                  October 2015 → present

• Chaitanya Manapragada: Learning from non-stationary distributions

                  July 2017 → present

• Thusitha Dayaratne: Anomaly detection in smart grid

                  November 2017 → present

• Yassien Shaalan: Text mining/Spam Detection

                  September 2018 → present

Past Master students:

• Mohammed AL-Kaf: Anomaly detection in non-stationary distributions

                  July 2017 → May 2018

• Janki Shah: Dynamic window selection for detecting anomalies in non-stationary data streams

                  Feb 2018 → October 2018

Research Assitants:

• Leyla Roohi: Unsupervised multi-variate time series segmentaion

                  April 2018 → September 2018

• Milad Chenaghlou: Supervised multi-variate time series segmentaion/classification

                  July 2018 → December 2018

• Soudeh Kasiri: Deep Neural networks for multivariate time series classification

                  April 2018 → October 2018

•  Jueqing Lu: Risk prediction in construction sites using machine learning

                  January 2019 → Present

Research interns:

• Vedic Partap: Data discritisation in anomaly detection 

                  Feb 2018 → June 2018

• Sarah Sheikhi: Fall detection using machine learing techniques/anomaly detection

                  Jan 2018 → April 2018

• Sepehr Mousavi: Feature selection and clustering in multivariate time series data

                  July 2018 → Present