Felipe Arturo Tobar Henriquez
Associate Professor
Universidad de Chile
Santiago, Chile
Machine Learning; Artificial Intelligence; Applied Statistics; Time Series; Signal Processing
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Signal Processing, IMPERIAL COLLEGE LONDON. Reino Unido, 2014
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Control Systems, UNIVERSIDAD DE CHILE. Chile, 2010
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Electrical Engineering, UNIVERSIDAD DE CHILE. Chile, 2007
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Electrical Engineering, UNIVERSIDAD DE CHILE. Chile, 2010
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Research Associate Full Time
UNIVERSITY OF CAMBRIDGE
Cambridge, Reino Unido
2014 - 2015
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Adjunct Lecturer (non-tenure) Full Time
UNIVERSIDAD DE CHILE
FCFM
Chile
2018 - 2021
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Researcher Full Time
UNIVERSIDAD DE CHILE
FCFM
Chile
2015 - A la fecha
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Associate Researcher Other
UNIVERSIDAD TECNICA FEDERICO SANTA MARIA
Chile
2020 - A la fecha
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Associate Professor Full Time
UNIVERSIDAD DE CHILE
FCFM
Santiago, Chile
2021 - A la fecha
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CMM-Data Group Leader Part Time
Center for Mathematical Modeling
Chile
2015 - A la fecha
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Coordinator, Master of Data Science Other
University of Chile
Santiago, Chile
2020 - 2022
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Member - study group Eng II and machine learning Other
Fondecyt
Chile
2021 - 2021
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Director, Initiative for Data & AI Full Time
Universidad de Chile
Santiago, Chile
2022 - A la fecha
Postdocs:
1) Application of Optimal Transportation to Machine Learning --- Elsa Cazelles --- research associate --- 1/2019 to 9/2020--- Dpt. Ingeniería Matemática
During the last 5 years, I have supervised and examined the following research theses.
Nomenclature:
MSc = Master of Science
Eng = Engineer's professional title
EE = Electrical Engineering
ME = Mathematical Engineering
IE = Industrial Engineering
DS = Data Science
CS = Computer Science
All degrees given at Universidad de Chile unless otherwise stated
All thesis supervised only by me unless otherwise stated
i) Current students
Sebastián López (MSc-ME & Eng-ME): Gaussian process and neural networks
Diego Canales (MSc-DS, & Eng-EE): Emulation of audio devices
Alonso Letelier (MSc-ME & Eng-ME): Probabilistic models for time series
Víctor Caro (MSc-DS & Eng-CS): Generalisation on neural networks
Camila Bergasa (MSc-DS, & Eng-IE)
Bruno Moreno (MSc-DS, & Eng-ME)
David Molina (MSc-ME & Eng-ME)
Benjamín Pizarro (PhD, Medical Science - jointly supervised)
ii) Completed thesis as supervisor
14) 2022, Jou-Hui Ho (MSc-EE & Eng-EE) Greedy online change point detection
13) 2021, Matías Altamirano (MSc-ME & Eng-ME): Nonstationary multi-output Gaussian processes via harmonizable spectral mixtures
12) 2021 Cristóbal Valenzuela (MSc-ME & Eng-ME): Bandlimited Functions in Machine Learning
11) 2021 Diego León (Eng-EE): Deconvolución en audio utilizando modelos basados en Machine y Deep Learning
10) 2020. Gonzalo Ríos (PhD-ME, co-supervised), Contributions to Bayesian Machine Learning via Transport Maps
9) 2020. Alejandro Cuevas (Eng-EE & MSc-ME), Multioutput Gaussian process toolkit with sparse formulation for spectral kernels
8) 2020. Juan Ruiz (Eng-EE), Destilación de modelo en redes convolucionales
7) 2020. Mauricio Campos (Eng-ME & MSc-ME), Análisis de imágenes hiperespectrales geológicas mediante herramientas de aprendizaje de máquinas
6) 2019 Lerko Araya (Eng-EE & MSc-EE), Un enfoque moderno para la estimación espectral probabilística
5) 2018. Iván Castro (Eng-EE & MSc-EE), Predicción no lineal en línea de series de tiempo mediante el uso y mejora de algoritmos de filtros adaptivos de Kernel
4) 2018 Rodrigo Lara (Eng-ME), Clasificación en imágenes satelitales: superficie construida y uso del suelo
3) 2017. Gabriel Parra (Eng-ME & MSc-ME), Spectral Mixture Kernels for Multi-Output Gaussian Processes
