Dr Rohitash Chandra
Senior Lecturer

Dr Rohitash Chandra

  1. PhD in Artificial Intelligence, Victoria University of Wellington (2012)
  2. MSc. in Artificial Intelligence, University of Fiji (2008)
  3. BSc. in Computer Science and Engineering Technology, University of the South Pacific (2006)
Science
School of Mathematics & Statistics

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 one of the Chief Investigators of the NHMRC Medical Research Future Fund (2021-2022) project on the use of machine learning for COVID-19 drug repurposing and leads the UNSW’s subcontract in Food and Drug Administration (FDA) funded project (October 2021-2024) on improving COVID-19 models with machine learning.

Apart from science, Dr Chandra takes a lot of interest in literature and humanities and has edited and published poetry collections. His current research explores the use of deep learning-based language models for studying ancient philosophical texts. Dr Chandra is a strong advocate of human rights and diversity and is a UNSW Cultural Diversity Champion (2021-2023).

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.

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 an Associate Fellow of the British Higher Education Academy (HEA).

Phone
0413071839
Location
School of Mathematics and Statistics UNSW Sydney NSW 2052 The Red Centre Room 4110
  • Book Chapters | 2019
    Deo R; Chandra R, 2019, 'Multi-step-ahead Cyclone Intensity Prediction with Bayesian Neural Networks', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Yanuca Island, Fiji, pp. 282 - 295, http://dx.doi.org/10.1007/978-3-030-29911-8_22
    Book Chapters | 2017
    Chandra R, 2017, 'Co-evolutionary multi-task learning for modular pattern classification', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 692 - 701, http://dx.doi.org/10.1007/978-3-319-70136-3_73
    Book Chapters | 2017
    Chandra R, 2017, 'Dynamic cyclone wind-intensity prediction using co-evolutionary multi-task learning', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 618 - 627, http://dx.doi.org/10.1007/978-3-319-70139-4_63
    Book Chapters | 2017
    Chandra R, 2017, 'Multi-task modular backpropagation for feature-based pattern classification', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 558 - 566, http://dx.doi.org/10.1007/978-3-319-70136-3_59
    Book Chapters | 2017
    Chandra R, 2017, 'Towards an affective computational model for machine consciousness', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 897 - 907, http://dx.doi.org/10.1007/978-3-319-70139-4_91
    Book Chapters | 2017
    Chandra R; Azizi L; Cripps S, 2017, 'Bayesian neural learning via langevin dynamics for chaotic time series prediction', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 564 - 573, http://dx.doi.org/10.1007/978-3-319-70139-4_57
    Book Chapters | 2016
    Chandra R; Gupta A; Ong YS; Goh CK, 2016, 'Evolutionary multi-task learning for modular training of feedforward neural networks', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 37 - 46, http://dx.doi.org/10.1007/978-3-319-46672-9_5
    Book Chapters | 2016
    Chaudhry S; Chandra R, 2016, 'Unconstrained face detection from a mobile source using convolutional neural networks', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 567 - 576, http://dx.doi.org/10.1007/978-3-319-46672-9_63
    Book Chapters | 2016
    Hussein S; Chandra R, 2016, 'Chaotic feature selection and reconstruction in time series prediction', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 3 - 11, http://dx.doi.org/10.1007/978-3-319-46675-0_1
    Book Chapters | 2016
    Nand R; Chandra R, 2016, 'Coevolutionary feature selection and reconstruction in neuro-evolution for time series prediction', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 285 - 297, http://dx.doi.org/10.1007/978-3-319-28270-1_24
    Book Chapters | 2016
    Nand R; Chandra R, 2016, 'Competitive Island cooperative neuro-evolution of feedforward networks for time series prediction', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 160 - 170, http://dx.doi.org/10.1007/978-3-319-28270-1_14
    Book Chapters | 2016
    Nand R; Chandra R, 2016, 'Reverse neuron level decomposition for cooperative neuro-evolution of feedforward networks for time series prediction', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 171 - 182, http://dx.doi.org/10.1007/978-3-319-28270-1_15
    Book Chapters | 2016
    Wong G; Chandra R; Sharma A, 2016, 'Memetic cooperative neuro-evolution for chaotic time series prediction', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 299 - 308, http://dx.doi.org/10.1007/978-3-319-46675-0_33
    Book Chapters | 2015
    Bali KK; Chandra R, 2015, 'Multi-island competitive cooperative coevolution for real parameter global optimization', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 127 - 136, http://dx.doi.org/10.1007/978-3-319-26555-1_15
    Book Chapters | 2015
    Bali KK; Chandra R, 2015, 'Scaling up multi-island competitive cooperative coevolution for real parameter global optimisation', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 34 - 48, http://dx.doi.org/10.1007/978-3-319-26350-2_4
    Book Chapters | 2015
    Bali KK; Chandra R; Omidvar MN, 2015, 'Competitive island-based cooperative coevolution for efficient optimization of large-scale fully-separable continuous functions', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 137 - 147, http://dx.doi.org/10.1007/978-3-319-26555-1_16
    Book Chapters | 2015
    Chandra R; Dayal KS, 2015, 'Coevolutionary recurrent neural networks for prediction of rapid intensification in wind intensity of tropical cyclones in the south pacific region', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 43 - 52, http://dx.doi.org/10.1007/978-3-319-26555-1_6
    Book Chapters | 2015
    Nand R; Chandra R, 2015, 'Neuron-synapse level problem decomposition method for cooperative neuro-evolution of feedforward networks for time series prediction', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 90 - 100, http://dx.doi.org/10.1007/978-3-319-26555-1_11
    Book Chapters | 2015
    Wong G; Chandra R, 2015, 'Enhancing competitive island cooperative neuro-evolution through backpropagation for pattern classification', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 293 - 301, http://dx.doi.org/10.1007/978-3-319-26532-2_32
    Book Chapters | 2012
    Chandra R; Zhang M; Peng L, 2012, 'Application of cooperative convolution optimization for 13C metabolic flux analysis: Simulation of isotopic labeling patterns based on tandem mass spectrometry measurements', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 178 - 187, http://dx.doi.org/10.1007/978-3-642-34859-4_18
    Book Chapters | 2010
    Chandra R; Frean M; Zhang M, 2010, 'An encoding scheme for cooperative coevolutionary feedforward neural networks', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 253 - 262, http://dx.doi.org/10.1007/978-3-642-17432-2_26
    Book Chapters | 2009
