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Associate Professor
Department of Electrical and Computer Systems Eng., Monash University
Address: 232, Eng. Building 72, 14 Alliance lane, Clayton, Melbourne, VIC, Australia
e-mail: mehrtash (döt) harandi (ät) monash (döt) edu
About me
I joined Monash’s Department of Electrical and Computer Systems Engineering (ECSE) in August 2018. Before joining Monash University, I spent five wonderful years at Canberra Research Laboratory-NICTA, working with Prof. Richard Hartley and Prof. Fatih Porikli. Before that, I worked at Queensland Research Laboratory-NICTA with Prof. Brian Lovell.
I am interested in various aspects of learning, especially with a flavor of visual data (see my google scholar page). I am an associate editor of the IET-CV, Frontiers In Imaging, and Journal of Imaging and regularly review papers for top conferences and journals in ML/CV, including CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, IEEE TPAMI, IEEE TNNLS, IEEE TIP.
Teaching
Neural Networks and Deep Learning, Monash University, 2019↝
Probability and AI for Engineers, Monash University, 2024↝
Computer Vision, Monash University, 2022↝2023
Advanced Engineering Data Analysis, Monash University, 2019-2022
Engineering Data Analytics, Australian National University, 2016-2018
Signal and Image Processing II, The University of Queensland, 2010-2012
Grants and Awards
ARC, Can Machines Unlearn? Toward Next-Generation Safe Artificial Intelligence (with Prof. Phung, Prof. Cai, Dr. Le, and Dr. Zhang), A$790k, 2025-2028
US Air Force Research Laboratory, Generalizing Model Adaptation with Geometry: Applications in Model Fine-tuning, Editing, and Personalized AI, A$185k, 2025-2026
ARC, Exploiting Geometries of Learning for Fast, Adaptive and Robust AI (with Prof. Hartley, Prof. Phung and Dr. Le), A$420k, 2023-2025
DARPA, Learning to Learn and Adapt with Less Labels, A$3,260k, 2019-2023
Data61-CSIRO, Trustworthy Learning from Limited Data, A$555k, 2018-2022
NFRFE, AI architectures for reliable prediction and optimization of advanced manufacturing processes (with Prof. A. Milani (UBC)), A$275k, 2020-2021
ARC, Semantic Vectorisation: From Bitmaps to Intelligent Representations (with Prof. Porikli), A$385k, 2015-2019
News
Our work on hyperbolic neural networks has been accepted to IJCV; congratulations to Xiaomeng, and Prof. Zhi, Prof. Wu, and Prof. Jia.
Two papers have been accepted to ICCV'25; congratulations to Jing, Chengyao and Dr. Trung Le.
Our work on machine unlearning in diffusion models has been accepted to CVPR'25; congratulations to Jing and the team.
Our work on compressing 3D Gaussian splitting has been accepted to ICLR'25; congratulations to Yihang and the team.
Will serve as an AC for ICLR'26, NeurIPS'25, ACM-MM'25 and IJCAI'25
Two papers have been accepted to NeurIPS'24; congratulations to Haocheng, Pengxiang, and the team.
Our work on implicit neural representations for super-resolution in medical images has been accepted to WACV'25; congratulations to Mevan.
Three papers have been accepted to ECCV'24; congratulations to Jing, Yihang, Ali, and the team.
Four papers accepted to CVPR'24, congratulations to Henry, Yihang, Yanshuo and the team.
Our work on robust MRI reconstruction has been accepted to ISBI'24; congratulations to Mevan.
Two papers accepted to AAAI'24, congratulations to Nilakshan and Jing.
Our work on robust MRI reconstruction has been accepted to Computers in Biology and Medicine; congratulations to Mevan.
Our work on segmenting volumetric medical data is accepted to Nature Machine Intelligence, huge congratulations to Himashi.
Our work on vector quantized auto-encoders is accepted to ICML'23. Congratulations to Vuong and Le.
Mevan won first place at the Visualize Your Thesis (VYT) competition and will be competing internationally. You can watch Mevan's winning video here.
Our work on Learning to Optimize on Riemannian Manifolds is accepted to TPAMI. Congratulations to Zhi.
Our work on Meta-Learning for manifold data is accepted to TPAMI. Congratulations to Zhi.
Outstanding reviewer, CVPR'21
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