About

I am a Ph.D. candidate specializing in Causal AI, with a focus on developing novel algorithms for causal discovery and causal representation learning. My current research leverages techniques like representation learning and meta-learning to uncover and model causal structures within data.

My future research interests lie in:

• Pushing the boundaries of causal representation learning
• Exploring controlled generation with causal-awareness for counterfactual and out-of-distribution scenarios
• Advancing neuro-symbolic AI where the symbolic component is explicitly causal

I bring a unique blend of cutting-edge academic research and practical, real-world experience deploying machine learning models that deliver business value. I am passionate about bridging the gap between theoretical research and practical applications to build more robust, fair, and transparent AI systems.

🔬 Research & Professional Experience

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

• Developing 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 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 patient data to quantify diabetic readmission risk and detect changes in depression severity using health tracker data
• 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 measurements
• 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)