    Chandra R; Zhang M; Rolland L, 2009, 'Solving the forward kinematics of the 3RPR planar parallel manipulator using a hybrid meta-heuristic paradigm', in Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA, pp. 177 - 182, http://dx.doi.org/10.1109/CIRA.2009.5423213
  • Journal articles | 2022
    Anshuka A; Chandra R; Buzacott AJV; Sanderson D; van Ogtrop FF, 2022, 'Spatio temporal hydrological extreme forecasting framework using LSTM deep learning model', Stochastic Environmental Research and Risk Assessment, http://dx.doi.org/10.1007/s00477-022-02204-3
    Journal articles | 2022
    Chandra R; Jain A; Chauhan DS, 2022, 'Deep learning via LSTM models for COVID-19 infection forecasting in India', PLoS ONE, vol. 17, http://dx.doi.org/10.1371/journal.pone.0262708
    Journal articles | 2022
    Chandra R; Jain M; Maharana M; Krivitsky PN, 2022, 'Revisiting Bayesian Autoencoders With MCMC', IEEE Access, vol. 10, pp. 40482 - 40495, http://dx.doi.org/10.1109/ACCESS.2022.3163270
    Journal articles | 2022
    Chandra R; Kulkarni V, 2022, 'Semantic and Sentiment Analysis of Selected Bhagavad Gita Translations Using BERT-Based Language Framework', IEEE Access, vol. 10, pp. 21291 - 21315, http://dx.doi.org/10.1109/ACCESS.2022.3152266
    Journal articles | 2022
    Chandra R; Tiwari A, 2022, 'Distributed Bayesian optimisation framework for deep neuroevolution', Neurocomputing, vol. 470, pp. 51 - 65, http://dx.doi.org/10.1016/j.neucom.2021.10.045
    Journal articles | 2022
    Kumar AK; Ritam M; Han L; Guo S; Chandra R, 2022, 'Deep learning for predicting respiratory rate from biosignals', Computers in Biology and Medicine, vol. 144, pp. 105338 - 105338, http://dx.doi.org/10.1016/j.compbiomed.2022.105338
    Journal articles | 2022
    Sharma A; Singh PK; Chandra R, 2022, 'SMOTified-GAN for Class Imbalanced Pattern Classification Problems', IEEE Access, vol. 10, pp. 30655 - 30665, http://dx.doi.org/10.1109/ACCESS.2022.3158977
    Journal articles | 2022
    Shirmard H; Farahbakhsh E; Heidari E; Pour AB; Pradhan B; Müller D; Chandra R, 2022, 'A Comparative Study of Convolutional Neural Networks and Conventional Machine Learning Models for Lithological Mapping Using Remote Sensing Data', Remote Sensing, vol. 14, pp. 819 - 819, http://dx.doi.org/10.3390/rs14040819
    Journal articles | 2022
    Shirmard H; Farahbakhsh E; Müller RD; Chandra R, 2022, 'A review of machine learning in processing remote sensing data for mineral exploration', Remote Sensing of Environment, vol. 268, http://dx.doi.org/10.1016/j.rse.2021.112750
    Journal articles | 2021
    Chandra R; Bhagat A; Maharana M; Krivitsky PN, 2021, 'Bayesian Graph Convolutional Neural Networks via Tempered MCMC', IEEE Access, vol. 9, pp. 130353 - 130365, http://dx.doi.org/10.1109/ACCESS.2021.3111898
    Journal articles | 2021
    Chandra R; Cripps S; Butterworth N; Muller RD, 2021, 'Precipitation reconstruction from climate-sensitive lithologies using Bayesian machine learning', Environmental Modelling and Software, vol. 139, pp. 105002 - 105002, http://dx.doi.org/10.1016/j.envsoft.2021.105002
    Journal articles | 2021
    Chandra R; Goyal S; Gupta R, 2021, 'Evaluation of Deep Learning Models for Multi-Step Ahead Time Series Prediction', IEEE Access, vol. 9, pp. 83105 - 83123, http://dx.doi.org/10.1109/ACCESS.2021.3085085
    Journal articles | 2021
    Chandra R; He Y, 2021, 'Bayesian neural networks for stock price forecasting before and during COVID-19 pandemic', PLoS ONE, vol. 16, pp. e0253217, http://dx.doi.org/10.1371/journal.pone.0253217
    Journal articles | 2021
    Chandra R; Krishna A, 2021, 'COVID-19 sentiment analysis via deep learning during the rise of novel cases', PLoS ONE, vol. 16, pp. e0255615, http://dx.doi.org/10.1371/journal.pone.0255615
    Journal articles | 2021
    Chandra R; Saini R, 2021, 'Biden vs Trump: Modeling US General Elections Using BERT Language Model', IEEE Access, vol. 9, pp. 128494 - 128505, http://dx.doi.org/10.1109/ACCESS.2021.3111035
    Journal articles | 2021
    Diaz-Rodriguez J; Müller RD; Chandra R, 2021, 'Predicting the emplacement of Cordilleran porphyry copper systems using a spatio-temporal machine learning model', Ore Geology Reviews, vol. 137, pp. 104300 - 104300, http://dx.doi.org/10.1016/j.oregeorev.2021.104300
    Journal articles | 2021
    Olierook HKH; Scalzo R; Kohn D; Chandra R; Farahbakhsh E; Clark C; Reddy SM; Müller RD, 2021, 'Bayesian geological and geophysical data fusion for the construction and uncertainty quantification of 3D geological models', Geoscience Frontiers, vol. 12, pp. 479 - 493, http://dx.doi.org/10.1016/j.gsf.2020.04.015
    Journal articles | 2020
    Chandra R; Azam D; Kapoor A; Dietmar Müller R, 2020, 'Surrogate-assisted Bayesian inversion for landscape and basin evolution models', Geoscientific Model Development, vol. 13, pp. 2959 - 2979, http://dx.doi.org/10.5194/gmd-13-2959-2020
    Journal articles | 2020
    Chandra R; Jain K; Kapoor A; Aman A, 2020, 'Surrogate-assisted parallel tempering for Bayesian neural learning', Engineering Applications of Artificial Intelligence, vol. 94, http://dx.doi.org/10.1016/j.engappai.2020.103700
    Journal articles | 2020
    Chandra R; Kapoor A, 2020, 'Bayesian neural multi-source transfer learning', Neurocomputing, vol. 378, pp. 54 - 64, http://dx.doi.org/10.1016/j.neucom.2019.10.042
    Journal articles | 2020
    Farahbakhsh E; Chandra R; Olierook HKH; Scalzo R; Clark C; Reddy SM; Müller RD, 2020, 'Computer vision-based framework for extracting tectonic lineaments from optical remote sensing data', International Journal of Remote Sensing, vol. 41, pp. 1760 - 1787, http://dx.doi.org/10.1080/01431161.2019.1674462
    Journal articles | 2020
    Farahbakhsh E; Hezarkhani A; Eslamkish T; Bahroudi A; Chandra R, 2020, 'Three-dimensional weights of evidence modelling of a deep-seated porphyry cu deposit', Geochemistry: Exploration, Environment, Analysis, vol. 20, pp. 480 - 495, http://dx.doi.org/10.1144/geochem2020-038
    Journal articles | 2020
    Pall J; Chandra R; Azam D; Salles T; Webster JM; Scalzo R; Cripps S, 2020, 'Bayesreef: A Bayesian inference framework for modelling reef growth in response to environmental change and biological dynamics', Environmental Modelling and Software, vol. 125, pp. 104610 - 104610, http://dx.doi.org/10.1016/j.envsoft.2019.104610
    Journal articles | 2020
    Shirmard H; Farahbakhsh E; Pour AB; Muslim AM; Dietmar Müller R; Chandra R, 2020, 'Integration of selective dimensionality reduction techniques for mineral exploration using ASTER satellite data', Remote Sensing, vol. 12, http://dx.doi.org/10.3390/RS12081261
    Journal articles | 2019
    Chandra R; Azam D; Müller RD; Salles T; Cripps S, 2019, 'Bayeslands: A Bayesian inference approach for parameter uncertainty quantification in Badlands', Computers and Geosciences, vol. 131, pp. 89 - 101, http://dx.doi.org/10.1016/j.cageo.2019.06.012
    Journal articles | 2019
    Chandra R; Jain K; Deo RV; Cripps S, 2019, 'Langevin-gradient parallel tempering for Bayesian neural learning', Neurocomputing, vol. 359, pp. 315 - 326, http://dx.doi.org/10.1016/j.neucom.2019.05.082
