Ratneel Deo

PhD Candidate, School of Geoscience, The University of Sydney
ratneel.deo@sydney.edu.au

Ratneel is a DARE PhD candidate studying geophysical modeling at the University of Sydney with a research interest in creating a synergy of optimization and inference methods. Prior to that, he completed his MSc in 2018 at the University of the South Pacific, majoring in Computing Science. In his postgraduate degree, he worked on gradient-based and coevolutionary neural learning to assist in writing his master’s degree research thesis. In his short career, he has worked mainly with time series forecasting problems with an application to cyclone intensity and path prediction, with a recent interest in working with large-scale climatic modelling methods.

Journal Publications

R. Chandra, D. Muller, R. Deo, D.Azam, N. Buttersworth, T. Salles, and S. Cripps, ”Multicore parallel tempering Bayeslands for basin and landscape evolution”, Geochemistry, Geophysics, Geosystems, vol. 20 – 11, pp. 5082 – 5104 (2019) (Journal Ranking: Q1 in SJR for Earth and Planetary Sciences, No. 10 in Google Scholar Metrics for Geophysics)

R. Chandra, K. Jain, R. V. Deo, S. Cripps, ” Langevin-gradient parallel tempering for Bayesian neural learning”, Neurocomputing, vol. 359, pp. 315 – 326 (2019) (FOR Code: 080108 (50%), 080109 (50% )) (Journal Ranking: No. 10 in Google Scholar Metrics for AI, Q1 in SJR for AI)

Conference Publications

R. Deo, R. Chandra. ”Multi-step-ahead cyclone intensity prediction with Bayesian neural networks”. In: Nayak A., Sharma A. (eds) PRICAI 2019: Trends in Artificial Intelligence. PRICAI 2019. Lecture Notes in Computer Science, vol 11671 Springer, Cham. (CORE Rank B)

R. Deo and R. Chandra, ”Identification of Minimal Timespan Problem for Recurrent Neural Networks with Application to Cyclone Wind-Intensity Prediction”, Proceedings of the IEEE Joint Conference on Neural networks, July 2016, Vancouver, IJCNN 2016: 489-496 (FOR Code: 080108 (50%), 080109 (50%) CORE Rank A)

R. Chandra, R. Deo and C. Omlin, ”An Architecture for Encoding Two-Dimensional Cyclone Track Prediction Problem in Coevolutionary Recurrent Neural Networks”, Proceedings of the IEEE Joint Conference on Neural networks, July 2016, Vancouver, IJCNN 2016: 4865-4872 (FOR Code: 080108 (50 %), 080109 (50%) CORE Rank A)

R. Chandra, R. Deo, K. Bali and A. Sharma, ”On the Relationship of Degree of Separability with Depth of Evolution in Subcomponents of Cooperative Coevolution”, Proceedings of the IEEE Congress on Evolutionary Computation, July 2016, Vancouver, CEC 2016: 4823-4830 (FOR Code: 080108 (50%), 080109 (50%) CORE Rank B)

Masters Thesis

R. Deo, ”Neural network methodologies for cyclone wind intensity and path prediction”, M.S. Thesis, University of the South Pacific, 2018.

Technical Report (Non-Refereed)

R. Deo, R Chandra, A. Sharma. ”Stacked transfer learning for tropical cyclone intensity prediction”. In ArXiv e-prints 1708.06539, August 2017.

Workshops and Seminars