Hans Jarett Ong

Hans Jarett Ong

PhD candidate at NAIST specializing in Causal AI. My research connects theory with practical applications to build robust and fair AI systems, drawing on my experience deploying ML models.

Posts by Collection

portfolio

publications

Determination of Monthly Aerosol Types in Manila Observatory and Notre Dame of Marbel University from Aerosol Robotic Network (AERONET) measurements

Published in AGU Fall Meeting, San Francisco, California, USA, 2016

This presentation identifies aerosol types in Manila Observatory and Notre Dame of Marbel University using AERONET data.

Recommended citation: Ong, Hans Jarett J., et al. (2016). "Determination of Monthly Aerosol Types in Manila Observatory and Notre Dame of Marbel University from Aerosol Robotic Network (AERONET) measurements." Oral Presentation, AGU Fall Meeting.

Aerosol Types from 25 Southeast Asian AERONET Sites Obtained Using Specified Clustering and Mahalanobis Distance

Published in Japan Geoscience Union, Chiba, Japan, 2018

This presentation discusses the identification of aerosol types over 25 Southeast Asian sites using AERONET data.

Recommended citation: Ong, Hans Jarett J., et al. (2018). "Aerosol Types from 25 Southeast Asian AERONET Sites Obtained Using Specified Clustering and Mahalanobis Distance." Oral Presentation, Japan Geoscience Union.

Using Mahalanobis Distance to Classify Aerosol in Southeast Asia based on AERONET-Retrieved Optical Properties

Published in Ateneo de Manila University, 2019

This undergraduate thesis classifies aerosol types over Southeast Asia using Mahalanobis distance on AERONET-retrieved optical properties.

Recommended citation: Ong, Hans Jarett J. (2019). "Using Mahalanobis Distance to Classify Aerosol in Southeast Asia based on AERONET-Retrieved Optical Properties." Bachelors Thesis, Ateneo de Manila University.
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Dynamic Principal Component Analysis for the Construction of High-Frequency Economic Indicators

Published in International Conference on Advances in Computational Science and Engineering, 2023

This study investigates the application of dynamic principal component analysis in offering real-time insights into various facets of an economy.

Recommended citation: Lim, Brian Godwin, Ong, Hans Jarett, Tan, Renzo Roel, and Ikeda, Kazushi. (2023). "Dynamic Principal Component Analysis for the Construction of High-Frequency Economic Indicators." International Conference on Advances in Computational Science and Engineering. pp. 645-663.

Paper Title Number 4

Published in GitHub Journal of Bugs, 2024

This paper is about fixing template issue #693.

Recommended citation: Your Name, You. (2024). "Paper Title Number 3." GitHub Journal of Bugs. 1(3).
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Redefining the Shortest Path Problem Formulation of the Linear Non-Gaussian Acyclic Model: Pairwise Likelihood Ratios, Prior Knowledge, and Path Enumeration

Published in arXiv preprint arXiv:2404.11922, 2024

This paper proposes a threefold enhancement to the LiNGAM-SPP framework for causal discovery.

Recommended citation: Ong, Hans Jarett J., and Lim, Brian Godwin S. (2024). "Redefining the Shortest Path Problem Formulation of the Linear Non-Gaussian Acyclic Model: Pairwise Likelihood Ratios, Prior Knowledge, and Path Enumeration." arXiv preprint arXiv:2404.11922.
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A Compression-Based Dependence Measure for Causal Discovery by Additive Noise Models

Published in International Conference on Neural Information Processing, 2025

This paper introduces a compression-based dependence measure (CDM) for causal discovery, demonstrating its performance across multiple causal discovery models and benchmarks.

Recommended citation: Ong, Hans Jarett J., Lim, Brian Godwin S., Tiu, Benedict Ryan C., Tan, Renzo Roel P., and Ikeda, Kazushi. (2025). "A Compression-Based Dependence Measure for Causal Discovery by Additive Noise Models." International Conference on Neural Information Processing, pp. 61–75. Springer, Singapore.

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

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