    Journal articles | 2019
    Chandra R; Müller RD; Azam D; Deo R; Butterworth N; Salles T; Cripps S, 2019, 'Multicore Parallel Tempering Bayeslands for Basin and Landscape Evolution', Geochemistry, Geophysics, Geosystems, vol. 20, pp. 5082 - 5104, http://dx.doi.org/10.1029/2019GC008465
    Journal articles | 2019
    Farahbakhsh E; Chandra R; Eslamkish T; Müller RD, 2019, 'Modeling geochemical anomalies of stream sediment data through a weighted drainage catchment basin method for detecting porphyry Cu-Au mineralization', Journal of Geochemical Exploration, vol. 204, pp. 12 - 32, http://dx.doi.org/10.1016/j.gexplo.2019.05.003
    Journal articles | 2019
    Scalzo R; Kohn D; Olierook H; Houseman G; Chandra R; Girolami M; Cripps S, 2019, 'Efficiency and robustness in Monte Carlo sampling for 3-D geophysical inversions with Obsidian v0.1.2: Setting up for success', Geoscientific Model Development, vol. 12, pp. 2941 - 2960, http://dx.doi.org/10.5194/gmd-12-2941-2019
    Journal articles | 2018
    Chandra R; Cripps S, 2018, 'Coevolutionary multi-task learning for feature-based modular pattern classification', Neurocomputing, vol. 319, pp. 164 - 175, http://dx.doi.org/10.1016/j.neucom.2018.08.011
    Journal articles | 2018
    Chandra R; Gupta A; Ong YS; Goh CK, 2018, 'Evolutionary Multi-task Learning for Modular Knowledge Representation in Neural Networks', Neural Processing Letters, vol. 47, pp. 993 - 1009, http://dx.doi.org/10.1007/s11063-017-9718-z
    Journal articles | 2018
    Chandra R; Ong YS; Goh CK, 2018, 'Co-evolutionary multi-task learning for dynamic time series prediction', Applied Soft Computing Journal, vol. 70, pp. 576 - 589, http://dx.doi.org/10.1016/j.asoc.2018.05.041
    Journal articles | 2017
    Chandra R; Ong YS; Goh CK, 2017, 'Co-evolutionary multi-task learning with predictive recurrence for multi-step chaotic time series prediction', Neurocomputing, vol. 243, pp. 21 - 34, http://dx.doi.org/10.1016/j.neucom.2017.02.065
    Journal articles | 2017
    Chaudhry S; Chandra R, 2017, 'Face detection and recognition in an unconstrained environment for mobile visual assistive system', Applied Soft Computing Journal, vol. 53, pp. 168 - 180, http://dx.doi.org/10.1016/j.asoc.2016.12.035
    Journal articles | 2016
    Chandra R; Chand S, 2016, 'Evaluation of co-evolutionary neural network architectures for time series prediction with mobile application in finance', Applied Soft Computing Journal, vol. 49, pp. 462 - 473, http://dx.doi.org/10.1016/j.asoc.2016.08.029
    Journal articles | 2016
    Rolland L; Chandra R, 2016, 'The forward kinematics of the 6-6 parallel manipulator using an evolutionary algorithm based on generalized generation gap with parent-centric crossover', Robotica, vol. 34, pp. 1 - 22, http://dx.doi.org/10.1017/S0263574714001362
    Journal articles | 2015
    Chandra R, 2015, 'Competition and collaboration in cooperative coevolution of elman recurrent neural networks for time-series prediction', IEEE Transactions on Neural Networks and Learning Systems, vol. 26, pp. 3123 - 3136, http://dx.doi.org/10.1109/TNNLS.2015.2404823
    Journal articles | 2015
    Chandra R; Rolland L, 2015, 'Global–local population memetic algorithm for solving the forward kinematics of parallel manipulators', Connection Science, vol. 27, pp. 22 - 39, http://dx.doi.org/10.1080/09540091.2014.948385
    Journal articles | 2014
    Chandra R, 2014, 'Memetic cooperative coevolution of Elman recurrent neural networks', Soft Computing, vol. 18, pp. 1549 - 1559, http://dx.doi.org/10.1007/s00500-013-1160-1
    Journal articles | 2012
    Chandra R; Frean M; Zhang M, 2012, 'Adapting modularity during learning in cooperative co-evolutionary recurrent neural networks', Soft Computing, vol. 16, pp. 1009 - 1020, http://dx.doi.org/10.1007/s00500-011-0798-9
    Journal articles | 2012
    Chandra R; Frean M; Zhang M, 2012, 'Crossover-based local search in cooperative co-evolutionary feedforward neural networks', Applied Soft Computing Journal, vol. 12, pp. 2924 - 2932, http://dx.doi.org/10.1016/j.asoc.2012.04.010
    Journal articles | 2012
    Chandra R; Frean M; Zhang M, 2012, 'On the issue of separability for problem decomposition in cooperative neuro-evolution', Neurocomputing, vol. 87, pp. 33 - 40, http://dx.doi.org/10.1016/j.neucom.2012.02.005
    Journal articles | 2012
    Chandra R; Zhang M, 2012, 'Cooperative coevolution of Elman recurrent neural networks for chaotic time series prediction', Neurocomputing, vol. 86, pp. 116 - 123, http://dx.doi.org/10.1016/j.neucom.2012.01.014
    Journal articles | 2011
    Chandra R; Frean M; Zhang M; Omlin CW, 2011, 'Encoding subcomponents in cooperative co-evolutionary recurrent neural networks', Neurocomputing, vol. 74, pp. 3223 - 3234, http://dx.doi.org/10.1016/j.neucom.2011.05.003
    Journal articles | 2011
    Chandra R; Rolland L, 2011, 'On solving the forward kinematics of 3RPR planar parallel manipulator using hybrid metaheuristics', Applied Mathematics and Computation, vol. 217, pp. 8997 - 9008, http://dx.doi.org/10.1016/j.amc.2011.03.106
    Journal articles | 2009
    Chandra R; Knight R; Omlin CW, 2009, 'Renosterveld conservation in South Africa: A case study for handling uncertainty in knowledge-based neural networks for environmental management', Journal of Environmental Informatics, vol. 13, pp. 56 - 65, http://dx.doi.org/10.3808/jei.200900140
  • Working Papers | 2021
    Chandra R; Jain A; Chauhan DS, 2021, Deep learning via LSTM models for COVID-19 infection forecasting in India, http://dx.doi.org, http://dx.doi.org/10.1371/journal.pone.0262708
    Working Papers | 2021
    Chandra R; Jain M; Maharana M; Krivitsky PN, 2021, Revisiting Bayesian Autoencoders with MCMC, http://dx.doi.org, http://dx.doi.org/10.1109/ACCESS.2022.3163270
    Working Papers | 2021
    Renanse A; Chandra R; Sharma A, 2021, Memory Capacity of Neural Turing Machines with Matrix Representation, http://dx.doi.org, http://arxiv.org/abs/2104.07454v1
    Working Papers | 2021
    Sharma A; Singh PK; Chandra R, 2021, SMOTified-GAN for class imbalanced pattern classification problems, http://dx.doi.org, http://dx.doi.org/10.1109/ACCESS.2022.3158977
    Working Papers | 2021
    Tiwari A; Gupta R; Chandra R, 2021, Delhi air quality prediction using LSTM deep learning models with a focus on COVID-19 lockdown, http://dx.doi.org, http://arxiv.org/abs/2102.10551v1
    Working Papers | 2017
    Chandra R, 2017, An affective computational model for machine consciousness, http://dx.doi.org, http://arxiv.org/abs/1701.00349v1
    Working Papers | 2017
    Chandra R, 2017, Towards prediction of rapid intensification in tropical cyclones with recurrent neural networks, http://dx.doi.org, http://arxiv.org/abs/1701.04518v1
    Working Papers | 2015
    Abel D; Gavidi B; Rollings N; Chandra R, 2015, Development of an Android Application for an Electronic Medical Record System in an Outpatient Environment for Healthcare in Fiji, http://dx.doi.org, http://arxiv.org/abs/1503.00810v1
    Working Papers | 2015
