CV

Research Collaborator

Nippon Telegraph and Telephone (NTT) R&D

• Developed and authored a full research paper on ‘MetaCaDI,’ a novel meta-learning framework for few-shot differentiable Bayesian causal discovery with unknown interventions (Submitted to UAI 2026).

Research Collaborator

Mobility Fundamentals Mathematics Research by Kyoto University & Toyota Motor

• Authored and presented the full research paper, ‘A Compression-Based Dependence Measure for Causal Discovery by Additive Noise Models,’ at the 2024 International Conference on Neural Information Processing (ICONIP), where it was published in the conference proceedings.
• Authored a full research paper, ‘Causal discovery in Additive Noise Models using beam search,’ published in the journal Artificial Life and Robotics, which details a tunable algorithm that outperforms standard greedy methods by mitigating the effects of statistical noise in finite sample regimes.

Senior Data Scientist

Bank of the Philippine Islands (BPI)

• Developed an investment propensity model that enhanced campaign targeting, resulting in a notable 22% increase in assets under management (AUM) invested.
• Designed a customer lifetime value (CLV) metric that captured both current and potential customer value.
• Engineered a recommender system that uses customer profiles, life stage, and financial needs to recommend tailored investment funds.

Research Intern

Nara Institute of Science and Technology

• Investigated novel enhancements to the LiNGAM-SPP framework, focusing on pairwise likelihood ratios to identify causal structures in the presence of unknown confounders.
• Presented initial findings at the internship’s conclusion, which formed the foundation for the full research paper ‘Redefining the Shortest Path Problem Formulation of the Linear Non-Gaussian Acyclic Model: Pairwise Likelihood Ratios, Prior Knowledge, and Path Enumeration,’ submitted to IEICE (under review).

Data Scientist

UnitedHealth Group

• Led project management for an R Shiny dashboard that streamlined power analysis with report and source code generation, which improved communication with stakeholders and reduced the time needed for analysis by up to 80%.
• Spearheaded the creation of several reusable data analysis tools, including automated feature engineering, model fitting, and visualization, that significantly reduced analysis time from weeks to minutes. These tools saved our stakeholders 75 person months per month.
• Developed a SQL generator package in Python that dramatically increased efficiency and reduced errors in data pulling. One person can now accomplish in a few minutes what used to take three people two weeks. Provided training and mentorship to teammates on this tool.
• Designed and directed the development of a report generation package in Python, enabling easy creation of standardized HTML reports with dynamic text and interactive plots, which improved collaboration and communication with stakeholders.

Data Science Apprentice

UnitedHealth Group (BPI-UHG R&D Data Science Program)

• Completed a highly selective 6-month intensive data science training program, BPI-UHG R&D Data Science (BUDS), that provided both theoretical foundations and practical applications of data science in healthcare and finance.
• Mastered the theoretical foundations of data science, including multivariable calculus, linear algebra, probability and statistics, optimization, signal processing, time series analysis, and programming in Python, R, Tableau, and Git/Github.
• Worked on weekly hackathon-style team competitions with real-life healthcare and finance data, honing skills in data wrangling, feature engineering, statistical modeling, and machine learning.
• Applied learned concepts to complete four in-depth case studies in healthcare and finance, demonstrating expertise in data analysis, problem-solving, and communication.

Research Intern

Manila Observatory - Remote Sensing Group

• Authored the undergraduate thesis, ‘Using Mahalanobis Distance to Classify Aerosol in Southeast Asia based on AERONET-Retrieved Optical Properties.’
• Presented original research at two major international conferences: American Geophysical Union (AGU) 2016 and Japan Geoscience Union (JpGU) 2018.
• Analyzed long-term AERONET sunphotometer data and satellite imagery (MODIS/MISR) to classify aerosol optical properties.
• Managed the remote sensing data pipeline, including data validation, maintenance, and regular data uploads to NASA’s servers.
• Contributed to a secondary cloud type detection experiment by gathering, cleaning, and preparing digital image data.

Doctoral degree

Nara Institute of Science and Technology

Mathematical Informatics

• Monbukagakusho (MEXT) Scholar

Masters in Engineering (Partial)

University of the Philippines Diliman

Artificial Intelligence

• Completed coursework towards Master’s degree

Bachelor's degree (Cum Laude)

Ateneo de Manila University

Physics

• Minor Degree, Data Science and Analytics
• Activities and societies: Ateneo League of Physicists

High School Diploma (Batch Salutatorian)

Chiang Kai Shek College

Monbukagakusho (MEXT) Scholarship

Japanese Government (Ministry of Education, Culture, Sports, Science and Technology)

Prestigious scholarship awarded by the Japanese Government for postgraduate studies at the Nara Institute of Science and Technology. (2023-2026)

Director's List Scholarship

Ateneo de Manila University

Recognized for academic excellence. Awarded to top 200 applicants.