Field of Research (FoR)
Dr. Rohitash Chandra is a Senior Lecturer in Data Science at the UNSW School of Mathematics and Statistics. Dr. Chandra has a built a program of research encircling methodologies and applications of artificial intelligence; particularly in areas of deep learning, neuro-evolution, Bayesian methods, climate extremes, solid Earth Evolution, reef modelling and mineral exploration. Together with leading international interdisciplinary teams, he has...view more
Dr. Rohitash Chandra is a Senior Lecturer in Data Science at the UNSW School of Mathematics and Statistics. Dr. Chandra has a built a program of research encircling methodologies and applications of artificial intelligence; particularly in areas of deep learning, neuro-evolution, Bayesian methods, climate extremes, solid Earth Evolution, reef modelling and mineral exploration. Together with leading international interdisciplinary teams, he has attracted over 11 million dollars from various funding agencies, including the Deep Carbon Observatory (2018-2019) and the Australian Research Council (ARC ITTC) Training Centre for Data Analytics in Minerals and Resources (2020-2025).
Dr. Chandra held a Sydney Research Fellowship at the School of Geosciences and Centre for Translational Data Science, 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 Nausori, Fiji with a Girmit heritage.
- Bayesian deep learning for protein function detection (PhD), Co-supervised by Prof. Alok Sharma
- Bayesian inference for MoRFs and protein-peptide interactions (PhD), Co-supervised by Prof. Alok Sharma
- Cyclone path and intensity prediction with deep insight based deep learning (Masters/Honours), Co-supervised by Prof. Alok Sharma
- Indoor path navigation for disabled persons in large buildings (Masters/Honours)
- Detection of electric cable hazards from Cyclones using a combination of drones with remote sensing and deep learning (Masters/Honours)
- Geo-tagging plastic pollution in coastlines using drones and remote sensing (Masters/Honours)
- Dynamic Earth models, landscape dynamics and basin evolution (PhD), Co-supervised by Prof. Dietmar Muller More details
- Machine learning for Reef Modelling and Optimisation, Co-supervised by Prof. Jody Webster More information
- Deep learning for the reconstruction of 3D Ore-bodies, Co-supervised by Ehsan Farahbakhsh
- PhD in Artificial Intelligence, Victoria University of Wellington (2012)
- MSc. in Artificial Intelligence, University of Fiji (2008)
- BSc. in Computer Science and Engineering Technology, University of the South Pacific (2006)
- Doctoral Completion Award, Victoria University of Wellington (2012)
- Sydney Fellowship Award, University of Sydney (2017-2019)
My Research Activities
Bayesian neural networks: Markov Chain Monte Carlo (MCMC) methods provide a probabilistic approach for estimation of the free parameters in a wide range of models. Parallel tempering is an MCMC method that features parallelism with enhanced exploration capabilities. It features a number of replicas with slight variations in the acceptance criteria. More recently, I have been developing algorithms for Bayesian neural networks that feature parallel tempering and parallel computing in order to address computationally expensive problems. The challenge is in the inference for deep learning network architectures that features millions of parameters. Collaboration: Prof. Sally Cripps, School of Mathematics and Statistics, University of Sydney.
Surrogate-assisted inference and optimisation: Surrogate-assisted optimization considers the estimation of an objective function for models given computational inefficiency or difficulty to obtain clear results. Surrogate-assistance inference addresses the inefficiency of parallel tempering for large-scale problems by combining parallel computing features with surrogate assisted estimation of likelihood function that describes the plausibility of a model parameter value, given specific observed data. I have been developing these methods for large-scale Bayesian neural networks and also for computationally expensive Geoscientific models such as landscape evolution models. The challenge is to have a good estimation by the surrogates when the actual model features hundreds of free parameters. Collaboration: Prof. Dietmar Muller, School of Geosciences, University of Sydney; Prof. Yew Soon Ong, School of Computer Science and Engineering, Nanyang Technological University, Singapore.
Neural networks and learning algorithms: Neural networks are loosely modelled after biological neural systems and have a wide range of data-driven applications that include time series prediction and pattern recognition. Opposed to gradient-based methods, neuro-evolution features evolutionary algorithms that provide a black-box approach to learning in neural networks. Hence, the learning algorithm is not constrained to the architecture of the network and does not face the limitations of gradient descent such as local minima and vanishing gradients. I have been developing novel neural network learning algorithms using neuro-evolution with motivations from transfer learning, multi-task learning and reinforcement learning. I have been using feedforward and recurrent neural networks with application to a wide range of time series problems that include multidimensional and multi-step ahead prediction with applications that include predicting the behaviour of extreme events such as cyclones. The challenge is in problems that have missing information, noise and inconsistencies in the organisation of data. Collaboration: Prof. Yew Soon Ong, School of Computer Science and Engineering, Nanyang Technological University; Prof. Junbin Gao, Business School, University of Sydney; Prof. Christian Omlin, University of Agder, Norway.