    Reddy E; Kumar S; Rollings N; Chandra R, 2015, Mobile Application for Dengue Fever Monitoring and Tracking via GPS: Case Study for Fiji, http://dx.doi.org, http://arxiv.org/abs/1503.00814v1
  • Preprints | 2022
    Chand S; Rajesh K; Chandra R, 2022, MAP-Elites based Hyper-Heuristic for the Resource Constrained Project Scheduling Problem, http://arxiv.org/abs/2204.11162v1
    Preprints | 2022
    Chandra R; Ranjan M, 2022, Artificial intelligence for topic modelling in Hindu philosophy: mapping themes between the Upanishads and the Bhagavad Gita, http://arxiv.org/abs/2205.11020v1
    Preprints | 2022
    Chandra R; Sharma YV, 2022, Surrogate-assisted distributed swarm optimisation for computationally expensive models, http://arxiv.org/abs/2201.06843v1
    Preprints | 2021
    Chandra R; Bhagat A; Maharana M; Krivitsky PN, 2021, Bayesian graph convolutional neural networks via tempered MCMC
    Preprints | 2021
    Chandra R; Jain M; Maharana M; Krivitsky PN, 2021, Revisiting Bayesian Autoencoders with MCMC
    Preprints | 2021
    Shirmard H; Farahbakhsh E; Muller RD; Chandra R, 2021, A review of machine learning in processing remote sensing data for mineral exploration
    Software / Code | 2020
    Farahbakhsh E; Hezarkhani A; Eslamkish T; Bahroudi A; Chandra R, 2020, 3DWofE: An open-source software package for three-dimensional weights of evidence modeling[Formula presented], Elsevier BV, Published: 01 November 2020, Software / Code, http://dx.doi.org/10.1016/j.simpa.2020.100039
    Conference Presentations | 2019
    Chandra R; Azam D; Dietmar Müller R, 2019, 'Probabilistic modelling of sedimentary basin evolution using Bayeslands', Vol. 2019, pp. 1 - 5, http://dx.doi.org/10.1080/22020586.2019.12073181
    Preprints | 2019
    Farahbakhsh E; Hezarkhani A; Eslamkish T; Bahroudi A; Chandra R, 2019, Three-dimensional weights of evidence modeling of a deep-seated porphyry Cu deposit
    Conference Presentations | 2018
    Alac R; Zahirovic S; Salles T; Muller D; Cripps S; Ramos F; Chandra R, 2018, 'Surface Process Models of The Lake Eyre Basin Using Badlands Software', Vol. 2018, pp. 1 - 1, http://dx.doi.org/10.1071/aseg2018abp011
    Conference Papers | 2018
    Chandra R, 2018, 'Multi-Task Modular Backpropagation for Dynamic Time Series Prediction', in Proceedings of the International Joint Conference on Neural Networks, http://dx.doi.org/10.1109/IJCNN.2018.8489740
    Preprints | 2018
    Chandra R; Azam D; Kapoor A; Müller RD, 2018, Surrogate-assisted Bayesian inversion for landscape and basin evolution models
    Preprints | 2018
    Chandra R; Azam D; Müller RD; Salles T; Cripps S, 2018, Bayeslands: A Bayesian inference approach for parameter uncertainty quantification in Badlands
    Conference Papers | 2018
    Chandra R; Cripps AS, 2018, 'Bayesian Multi-task Learning for Dynamic Time Series Prediction', in Proceedings of the International Joint Conference on Neural Networks, http://dx.doi.org/10.1109/IJCNN.2018.8489323
    Preprints | 2018
    Chandra R; Jain K; Deo RV; Cripps S, 2018, Langevin-gradient parallel tempering for Bayesian neural learning
    Preprints | 2018
    Chandra R; Jain K; Kapoor A; Aman A, 2018, Surrogate-assisted parallel tempering for Bayesian neural learning
    Preprints | 2018
    Chandra R; Müller RD; Azam D; Deo R; Butterworth N; Salles T; Cripps S, 2018, Multi-core parallel tempering Bayeslands for basin and landscape evolution
    Preprints | 2018
    Farahbakhsh E; Chandra R; Olierook HKH; Scalzo R; Clark C; Reddy SM; Muller RD, 2018, Computer vision-based framework for extracting geological lineaments from optical remote sensing data
    Preprints | 2018
    Pall J; Chandra R; Azam D; Salles T; Webster JM; Scalzo R; Cripps S, 2018, Bayesreef: A Bayesian inference framework for modelling reef growth in response to environmental change and biological dynamics
    Preprints | 2018
    Scalzo R; Kohn D; Olierook H; Houseman G; Chandra R; Girolami M; Cripps S, 2018, Efficiency and robustness in Monte Carlo sampling of 3-D geophysical inversions with Obsidian v0.1.2: Setting up for success
    Conference Papers | 2018
    Wong G; Sharma A; Chandra R, 2018, 'Information Collection Strategies in Memetic Cooperative Neuroevolution for Time Series Prediction', in Proceedings of the International Joint Conference on Neural Networks, http://dx.doi.org/10.1109/IJCNN.2018.8489184
    Conference Papers | 2018
    Zhang Y; Chandra R; Gao J, 2018, 'Cyclone Track Prediction with Matrix Neural Networks', in Proceedings of the International Joint Conference on Neural Networks, http://dx.doi.org/10.1109/IJCNN.2018.8489077
    Preprints | 2017
    Chandra R; Ong Y-S; Goh C-K, 2017, Co-evolutionary multi-task learning for dynamic time series prediction
    Preprints | 2017
    Deo RV; Chandra R; Sharma A, 2017, Stacked transfer learning for tropical cyclone intensity prediction
    Conference Papers | 2017
    Tan AW; Sagarna R; Gupta A; Chandra R; Ong YS, 2017, 'Coping with Data Scarcity in Aircraft Engine Design', in 18th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, American Institute of Aeronautics and Astronautics, presented at 18th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, http://dx.doi.org/10.2514/6.2017-4434
    Conference Papers | 2016
    Bali K; Chandra R; Omidvar MN, 2016, 'Contribution based multi-island competitive cooperative coevolution', in 2016 IEEE Congress on Evolutionary Computation, CEC 2016, pp. 1823 - 1830, http://dx.doi.org/10.1109/CEC.2016.7744010
    Conference Papers | 2016
    Chandra R; Deo R; Bali K; Sharma A, 2016, 'On the relationship of degree of separability with depth of evolution in decomposition for cooperative coevolution', in 2016 IEEE Congress on Evolutionary Computation, CEC 2016, pp. 4823 - 4830, http://dx.doi.org/10.1109/CEC.2016.7744408
    Conference Papers | 2016
    Chandra R; Deo R; Omlin CW, 2016, 'An architecture for encoding two-dimensional cyclone track prediction problem in coevolutionary recurrent neural networks', in Proceedings of the International Joint Conference on Neural Networks, pp. 4865 - 4872, http://dx.doi.org/10.1109/IJCNN.2016.7727839
    Conference Papers | 2016
    Deo R; Chandra R, 2016, 'Identification of minimal timespan problem for recurrent neural networks with application to cyclone wind-intensity prediction', in Proceedings of the International Joint Conference on Neural Networks, pp. 489 - 496, http://dx.doi.org/10.1109/IJCNN.2016.7727239
    Conference Papers | 2016
    Hussein S; Chandra R; Sharma A, 2016, 'Multi-step-ahead chaotic time series prediction using coevolutionary recurrent neural networks', in 2016 IEEE Congress on Evolutionary Computation, CEC 2016, pp. 3084 - 3091, http://dx.doi.org/10.1109/CEC.2016.7744179
    Conference Papers | 2016
    Rana M; Chandra R; Agelidis VG, 2016, 'Cooperative neuro-evolutionary recurrent neural networks for solar power prediction', in 2016 IEEE Congress on Evolutionary Computation, CEC 2016, pp. 4691 - 4698, http://dx.doi.org/10.1109/CEC.2016.7744389
    Preprints | 2015
