| 21-11-2023 |
Inversión del problema de pronóstico de tiempo de descarga de baterías con Deep Reinforcement Learning
El tiempo en que ocurre la descarga de una batería en el futuro depende de cuál es la energía actual almacenada y cómo se dispondrá de ella posteriormente, es decir, de cómo se descargará la batería en el futuro. En esta investigación se busca invertir este problema. La idea principal es definir el tiempo futuro en que se desea se descargue una batería, y con esa información construir una función que defina cómo ha de descargarse la batería para conseguir tal propósito. Esta función corresponde a una red neuronal profunda, la cual es entrenada mediante aprendizaje reforzado. Este trabajo contempla reuniones semanales para reporte de avances y discusión. Ya se cuenta con un código base en Python, y la idea principal es afinar resultados y explorar mejoras.
Prerequisitos:
no tiene.
Tiene un método de evaluación Nota 1-7, con 10 créditos y tiene 1/2 vacantes disponibles |
Mentor(es): Ver en la plataforma |
| 21-07-2022 |
Ingeniería e Inteligencia Artificial para la Salud - iHEALTH
La atención médica y, particularmente las imágenes médicas, están bajo una gran presión. Lo anterior se debe a diversas razones como por ejemplo que sus costos operacionales son elevados, se requiere de personal altamente capacitado que es escaso, no son completamente eficientes en la entrega de diagnósticos de patologías en etapas tempranas y el acceso a estas tecnologías es limitado. El Instituto Milenio iHEALTH tiene como misión desarrollar métodos innovadores que integren imágenes médicas, ingeniería e inteligencia artificial para mejorar las atenciones médicas basadas en imágenes con el objetivo, a largo plazo, de que ésta sea una alternativa más asequible y accesible a todos los chilenos. En este contexto existen diferentes oportunidades de investigación de pregrado interdisciplinaria, una gran parte de ellas remuneradas, con los profesores de iHEALTH, cuya lista y areas de investigación se encuentran disponible: https://i-health.cl/es/researchers-and-staff/.
Keywords:
machine learning
deep learning
resonancia magnética
Imágenes Médicas
imágenes cardiacas
Problemas Inversos
Prerequisitos:
no tiene.
Tiene un método de evaluación Nota 1-7, con 10 créditos y tiene 8/10 vacantes disponibles |
Mentor(es): Ver en la plataforma |
| 21-11-2023 |
Prerequisites:
None.
Evaluation method: Nota 1-7, with 1/2 available vacants |
Mentor(s): Open in the plataform |
| 26-12-2022 |
Prerequisites:
IMT2113
Evaluation method: Nota 1-7, with 0/1 available vacants |
Mentor(s): Open in the plataform |
| 21-07-2022 |
Intelligent Healthcare Engineering- iHEALTH
Healthcare, and medical imaging in particular, is under big pressure due to rising costs, shortage of highly trained staff, inefficient early diagnosis, and limited access. The mission of the Millenium Institute for Intelligent Healthcare Engineering – iHEALTH- is to develop innovative methods that integrate medical imaging physics, engineering, and artificial intelligence to improve medical imaging-based healthcare by making it more accurate, efficient, and effective, and in the long term, more affordable and accessible to all Chileans. In this context there are different interdisciplinary undergraduate research opportunities, most of them paid, with iHEALTH professors, whose list and research areas are available: https://i-health.cl/es/researchers-and-staff/.
Keywords:
machine learning
deep learning
resonancia magnética
Imágenes Médicas
imágenes cardiacas
Problemas Inversos
Prerequisites:
None.
Evaluation method: Nota 1-7, with 8/10 available vacants |
Mentor(s): Open in the plataform |
| 17-06-2022 |
Inverse problems and RKHS
There is a class of inverse problems with the property of being separable, that is, they are linear in some variables and non-linear in others. A problem in this class can be reformulated as a linear problem in a space of measures on a suitable set. Recently, Bernstein et al analyzed the solvability of this equivalent formulation in terms of a kernel defined on the same set. The objective of this project is to study how the Reproducing Kernel Hilbert Space (RKHS) associated to this kernel yields insight into the properties of the inverse problem. In particular, whether there exist closed subspaces on which the inverse problem is well-posed.
Keywords:
análisis matemático
Problemas Inversos
Prerequisites:
None.
Evaluation method: Nota 1-7, with 0/1 available vacants |
Mentor(s): Open in the plataform |
| 24-01-2022 |
Atomic norm regularization with generic atoms
Atomic norm regularization consists in considering an atomic set that forms the building blocks for a class of objects of interest, to then use the Minkowski functional associated to its convex hull as a regularizer. A problem of the convex hull is masking: any atom in the interior of the hull will never be selected when reconstructing an object. In practice, this is avoided by normalizing the atoms. However, this may destroy the structure of the atomic set. In this iPre, we will study strategies to avoid masking, and we will propose a reconstruction method where each atom has a chance of being selected.
Prerequisites:
IMT2113
Evaluation method: Nota 1-7, with 0/1 available vacants |
Mentor(s): Open in the plataform |
| 08-04-2021 |
Mathematical methods for the deconvolution problem
One of the main properties of an optical system is its resolution. This is defined as the minimum separation between two ideal point sources so that they can be distinguished from one another when observed through the system. In practice, the diffraction of light imposes a physical limit to the resolution of the system. For a linear system, this process is typically modeled by a convolution by the Point Spread Function (PSF). For this reason, a technique that improves the resolution of the system can be interpreted as a deconvolution method. The objective of this project is to study mathematical methods proposed in the literature in the past decade, which combine applied Fourier analysis, convex optimization, and probability, for which there exists conditions that ensure they solve the superresolution problem in a computationally efficient manner.
Prerequisites:
IMT2113
Evaluation method: Nota 1-7, with 0/1 available vacants |
Mentor(s): Open in the plataform |