Complex Systems are systems composed of non-homogenous agents that are highly interacting and whose interactions result to emergent …

Data mining is the study of collecting, cleaning, processing, analyzing, and gaining useful insights from data. Its task is to examine …

We found that MLM membership growths decelerates after reaching a size threshold, contrary to claims of unbridled growth. The network …


Philippine News Agency

Introducing data science as ‘future’ of policy making in PH

Cosmo Philippines

This Pinay Is Leading Data Science Education In The Philippines

Rappler Philippines

AIM’s big data bid to regain business school leadership

Entrepreneur PH

Filipina Physicist Back from SG to Head AIM’s Data Science Program


Machine-learning program predicts public transport use in Singapore


Industry Projects

A list of all industry projects I’ve been involved in

Manila WiMLDS

To support and promote women and gender minoritie in ML and DS

Databeers Manila

Event that brings together experts to share short data stories with the public over free beers.

Recent Publications

We develop a numerical model using both artificial and empirical inputs to analyze taxi dynamics in an urban setting. More …

The Philippines is among the most exposed countries to natural hazards. Little is known about impacts of different natural hazards on …

Rapid transit systems form a crucial transport mode in modern cities today due to their ability to sustain high passenger volume …

Understanding the morphology of an urban system is an important step towards unveiling the dynamical processes of its growth and …

A sufficient knowledge of the demographics of a commuting public is essential in formulating and implementing more targeted …

Recent & Upcoming Talks

The main theme is ‘Promoting Integrity, Advancing Excellence’ to update delegates and keep them abreast with current …

On the 6th and 7th of April 2019, the UP DOST SA will be catering 700 DOST Scholars across the country for the National DOST …

To promote women empowerment

This event is in celebration of the International Women’s Month organized by Ayala Malls Feliz. This is in partnership with the …

How can Data Science revolutionize the logistics and supply chain sector? In this masterclass we cover the basics of data science and …


This course introduces participants to the latest trends in analytics in the era of big data, artificial intelligence, and the Internet of Things. The course explores various data-driven approaches, frameworks, and models used by different industries across functions to improve processes and/or create new and innovative products. In particular, participants will familiarize themselves with the different levels of analytics—descriptive, predictive, and prescriptive, and will be tasked to identify use cases where the approaches can be applied.

Complex Systems are systems composed of heterogeneous agents that are highly interacting and whose interactions result to emergent behavior, e.g. societies, economies, markets, cities, and biological systems like the immune system and the brain, to list a few. In this class, the students will be exposed to various tools used in characterizing and modeling complex systems. The topics include dynamical systems, chaos, fractals, self-organization, cellular-automata modeling, agent-based modeling, and complex networks.

The module covers the basics of Complexity Science with particular focus on Complex Networks (network science), which are the backbones of complex systems (e.g. cities, organizations, economies, and financial markets). Complex networks quantify the interactions of various entities/players in complex systems. Examples of complex networks include social networks like those generated from Twitter, Facebook, and Instagram, financial networks, biological networks, and organizational networks. Students learn how to visualize, analyze, and model complex networks using Python, NetworkX, and Gephi. At the end of the course, students should be able to view and analyze problems in business and marketing, among others, through the lens of complexity science. They should also be able to argue, in descriptive and quantitative manner, why a system-of-systems thinking is necessary to address most real-world issues.

In this course, students learn data science fundamentals that are more in tune with their applications to business; essentially, how the field is applied in the real-world. Students are provided with a comprehensive overview of data science and artificial intelligence—what they are and what they’re not. Students are also exposed to the current state of data science and its future direction(s). The class has data science practitioners share their experiences—from how companies come up with a data strategy toward becoming a truly data-driven organization, to building data science teams, to learning about the challenges companies faced and are currently facing. Participants learn about data workflows and pipelines; they will learn and appreciate how to assemble and lead data science enterprises. Finally, the course also covers the fundamentals of data privacy and data/AI ethics.

In this course, students will learn to appreciate the importance of successful data visualizations and intelligible data-driven stories in creating actionable insights. Using real-world datasets, learners will assimilate the necessary skills to fashion effective visualizations that exhibit not only good design elements but also layers of information that when weaved together as a narrative can generate actionable insights and new levels of understanding. The course will introduce learners to some business intelligence and analytics tools/software including Tableau (Public), QGIS, and Gephi (a network visualization tool). They will also learn how to create visualizations in Python using pandas, seaborn, networkx, and matplotlib.