    Abel D; Gavidi B; Rollings N; Chandra R, 2015, Development of an Android Application for an Electronic Medical Record System in an Outpatient Environment for Healthcare in Fiji
    Conference Papers | 2015
    Chandra R, 2015, 'Multi-objective cooperative neuro-evolution of recurrent neural networks for time series prediction', in 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings, pp. 101 - 108, http://dx.doi.org/10.1109/CEC.2015.7256880
    Conference Papers | 2015
    Chandra R; Bali K, 2015, 'Competitive two-island cooperative coevolution for real parameter global optimisation', in 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings, pp. 93 - 100, http://dx.doi.org/10.1109/CEC.2015.7256879
    Conference Papers | 2015
    Chandra R; Dayal K, 2015, 'Cooperative neuro-evolution of Elman recurrent networks for tropical cyclone wind-intensity prediction in the South Pacific region', in 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings, pp. 1784 - 1791, http://dx.doi.org/10.1109/CEC.2015.7257103
    Conference Papers | 2015
    Chandra R; Dayal K; Rollings N, 2015, 'Application of cooperative neuro-evolution of Elman recurrent networks for a two-dimensional cyclone track prediction for the south pacific region', in Proceedings of the International Joint Conference on Neural Networks, http://dx.doi.org/10.1109/IJCNN.2015.7280394
    Conference Papers | 2015
    Chandra R; Wong G, 2015, 'Competitive two-island cooperative co-evolution for training feedforward neural networks for pattern classification problems', in Proceedings of the International Joint Conference on Neural Networks, http://dx.doi.org/10.1109/IJCNN.2015.7280349
    Preprints | 2015
    Reddy E; Kumar S; Rollings N; Chandra R, 2015, Mobile Application for Dengue Fever Monitoring and Tracking via GPS: Case Study for Fiji
    Conference Papers | 2014
    Chand S; Chandra R, 2014, 'Cooperative coevolution of feed forward neural networks for financial time series problem', in Proceedings of the International Joint Conference on Neural Networks, pp. 202 - 209, http://dx.doi.org/10.1109/IJCNN.2014.6889568
    Conference Papers | 2014
    Chand S; Chandra R, 2014, 'Multi-objective cooperative coevolution of neural networks for time series prediction', in Proceedings of the International Joint Conference on Neural Networks, pp. 190 - 197, http://dx.doi.org/10.1109/IJCNN.2014.6889442
    Conference Papers | 2014
    Chandra R, 2014, 'Competitive two-island cooperative coevolution for training Elman recurrent networks for time series prediction', in Proceedings of the International Joint Conference on Neural Networks, pp. 565 - 572, http://dx.doi.org/10.1109/IJCNN.2014.6889421
    Conference Papers | 2014
    Singh V; Bali A; Adhikthikar A; Chandra R, 2014, 'Web and mobile based tourist travel guide system for Fiji's tourism industry', in Asia-Pacific World Congress on Computer Science and Engineering, APWC on CSE 2014, http://dx.doi.org/10.1109/APWCCSE.2014.7053840
    Conference Papers | 2013
    Chandra R, 2013, 'Adaptive problem decomposition in cooperative coevolution of recurrent networks for time series prediction', in Proceedings of the International Joint Conference on Neural Networks, http://dx.doi.org/10.1109/IJCNN.2013.6706997
    Conference Papers | 2011
    Chandra R; Frean M; Zhang M, 2011, 'A memetic framework for cooperative coevolution of recurrent neural networks', in Proceedings of the International Joint Conference on Neural Networks, pp. 673 - 680, http://dx.doi.org/10.1109/IJCNN.2011.6033286
    Conference Papers | 2011
    Chandra R; Frean M; Zhang M, 2011, 'Modularity adaptation in cooperative coevolution of feedforward neural networks', in Proceedings of the International Joint Conference on Neural Networks, pp. 681 - 688, http://dx.doi.org/10.1109/IJCNN.2011.6033287
    Conference Papers | 2010
    Rolland L; Chandra R, 2010, 'On solving the forward kinematics of the 6-6 general parallel manipulator with an efficient evolutionary algorithm', in CISM International Centre for Mechanical Sciences, Courses and Lectures, pp. 117 - 124, http://dx.doi.org/10.1007/978-3-7091-0277-0_13
    Conference Papers | 2009
    Chandra R; Frean M; Rolland L, 2009, 'A meta-heuristic paradigm for solving the forward kinematics of 6-6 general parallel manipulator', in Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA, pp. 171 - 176, http://dx.doi.org/10.1109/CIRA.2009.5423212
    Conference Papers | 2009
    Chandra R; Zhang M; Rolland L, 2009, 'Solving the Forward Kinematics of the 3RPR Planar Parallel Manipulator using a Hybrid Meta-Heuristic Paradigm', pp. 1 - 6, http://dx.doi.org/10.1109/cira.2009.5423213
    Conference Papers | 2009
    Rolland L; Chandra R, 2009, 'Forward kinematics of the 3RPR planar parallel manipulators using real coded Genetic Algorithms', in 2009 24th International Symposium on Computer and Information Sciences, ISCIS 2009, pp. 381 - 386, http://dx.doi.org/10.1109/ISCIS.2009.5291810
    Conference Papers | 2009
    Rolland L; Chandra R, 2009, 'Forward kinematics of the 6-6 general parallel manipulator using real coded genetic algorithms', in IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM, pp. 1637 - 1642, http://dx.doi.org/10.1109/AIM.2009.5229824
    Conference Papers | 2008
    Chandra R; Omlin CW, 2008, 'Hybrid evolutionary one-step gradient descent for training recurrent neural networks', in Proceedings of the 2008 International Conference on Genetic and Evolutionary Methods, GEM 2008, pp. 305 - 311
    Conference Papers | 2007
    Chandra R; Omlin CW, 2007, 'A hybrid recurrent neural networks architecture inspired by hidden Markov models: Training and extraction of deterministic finite automaton', in International Conference on Artificial Intelligence and Pattern Recognition 2007, AIPR 2007, pp. 278 - 285
    Conference Papers | 2007
    Chandra R; Omlin CW, 2007, 'The comparison and combination of genetic and gradient descent learning in recurrent neural networks: An application to speech phoneme classification', in International Conference on Artificial Intelligence and Pattern Recognition 2007, AIPR 2007, pp. 286 - 293
    Conference Papers | 2006
    Chandra R; Omlin CW, 2006, 'Training and extraction of fuzzy finite state automata in recurrent neural networks', in Proceedings of the 2nd IASTED International Conference on Computational Intelligence, CI 2006, pp. 271 - 275
    Preprints |
    Chandra R; Azam D; Kapoor A; Mulller RD, Surrogate-assisted Bayesian inversion for landscape and basin evolution models, http://dx.doi.org/10.5194/gmd-2018-315
    Preprints |
    Olierook HKH; Scalzo R; Kohn D; Chandra R; Farahbakhsh E; Houseman G; Clark C; Reddy SM; Müller RD, Bayesian geological and geophysical data fusion for the construction and uncertainty quantification of 3D geological models, http://dx.doi.org/10.5194/se-2019-4
    Preprints |
    Scalzo R; Kohn D; Olierook H; Houseman G; Chandra R; Girolami M; Cripps S, Efficiency and robustness in Monte Carlo sampling of 3-D geophysical inversions with Obsidian v0.1.2: Setting up for success, http://dx.doi.org/10.5194/gmd-2018-306

  1. S. Vasan and R. Chandra et al.,“Strengthening COVID-19 animal models and regulatory science using a systems biology approach” (FDABAA-21-00123)," United States Food and Drug Administration (FDA), 2022-2024  ($2,000,000 USD)
  2. S. Vasan and R. Chandra et al., "The sySTEMs initiative: systems biology-augmented, stem cell-derived, multi-tissue panel for rapid screening of approved drugs as potential COVID-19 treatments," NHMRC - Medical Research Future Fund  (MRFF), July 2021- June 2022 ($1,000,000), 
  3. S. Cripps, R. Chandra, et al., "ARC training centre in data analytics for resources and environments (ARC ITTC DARE)," 2020 - 2024: https://darecentre.org.au/ ($4,000,000 from ARC and $6,500,000 in-kind support from industry)
  4. R. Chandra, Sydney Fellowship Awards, DVC Research, University of Sydney,  2017 -2019 
  5. D. Muller, R. Chandra,  et al., Understanding the deep carbon cycle from icehouse to greenhouse climates, Sydney Research Excellence Initiative (SREI), DVC Research, University of Sydney, 2017 - 2018 ($300,000)

  1. Doctoral Completion Award, Victoria University of Wellington (2012)
  2. Sydney Fellowship Award, University of Sydney (2017-2019)
  3. UNSW Science Silverstar Award 2022


