Dr. Karl Ezra Pilario

Dr. Karl Ezra Pilario

he/him

Associate Professor at the University of the Philippines, Diliman

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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

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).
Download Paper

talks

teaching

AI 221: Classical Machine Learning

Graduate course, Artificial Intelligence Program, University of the Philippines, Diliman

COURSE DESCRIPTION

Exploratory Data Analysis. Linear Models. Kernel Methods. Neural Networks. Trees. Clustering. Dimensionality Reduction. Feature Engineering. Density Estimation. Ensemble Learning. Gaussian Processes. Bayesian Methods. Hyperparameter Search. AutoML. Explainability.

ChE 197/297: Intro to AI/ML for Chemical Engineers

Graduate and Undergraduate course, Department of Chemical Engineering, University of the Philippines, Diliman

COURSE DESCRIPTION

Mathematical details, code implementations, and chemical engineering applications of basic machine learning and artificial intelligence methods.

ChE 251: Advanced Chemical Process Dynamics and Control

Graduate course, Department of Chemical Engineering, University of the Philippines, Diliman

COURSE DESCRIPTION

Analysis of SISO and MIMO process dynamic behavior. Process modeling, dynamics, and control using the state-space representation. Design and simulation of advanced control systems. Analysis of real-world process data.

COURSE CREDIT

3 units (3 hr/week)

COURSE OBJECTIVES

After completing this course, the student should be able to:

  • Identify the dynamic behavior of multivariate processes in terms of state-space models.
  • Understand the properties of linear dynamical systems and their limitations.
  • Design classical and advanced control systems for multivariate processes.
  • Perform simulation, control system analysis, and model evaluation using software.
  • Familiarize with related techniques such as filtering, system identification, and fault detection.

ChemE 105: Mathematical Methods in Chemical Engineering I

Undergraduate course, Department of Chemical Engineering, University of the Philippines, Diliman

COURSE DESCRIPTION

Introduction to programming as a computational tool; matrix algebra; analytical and numerical solutions of systems of linear equations as applied to chemical engineering.

COURSE CREDIT

3 units (2 hr/week lec, 3 hr/week lab)

COURSE OBJECTIVES

After completing this course, the students should be able to:

  • Apply programming knowledge to solve problems in mathematics and science, with applications in chemical engineering;
  • Implement algorithms using computing programs;
  • Develop computing programs to solve chemical engineering problems;
  • Use linear algebra concepts in understanding systems of equations;
  • Use computational tools to obtain numerical solutions to linear and non-linear equations; and
  • Analyze the fit of models to data.

ChemE 182: Chemical Process Dynamics and Control

Undergraduate course, Department of Chemical Engineering, University of the Philippines, Diliman

COURSE DESCRIPTION

Introduction to process dynamics of simple chemical systems. Time-domain, Laplace-domain, and frequency-domain techniques. Design and simulation of PID controllers for SISO systems.

COURSE CREDIT

3 units (2 hr/week lecture; 3 hr/week lab)

COURSE OBJECTIVES

At the end of this course, the students should be able to:

  • Apply the concepts of material and energy balances, unit operations, chemical reaction engineering and mathematical methods to:
    • derive and simulate dynamic models of chemical processes,
    • solve for and analyze the response of chemical processes to process input changes,
    • derive the overall model of a control system using different feedback controllers;
  • Design and analyze the stability, performance, and robustness of feedback control systems;
  • Use computational tools to design, simulate, and analyze the behavior and stability of processes and control systems; and,
  • Familiarize with common instrumentation such as valves, controllers, and sensors found in the industry, as well as their principles of operation.

DS 397: Advanced Computational Methods in Data Science

Graduate course, College of Science, University of the Philippines, Diliman

COURSE DESCRIPTION

An algorithmic and computational perspective on machine learning and data science methods.

COURSE CREDIT

3 units (3 hr/week)

COURSE OBJECTIVES

After completing this course, the student should be able to:

  • Implement common supervised and unsupervised learning algorithms in code via computational executable notebooks.
  • Understand learning algorithms through existing code implementations.
  • Solve issues arising from numerical implementations of learning methods.
  • Evaluate the performance of popular learning algorithms in terms of time and space complexities.
  • Select the right algorithms for data mining and learning in different cases depending on the available time and computational resouces.
  • Design new learning algorithms incorporating readable code implementation, good programming practice, sufficient testing, maintenance, and version control.

COURSE MATERIALS

Please visit this github repo.

ES 204: Numerical Methods in Engineering

Graduate course, College of Engineering, University of the Philippines, Diliman

COURSE DESCRIPTION

Roots of single equations. Systems of linear and nonlinear equations. Ordinary differential equations. Partial differential equations. Applications.

COURSE CREDIT

3 units (3 hr/week)

COURSE OBJECTIVES

After successfully completing this course, the student should be able to

  • Understand numerical approaches in solving engineering problems;
  • Implement numerical methods by writing algorithms using a computing environment;
  • Acquire computational skills in solving linear and nonlinear algebraic equations;
  • Develop mathematical insights and computational skills in solving ordinary and partial differential equations.