Projects

We propose a deep learning strategy to compute the mean curvature of an implicit level-set representation of an interface. Our approach is based on fitting neural networks to synthetic datasets of pairs of nodal phi values and curvatures obtained from circular interfaces immersed in different uniform resolutions. These neural networks are multilayer perceptrons that ingest sample level-set values of grid points along a free boundary and output the dimensionless curvature at the center vertices of each sampled neighborhood. Evaluations with irregular (smooth and sharp) interfaces, in both uniform and adaptive meshes, show that our deep learning approach is systematically superior to conventional numerical approximation in the L2 and L-Infinity norms. Our methodology is also less sensitive to steep curvatures and approximates them well with samples collected with fewer iterations of the reinitialization equation, often needed to regularize the underlying implicit function. Additionally, we show that an application-dependent map of local resolutions to neural networks can be constructed and employed to estimate interface curvatures more efficiently than using typically expensive numerical schemes while still attaining comparable or higher precision.

Submitted to SIAM Journal on Scientific Computing.

In this work, we provide a solution to the disambiguation task by leveraging the traditional techniques of candidate mapping entity generation and local evaluation with some of the latest developments, such as word embeddings. We also consider a graph-based collective process to establish a topical relatedness metric that helps true mapping entities in a document to disambiguate one another through personalized PageRank. The final mapping entities for the given surface forms are obtained by heuristically reincorporating the candidates' local features with their resulting graph score and performing a maximal discriminant selection. The proposed methodology is capable of reaching up to 80% accuracy when it is evaluated against a well known dataset with around 18,000 named entity mentions.

We have implemented Reflective Shadow Maps (RSM), together with Percentage-Closer Soft Shadows (PCSS) and Screen-Space Ambient Occlusion (SSAO) for added realism and details. Our approach works mostly with blurred textures and diffuse 3D objects shaded with the Blinn-Phong Reflectance Model. We have also resorted to Deferred Rendering to achieve interactive rates when RSM and SSAO are enabled, which are, by definition, very expensive tasks in terms of GPU resources. Finally, with regards to random sampling, we are using Poisson Disks which provide a good even distribution of 2D points without the artifacts that usually appear with uniformly distributed numbers in both PCSS and the importance driven sampling in RSM's indirect lighting.

This OpenGL 4.1 project creates a GLFW window and renders on it a scene with geometries and a 3D object model. We currently support Precomputed Radiance Transfer on shadowed diffuse objects.

This OpenGL 4.1 project creates a GLFW window and renders on it a scene with geometric and textured 3D object models. The scene also has 3 colored area lights that make objects cast shadows using the Percentage Closer Soft Shadows procedure. We further support the Blinn-Phong Reflectance Model, and render text using FreeType and textures.

WebGL implementation using JSPs. This template uses the numeric.js library and other extensions to support drawing cubes, spheres, cylinders, prisms, dots, and paths. It also implements Phong shading for 3D bodies. Check the PHP template version now!

A physics-based simulation of a biomechanical model of an Araneous Diadematus specimen. The spider is able to walk by using ODE for dynamics emulation and OpenGL for rendering.

A physics-based simulation of a biomechanical model of a salamander, capable of walking and swimming. The system uses ODE for dynamics computation and OpenGL for rendering.

System that yields the correct mapping for mentioned entities in a list by using optimization (simulated annealing). Our application utilizes Wikipedia (2012) as knowledge base to define the metrics and semantics used in the mapping process.

2D physics-based simulation of snow by using the material point method. We emulate the mechanics, viscosity, and composition of snow as the latter is affected by external and internal forces. The system is developed in C++ and OpenGL.

MATLAB application that uses Singular Value Decomposition and other image-processing machinery to classify male and female faces. Further statistical analysis allows to generate random faces given the intensity and geometry features extracted from the training set.

Our symmetry-seeking model allows construction of 3D objects from 2D input images, using a deformable tube made up of particles interconnected by damped-springs. This project is submitted as part of the CS269 Visual Modeling course at UCLA.

Simulation of an artificial ecosystem where virtual creatures learn to survive by eating food and avoiding poison, and to reproduce in order to maintain the continuity of their species. The agents emulate natural phenomena such as nuptial feeding and male brooding by resorting to a neural-based reinforcement learning.

Web application that implements an auction website using Java, MySQL, Lucene, Apache Tomcat, Apache Axis2, JavaScript, and AJAX.

Crowd simulation where agent behavior resembles fluid motion. The displacement of agents depends on crowd densities at different locations in a discrete environment that represents a potential/velocity field.

Simulation of an artificial ecosystem where animal-like creatures learn to survive from two evolutionary perspectives: Darwinism and Lamarckism. Agents have egocentric maps that allow them to acquire and share knowledge about the environment they live in.

A simple spring mass, damped system simulating spheres bouncing off the ground.

A basic computer animation using (fixed-pipeline) OpenGL with textures and C++. This is project 2 of CS 164A: Introduction to Computer Graphics.

Development of a neural network that prognosticates a volcanic eruption based on the input data and the historic information that was used as training set. This project fulfills the thesis requirement to acquire the degree of Bachelor's in Engineering.

Stock-management desktop application to control sales, purchases, and product reservations in a drugstore. Our system is written in Delphi and developed with the highest standards in software engineering.