2) 2017. Romain Gouron (Eng-ME), Estudiando Obras Literarias con Herramientas de Procesamiento de Lenguaje Natural
1) 2017 David Gómez (Eng-EE, co-supervised), Mejoramiento de la clasificación funcional de enzimas usando aprendizaje de máquinas
iii) Examined thesis
17) 2022. Abdiel Ricaldi M. (MSc-EE), Diseño y comparación de controladores fraccionales en un banco de celdas y una columna de flotación en el proceso de extracción de cobre
16) 2021. Jhon Intriago C. (MSc-EE), Development of Spike Neural Networks Models Based on Information Theory and Biological Optimitization Criterion
15) 2021. Nicolás Cruz B. (Eng-EE & MSc-EE), Bridging the gap between simulation and reality using generative neural networks
14) 2021. Mauricio González (Eng-EE & MSc-EE), A fast-running failure prognostic algorithm based on a non-homogeneous markow chain
13) 2020. Daniel Augusto Ramos (MSc-Computer Science, Universidade Federal do Ceará), Contributions on latent projections for Gaussian processes modelling
12) 2020. Manuel Suil (Eng-ME & MSc-ME), Análisis sobre métodos de ajuste y aprendizaje de máquinas aplicados a la equivalencia y reducción de modelos de electrofisiología cardíaca
11) 2020. Nicolás Cruz (Eng-EE & MSc-EE), Bridging the gap between simulation and reality using generative neural networks
10) 2019. Rodrigo Pérez D. (Eng-EE & MSc-EE), Interactive learning with corrective feedback for continuous-action policies based on deep neural networks
9) 2019 Diego Ibáñez I. (Eng-IE) Predicción y descripción de la exclusión educativa del sistema escolar regular Chileno, ciencia de datos para la innovación pública
8) 2019 Pedro orellana (Eng-EE & MSc-EE), Segmentación semántica y reconocimiento de lugares usando características CNN pre-entrenadas
7) 2018 Tomás Valdivia (Eng-IE) Aplicaciones de aprendizaje de máquinas en electroencefalografias para salud mental
6) 2018 Antonia Larrañaga R. (Eng-EE), Evaluación de carga cognitiva y estado emocional mediante sensores psico-fisiológicos en tareas de redacción
5) 2018 Matías Mattamala (Eng-EE & MSc-EE), Localización visual en robots de recursos computacionales limitados
4) 2018 Alonso Guzman (Eng-EE) Árboles de decisión e identificación de genes en bacterias
3) 2018 Kenzo Lobos (Eng-EE & MSc-EE), Aprendizaje reforzado en robótica móvil
2) 2018. Diego Campanini G. (Eng-EE), Detección de objetos usando redes neuronales convolucionales junto con Random Forest y Support Vector Machines
1) 2017 Claudia Soto (Eng-EE & MSc-EE), Reconocimiento rápido de objetos usando object proposals y Deep Learning
1) PROFESSIONAL SERVICE (RECENT)
Editorial and Grant Agency work
-2021: Associate Editor, IEEE Transactions on Neural Networks and Learning Systems.
-2021: Member of the Fondecyt Study Group (Engineering II, Machine Learning). Duties include to recruit reviewers and assess completion reports for Chile’s Fondecyt grants.
-2021: Topic Chair - The IEEE Latin American Conference on Computational Intelligence (LA-CCI)
-2021: Guest Editor - MDPI Entropy Special Issue Adaptive filters and machine learning algorithms for non-linear system identification and processing
Reviewer
-Journals: Journal of Machine Learning Research, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Signal Processing, IEEE Transactions on Neural Networks and Learning Systems, Springer Machine Learning
-Conferences: NeurIPS, ICML, AAAI, UAI, ICLR, AISTATS, IEEE-MLSP & IEEE-ICASSP
2) TALKS (RECENT)
The Art of Gaussian processes: Classic and Contemporary
Dec 2021: Neural Information Processing Systems (online)
From Gaussian processes to Multioutput Gaussian processes
Nov 2021: IEEE Escuela de Verano en Inteligencia Computacional (at Temuco, Chile, given remotely)
Multioutput Gaussian processes
Sept 2021: Gaussian Process Summer School (at Sheffield, UK, given remotely)
Multioutput Gaussian processes for EEG imputation
14 Jan 2021: AC3E, Bioinformatics Group, U. Santa María, Valparaíso, Chile (3eonline)
25 Nov 2021: AC3E, U. Santa María, Valparaíso, Chile (online)
19 Dec 2020: IEEE EVIC, U. de la Frontera, Temuco, Chile (online)
Discussion Panel (participant): Data Science & AI, transforming the industry form Academia
26 Nov 2020: Facultad de Ciencias Físicas y Matemáticas, U. de Chile, