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.

About

Hello! I’m a Ph.D. candidate specializing in Causal AI, building machine learning systems that can reason about cause and effect to be more robust, fair, and generalizable. My goal is to bridge the gap between foundational research and real-world application, and I am currently seeking Research Scientist or Applied Scientist roles.

My Ph.D. research, supported by Japan’s prestigious MEXT Scholarship, focuses on causal discovery. In a key project with NTT R&D, I developed a novel Bayesian meta-learning framework that uncovers causal relationships from extremely limited data—a critical capability for high-stakes domains like drug discovery or personalized marketing where data is scarce.

My next research area is causal representation learning, where I aim to build models that learn causally-sound concepts from unstructured data (images, text). This is essential for the next generation of Generative AI, enabling capabilities like controlled content generation, reliable counterfactual reasoning, and robust performance in out-of-distribution scenarios.

Before my Ph.D., I was an industry data scientist deploying high-impact models in finance and healthcare. At the Bank of the Philippine Islands, I built production recommender systems and investment propensity models. At OptumLabs (UnitedHealth Group R&D), I developed models to assess patient health risks. My background in Physics grounds my work in a first-principles approach, ensuring the solutions I build are both theoretically sound and practically valuable.

🔬 Research & Professional Experience

Research Collaborator, Aug 2024 – Present
Nippon Telegraph and Telephone (NTT) R&D, Kyoto, Japan

  • Developed MetaCaDI, a novel meta-learning framework for few-shot differentiable Bayesian causal discovery with unknown interventions
  • Authored a full research paper prepared for submission to AAAI 2026

Research Collaborator, Oct 2023 – Present
Mobility Fundamentals Mathematics Research by Kyoto University & Toyota, Kyoto, Japan

  • Conducted foundational research in causality, focusing on novel methods for causal discovery
  • Presented and published key findings on a compression-based causal discovery method at ICONIP 2024

Senior Data Scientist, May 2022 – Sep 2023
Bank of the Philippine Islands, Manila, Philippines

  • Developed and deployed an investment propensity model that enhanced campaign targeting, resulting in a 22% increase in assets under management (AUM)
  • Engineered a production-level recommender system using customer profiles and financial needs
  • Designed a customer lifetime value (CLV) metric to shift strategic focus toward long-term customer value

Data Scientist, AI Engineering, Jul 2019 – Apr 2022
Optum Labs (UnitedHealth Group R&D), Minneapolis, US (Remote)

  • Spearheaded research analyzing diverse patient data, such as claims data and fitness tracker data, to quantify risks and identify key predictive factors for various health conditions, including diabetic readmissions, undiagnosed obesity, neonatal complications, and changes in depression severity.
  • Led the development of internal Python packages and R Shiny dashboards that automated data analysis and model fitting
  • Reduced analysis time by up to 80%, saving significant person-months

Undergraduate Researcher, May 2016 – Mar 2019
Manila Observatory Remote Sensing Group

  • Characterized aerosols using AERONET sun photometer measurement data
  • Validated MISR/MODIS satellite data for local atmospheric studies

🎓 Education

D.Sc. in Information Science (Expected 2026)
Nara Institute of Science and Technology (NAIST), Nara, Japan

M.Eng. in Artificial Intelligence (Partial Completion)
University of the Philippines, Diliman

B.S. in Physics, Cum Laude (Minor in Data Science and Analytics), 2019
Ateneo de Manila University, Quezon City, Philippines

🛠 Technical Skills

Causal AI & Statistics
Causal Discovery, Structural Causal Models, Causal Inference, Representation Learning, Meta-Learning, Bayesian Networks, Instrumental Variables, Propensity Score Matching

Programming & Data Science
Python (PyTorch, Scikit-learn, Pandas), R, SQL, C/C++

Tools & Platforms
Git/GitHub, Plotly & Dash, Weights & Biases (WandB), Optuna, LaTeX, AWS, GCP

🌐 Languages

  • English: Native/Bilingual Proficiency (TOEIC-IP: 965)
  • Filipino: Native/Bilingual Proficiency
  • Japanese: Limited Working Proficiency
  • Mandarin Chinese: Limited Working Proficiency

🏅 Awards & Certifications

  • Monbukagakusho (MEXT) Scholarship, Japanese Government (2023–2026)
  • Director’s List Scholarship, Ateneo de Manila University (2014–2019)
  • Student Travel Grant, American Geophysical Union (2016)
  • Certified Spark NLP Data Scientist, John Snow Labs (Jan 2022)