Dr Rohitash Chandra

Dr Rohitash Chandra is a Senior Lecturer in Data Science at the UNSW School of Mathematics and Statistics. Dr Chandra leads a program of research encircling methodologies and applications of artificial intelligence; particularly in areas of Bayesian deep learning, neuro-evolution,  climate extremes, geoscientific models, and mineral exploration.  Dr Chandra has developed novel methods for machine learning inspired by neural systems and learning behaviour that include transfer and multi-task learning, with the goal of modular deep learning. His current interest is uncertainty quantification and deep learning with applications to language models,  vaccine research, and COVID-19.

Dr Chandra has attracted multi-million dollar funding with a leading international interdisciplinary team. He is the Data Theme Lead of the Australian Research Council (ARC ITTC) Training Centre for Data Analytics in Minerals and Resources (2020-2025). Dr Chandra is an Associate Editor (Topical Editor) for Geoscientific Model Development, Neurocomputing (Elsevier), and  IEEE Transactions on Neural Networks and Learning Systems. Dr Chandra is a Senior Member of IEEE and UNSW Cultural Diversity Champion (2021-2023). Dr Chandra is part of NHMRC Medical Research Future Fund (2021-2022) for COVID-19 vaccine testing research led by CSIRO.  

Prior to joining UNSW, Dr Chandra held Sydney Research Fellowship at the University of Sydney (2017 - 2019). Prior to this, he has taken roles as Research Fellow in Machine Learning at Rolls Royce @Corp Lab, Nanyang Technological University, Singapore; Postdoctoral Research Fellow in Bioinformatics at Victoria University of Wellington (January to June 2012), and Lecturer in Computing Science at the University of the South Pacific (2013- 2015). Dr Chandra is originally from Fiji with a Girmit Indian heritage.

Available PhD project with scholarship

• Bayesian deep learning for hydrological models (PhD) with Prof. Lucy Marshall. The project is funded by the Australian Research Council (ARC ITTC) Training Centre for Data Analytics in Minerals and Resources.
• Requirements: First Class Honours or Masters by Research in Computational Statistics, Machine Learning, or Computer Science (with good grades). At least one paper in SJR Q1/Q2 ranked journal or equivalent conference proceeding. Available to Australian and international candidates. Apply: email rohitash.chandra at unsw.edu.au

Available Research Projects 

1. Bayesian deep learning for protein function detection (PhD), Co-supervised by Prof. Alok Sharma
2. Cyclone path and intensity prediction with deep insight based deep learning (Masters/Honours)
3. Indoor path navigation for disabled persons in large buildings (Masters/Honours) 
4. Detection of electric cable hazards from Cyclones using  drones and  remote sensing and deep learning (Masters/Honours) 
5. Dynamic Earth models, landscape dynamics and basin evolution (PhD), Co-supervised by Prof. Dietmar Muller  More details
6. Machine learning for reef modelling and Optimisation, Co-supervised by Prof. Jody Webster More information
7. Deep learning for the reconstruction of 3D Ore-bodies, Co-supervised by  Dr Ehsan Farahbakhsh
8. Memory in Recurrent Neural Networks and Neural Turing Machines, Honours/PhD
9. Bayesian deep learning with incomplete information, Honours/PhD
10. Variational Bayes for Spatio-temporal modelling (Honours/Masters/PhD) with Prof. Robert Kohn
11. COVID-19 Modelling with Graph Neural Networks (Honours/Masters)
12. Pruning Bayesian deep learning (Honours/Masters)
13. Bayesian deep learning for language models  (Honours/Masters/PhD)
14. Sentiment analysis with deep learning during natural disasters and extreme events (Honours/Masters/PhD)
15. Human-robot language translation using deep learning  (Honours/Masters/PhD)
16. Deep learning for monitoring abuse in social media  (Honours/PhD)
17. Ensemble learning for class imbalanced problems (Honours/PhD)  with Dr Rodney Beard 
18. Bayesian deep learning for hydrological models (Honours/PhD) with Prof. Lucy Marshall

Access research papers: https://github.com/rohitash-chandra/research