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Dr Arian Prabowo

Dr Arian Prabowo

Postdoctoral Fellow
Engineering
Computer Science and Engineering

Arian is a postdoctoral researcher in machine learning, School of Computer Science and Engineering, University of New South Wales. He did his PhD in Computer Science in the at RMIT University Under the supervision of Professor Flora Salim from UNSW,  Dr. Piotr Koniusz from Data61/CSIRO, and Dr. Wei Shao. His PhD was on spatiotemporal deep learning where he used deep learning methods such as graph neural networks (GNN), self-supervised learning (SSL), and contrastive learning to various applications such as map inference and traffic forecasting. After his PhD, he works on building-level, electricity data.

Arian's research interests include spatiotemporal data, geometric deep learning, physics informed nerual networks (PINN), and greybox models. More information about his research such as papers, posters, talks, figures, and codes are available from his website www.arianprabowo.com.
E-mail
arian.prabowo@unsw.edu.au
  • Books | 2023
    Prabowo A; Chen K; Xue H; Sethuvenkatraman S; Salim FD, 2023, Correction to: Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track (LNAI 14175 (10.1007/978-3-031-43430-3_1)), http://dx.doi.org/10.1007/978-3-031-43430-3_34
  • Book Chapters | 2023
    Prabowo A; Chen K; Xue H; Sethuvenkatraman S; Salim FD, 2023, 'Continually Learning Out-of-Distribution Spatiotemporal Data for Robust Energy Forecasting', in , pp. 3 - 19, http://dx.doi.org/10.1007/978-3-031-43430-3_1
    Book Chapters | 2023
    Prabowo A; Chen K; Xue H; Sethuvenkatraman S; Salim FD, 2023, 'Correction to: Continually Learning Out-of-Distribution Spatiotemporal Data for Robust Energy Forecasting', in Lecture Notes in Computer Science, Springer Nature Switzerland, pp. C1 - C2, http://dx.doi.org/10.1007/978-3-031-43430-3_34
  • Journal articles | 2024
    Prabowo A; Xue H; Shao W; Koniusz P; Salim FD, 2024, 'Traffic forecasting on new roads using spatial contrastive pre-training (SCPT)', Data Mining and Knowledge Discovery, 38, pp. 913 - 937, http://dx.doi.org/10.1007/s10618-023-00982-0
    Journal articles | 2022
    Shao W; Prabowo A; Zhao S; Koniusz P; Salim FD, 2022, 'Predicting flight delay with spatio-temporal trajectory convolutional network and airport situational awareness map', Neurocomputing, 472, pp. 280 - 293, http://dx.doi.org/10.1016/j.neucom.2021.04.136
  • Preprints | 2024
    Prabowo A; Lin X; Razzak I; Xue H; Yap EW; Amos M; Salim FD, 2024, BTS: Building Timeseries Dataset: Empowering Large-Scale Building Analytics, , http://arxiv.org/abs/2406.08990v2
    Conference Papers | 2023
    Prabowo A; Chen K; Xue H; Sethuvenkatraman S; Salim FD, 2023, 'Navigating Out-of-Distribution Electricity Load Forecasting during COVID-19: Benchmarking energy load forecasting models without and with continual learning', in BuildSys 2023 - Proceedings of the10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, pp. 41 - 50, http://dx.doi.org/10.1145/3600100.3623726
    Preprints | 2023
    Prabowo A; Chen K; Xue H; Sethuvenkatraman S; Salim FD, 2023, Continually learning out-of-distribution spatiotemporal data for robust energy forecasting, , http://dx.doi.org/10.1007/978-3-031-43430-3_1
    Preprints | 2023
    Prabowo A; Chen K; Xue H; Sethuvenkatraman S; Salim FD, 2023, Navigating Out-of-Distribution Electricity Load Forecasting during COVID-19: Benchmarking energy load forecasting models without and with continual learning, , http://dx.doi.org/10.1145/3600100.3623726
    Conference Papers | 2023
    Prabowo A; Shao W; Xue H; Koniusz P; Salim FD, 2023, 'Because Every Sensor Is Unique, so Is Every Pair: Handling Dynamicity in Traffic Forecasting', in ACM International Conference Proceeding Series, pp. 93 - 104, http://dx.doi.org/10.1145/3576842.3582362
    Preprints | 2023
    Prabowo A; Shao W; Xue H; Koniusz P; Salim FD, 2023, Because Every Sensor Is Unique, so Is Every Pair: Handling Dynamicity in Traffic Forecasting, , http://dx.doi.org/10.1145/3576842.3582362
    Preprints | 2023
    Prabowo A; Xue H; Shao W; Koniusz P; Salim FD, 2023, Message Passing Neural Networks for Traffic Forecasting, , http://arxiv.org/abs/2305.05740v1
    Preprints | 2023
    Prabowo A; Xue H; Shao W; Koniusz P; Salim FD, 2023, Traffic Forecasting on New Roads Using Spatial Contrastive Pre-Training (SCPT), , http://dx.doi.org/10.1007/s10618-023-00982-0
    Preprints | 2021
    Shao W; Prabowo A; Zhao S; Koniusz P; Salim FD, 2021, Predicting Flight Delay with Spatio-Temporal Trajectory Convolutional Network and Airport Situational Awareness Map, , http://dx.doi.org/10.1016/j.neucom.2021.04.136
    Preprints | 2020
    Gao N; Xue H; Shao W; Zhao S; Qin KK; Prabowo A; Rahaman MS; Salim FD, 2020, Generative Adversarial Networks for Spatio-temporal Data: A Survey, , http://dx.doi.org/10.1145/3474838
    Preprints | 2019
    Prabowo A; Koniusz P; Shao W; Salim FD, 2019, COLTRANE: ConvolutiOnaL TRAjectory NEtwork for Deep Map Inference, , http://dx.doi.org/10.1145/3360322.3360853
    Preprints | 2019
    Shao W; Prabowo A; Zhao S; Tan S; Konuiusz P; Chan J; Hei X; Feest B; Salim FD, 2019, Flight Delay Prediction using Airport Situational Awareness Map, , http://arxiv.org/abs/1911.01605v1