Methodology Research

  1. Bayesian deep learning: Markov Chain Monte Carlo (MCMC) methods provide a probabilistic approach for the estimation of the free parameters in a wide range of models. Parallel tempering is an MCMC method that features parallelism with enhanced exploration capabilities. We have developed a Bayesian neural network framework that features parallel tempering MCMC with parallel computing [1]. We have addressed the challenge of applying MCMC methods for deep learning network architectures that features millions of parameters with Bayesian Autoencoders [2] and Bayesian Graph Convolutional Neural Networks [3]. We have also presented a framework that provides a synergy of multi-source transfer learning with Bayesian neural networks powered by MCMC [4]. Our current focus is on their application to other deep learning methods, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-term Memory (LSTM) networks. 
  2. Generative adversarial networks: The major challenge is to develop machine learning models given a low number of training examples. In this area, we use generative adversarial networks (GANs) with machine learning models to generate data in scenarios with space and limited data. Our current focus is on pattern classification problems [5],  but the method can be used for spatiotemporal problems, and also augmented with Bayesian inference for robust uncertainty quantification. 
  3. Surrogate-assisted and Bayesian 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 MCMC 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 [6][7].  The challenge is to have a good estimation by the surrogates when the actual model features hundreds of free parameters.  
  4. Neuroevolution and modular learning algorithms: Neuroevolution features evolutionary algorithms that provide a gradient-free and 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. We have developed novel neural network learning algorithms using neuro-evolution with motivations from transfer learning, multi-task learning and reinforcement learning [8] [9] [10] [11].  The challenge is in problems that have missing information, noise and inconsistencies in the organisation of data.   