Evolutionary optimisation: Evolutionary algorithms used for optimisation are inspired by the theory of evolution. The major feature of these algorithms is their applicability in large scale problems, particularly that do not have the feature to use gradient information to form new proposals. I have contributed most to the field of cooperative coevolution and problem decomposition for neuro-evolution and large scale global optimisation problems. I would like to extend this field further with Bayesian methods that have a natural way for uncertainty quantification which could address the limitation of convergence in evolutionary optimization and related stochastic and metaheuristic algorithms. Collaboration: Prof. Mengie Zhang, Victoria University of Wellington, New Zealand; Prof. Yew Soon Ong, School of Computer Science and Engineering, Nanyang Technological University, Singapore.
Solid Earth evolution: Bayesian inference has been a popular methodology for the estimation and uncertainty quantification of parameters in geological and geophysical forward models. Badlands is a basin and landscape evolution forward model for simulating topography evolution at a large range of spatial and time scales. Our solid Earth evolution projects consider Bayesian inference for parameter estimation and uncertainty quantification for landscape dynamics model (Bayeslands). The challenge is in parameter estimation for computationally expensive models which are being addressed by high-performance computing and surrogate-assisted Bayesian inversion. Collaboration: Prof. Dietmar Muller and Dr. Tristan Salles, School of Geosciences, University of Sydney.
Reef modelling: Geological reef models such as Py-Reef-Core provides insights into the flux of carbon by analysing carbonate platform growth and demise through time, and modelling their evolution using landscape dynamics and reef modelling. We provide uncertainty quantification estimation of free parameters using Bayesian inference for reef modelling (BayesReef). Bayesian inference via MCMC and parallel tempering is used with Py-Reef-Core model to understand reef evolution on a geological timescale that can help in predicting the future evolution of coral reefs. The challenge here is in the estimation of the parameters which involves highly non-separable and constrained optimisation. Collaboration: A/Prof. Jody Webster and Dr. Tristan Salles, School of Geosciences, University of Sydney.
Mineral exploration: The extraction of geological lineaments from digital satellite data is a fundamental application in remote sensing. The location of geological lineaments such as faults and dykes are of interest in terms of mineralization. Although a wide range of applications utilized computer vision techniques, a standard workflow for application of these techniques to mineral exploration is lacking. We use computer vision techniques for extracting geological lineaments using optical remote sensing data. Furthermore, in another research direction, we provide a synergy of geophysical forward models and Bayesian inference for 3D joint inversion for mineral prospecting and exploration. Collaboration: Prof. Dietmar Muller, Ehsan Farahbakhsh, and Prof. Gregory Houseman, School of Geosciences, University of Sydney. Dr. Hugo Olierook, Prof. Chris Clark, and Prof. Steven Reddy, Curtin University. Dr. Richard Scalzo and Prof. Sally Cripps, Centre for Translational Data Science, University of Sydney.
Paleoclimate reconstruction: The reconstruction of paleoclimate precipitation can provide light to Earth’s climate history of millions of years in the past. Although global circulation models have been used with success for the reconstruction of precipitation in the Miocene period, their application to an era back in time is a major challenge due to limited data. We use an alternate approach that features machine learning methods to predict precipitation that defines paleoclimate that spans up to 400 millions of year in the past. The data features a range of geological indicators including sedimentary deposits (coal, evaporates, glacial deposits). The challenge has been in addressing missing values in the dataset and providing rigorous uncertainty quantification in order to develop paleo-maps of forests and vegetation. Collaboration: Prof. Dietmar Muller, School of Geosciences, and Prof. Sally Cripps, Centre for Translational Data Science, University of Sydney.
My Research Supervision
Areas of supervision
Bayesian inference, deep learning, MCMC methods, Environmental informatics, Earth evolution models, reef evolution models, mineral exploration
- Ehsan Farahbakhsh, “Machine learning for mineral prospecting”, PhD, Tehran Polytechnic, Tehran (External Supervisor, 2017 - 2020)
- Julian Rodriguez, "Machine learning for spatial-temporal mineral prospecting using plate tectonic models, MPhil, University of Sydney (External Supervisor, 2019-2020)
- Ravinesh Deo, "Bayesian neural learning for rainfall prediction in Fiji", MSc, University of the South Pacific (External Supervisor, 2020)
- Yixuan He, "Bayesian neural learning for financial prediction", Master of Financial Mathematics, UNSW School of Mathematics and Statistics (Principal Supervisor, 2020)
Master of Data Science:
- ZZSC5836 - Data Mining and Machine Learning (Online), Hexamester 5 https://studyonline.unsw.edu.au/online-programs/master-data-science
- MATH5836 - Data Mining and its Business Applications, Trimester 3: https://www.maths.unsw.edu.au/courses/math5836-data-mining