Mahsa Salehi

Mahsa Salehi is a Senior Lecturer in the Department of Data Science & AI at Monash University’s Faculty of Information Technology. She holds a PhD in Computer Science from the University of Melbourne and previously served as a postdoctoral researcher at IBM Research Australia. Her research focuses on data mining, machine learning, and time series analysis, with applications in healthcare, cybersecurity, and smart grids.

• Education:
  • PhD in Computer Science, University of Melbourne (2016)
  • MSc in Software Engineering, Amirkabir University of Technology (2009)
  • BSc in Information Technology & Computer Engineering, Amirkabir University of Technology (2008/2006)

Her key research interests include multi-dimensional time series analysis, anomaly detection, brain-inspired machine learning, and non-stationary data learning. She has led or contributed to over 40 research outputs, including high-impact papers on anomaly detection frameworks (e.g., CARLA) and EEG representation learning (EEG2Rep). Her work bridges theoretical advancements with practical applications, such as detecting urinary anomalies in seniors and securing smart grid systems against cyberattacks.

Dr. Salehi has secured significant grants, including AU$246K from ARENA (2019–2021) and AU$30K from Emotiv Research (2022–2024). She is an Associate Editor of the ACM Transactions on Knowledge Discovery from Data and has been recognized with awards like the ICDM 2022 Best Paper Runner-Up and IBM’s Manager’s Choice Award (2016).

• Grants & Projects:
  • Privacy-Preserving Machine Learning (CSIRO Next Gen, 2023–2027)
  • AI for Clean Energy & Sustainability (Monash, 2023–2027)
  • Deep Learning for Brain EEG Analysis (PhD Top-Up, 2022–2025)

Her contributions extend to editorial and patent activities, including roles at IBM Research and collaborative projects with industry partners like Emotiv.