Applications

  1. COVID-19 drug repurpose and modelling: We focus on long-COVID19 and vaccine testing using machine learning methods such as Bayesian optimisation and deep learning.  The goal is to repurpose drugs for COVID-19 using human tissue and organoid models in an ongoing project funded by the NHMRC. We use artificial intelligence and machine learning (AIML) techniques to characterise the systems biology responses better in a follow-on project with the US FDA. This brings together deep learning and tissue/organoid models along with experimental design using Bayesian optimisation. The approach can be extended to other diseases and enable AIML-based management of future pandemics.
  2. Cyclone modelling and prediction: The drastic effect of climate change is visible with extreme weather conditions such as tropical storms and cyclones. In this research, we use machine learning methods for forecasting cyclone formation for decades to come given drastic changes in the climate. We use global circulation models with machine learning methods to estimate cyclone categories decades ahead in the future. 
  3. Language models: In this research, we use novel deep learning models to develop language models via social media to understand public behaviours in events such as COVID-19.  We have applied deep learning-enabled language models for COVID-19 sentiment analysis with a case study of the Indian first wave of the pandemic. Currently, we are extending the methodology with topic modelling for comparing the three major waves of COVID-19 along with emerging topics in India. Furthermore, we are also reviewing anti-vaccine tweets during COVID-19 and sentiments related to them as the peak was reached in the first and the second wave around the world. Deep learning-based language models with sentiment analysis have also been used to model US 2020 Presidential elections. 
  4. Artificial intelligence for philosophy of religion: It is well known that artificial intelligence methods have immense success in their applications in areas of science and technology. It is important to uncover the potential of these methods in areas of arts and humanities. The Bhagavad Gita is a Hindu sacred and philosophical text which has been one of the most translated texts over the course of history. We use artificial intelligence methods to analyse the sentiments uncovered with philosophical issues presented in the Bhagavad Gita. We show that artificial intelligence methods powered by deep learning can be used to guide the study of religious and philosophical texts. We show that artificial intelligence can be used for understanding sentiments expressed in ancient philosophical texts. We use novel language models to analyse selected translations of the Bhagavad Gita (from Sanskrit to English) using semantic and sentiment analyses which help in the evaluation of translation quality.
  5. Mineral exploration and remote sensing: The extraction of geological lineaments from digital satellite data is a fundamental application in remote sensing. We use computer vision techniques for extracting geological lineaments using optical remote sensing data. Furthermore, in another research direction, we provide a synergy of deep learning methods with remote sensing for lithological mapping which is a critical aspect of geological mapping that can be useful in studying the mineralization potential of a region. Currently, we are using variational autoencoders and remote sensing for the identification of lineaments along with novel clustering methods. We would like to extend these methods for space exploration projects with the study of the Moon and Mars using satellite sate.
  6. Reef modelling and remote sensing: Geological reef models such as Py-Reef-Core provide 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 estimate and provide uncertainty quantification of free model parameters using Bayesian inference with Py-Reef-Core. This can help us 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.  Currently, we are utilising remote sensing and machine learning method to study reef areas using satellite and drone datasets.
  7. 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 a landscape evolution 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.  
  8. 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 million years 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.   

Seminars

  1. R. Chandra, “Machine learning for paleo-geology and mineral exploration: A spatiotemporal odyssey”, ARC ITTC Data Analytics in Resources and Environments, December 2021. Youtube
  2. R. Chandra, “BERT-based language models for US Elections, COVID-19, and Bhagavad Gita”, UNSW Statistics Seminar Series, December 2021. Youtube
  3. R. Chandra, “Revisiting Bayesian deep learning with advancements in MCMC”, University of Auckland, Department of Statistics, April 2021. Youtube
  4. R. Chandra, “Unravelling Earth’s geological history with geoscientific models powered by artificial intelligence” University of the South Pacific, Public Seminar Series, September 2019. Youtube
  5. R. Chandra, ``Bayesian inference for Geoscientific models'', School of Computer Science, University of Wollongong, February 2019.
  6. R. Chandra, ``Bayesian inference for computationally expensive Earth evolution models'', School of Computer Science, University of Adelaide, October 2018. Youtube
  7. R. Chandra, ``Bayesian inference for modelling geo-coastal, basin and landscape evolution'', Basin Genesis Hub Workshop, The University of Sydney, February 2018.
  8. R. Chandra, `` Tackling climate change problems with machine learning '', EarthByte Group, School of Geosciences, The University of Sydney, July 2017. Youtube
  9. R. Chandra, ``Competitive neuroevolution with applications'', Seminar, School of Computing, Information and Mathematical Sciences, University of South Pacific, August 2015. Youtube
  10. R. Chandra, ``Open source software for education in Fiji'', Seminar, South Pacific Computer Society, University of South Pacific, Suva, Fiji, April 2013.
  11. R. Chandra, ``Chaotic time series prediction using recurrent networks", Seminar, School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand, August, 2011.

Available Research Projects 

  1. Bayesian deep learning for protein function detection (PhD), Co-supervised by Prof. Alok Sharma (RIKEN, Japan)
  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 (University of Sydney)
  6. Machine learning for reef modelling and Optimisation, Co-supervised by Prof. Jody Webster (University of Sydney)
  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. Bayesian deep learning for language models  (Honours/Masters/PhD)
  12. Sentiment analysis with deep learning during natural disasters and extreme events (Honours/Masters/PhD)
  13. Deep learning for monitoring abuse in social media  (Honours/PhD)
  14. Ensemble learning for class imbalanced problems (Honours/PhD)  with Dr Rodney Beard 
  15. Bayesian deep learning for hydrological models (Honours/PhD) with Prof. Lucy Marshall (UNSW) and A/Prof Willem Vervoort (University of Sydney)
  16. COVID-19 related vaccine research  (Honours/PhD with Prof. Seshadri Vasan (CSIRO and University of York)
  17. Knowledge-based Recurrent Neural Networks (Honours/PhD) with Prof. Christian Omlin (University of Agder, Norway)
  18. Deep learning for bio-diversity and ecology (Honours/PhD) with Prof. Glenda Wardle and Dr Aaron Greenville (University of Sydney)

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

 

Master of Data Science: 

  1. ZZSC5836 - Data Mining and Machine Learning (Online), https://studyonline.unsw.edu.au/online-programs/master-data-science
  2. MATH5836 - Data Mining, Trimester 3: https://www.maths.unsw.edu.au/courses/math5836-data-mining  Github repo: https://github.com/rohitash-chandra/dataminingMATH5836

Programming Bootcamp:

  1. Resources - code and exercises: https://github.com/rohitash-chandra/python-bootcamp
  2. Youtube Videos available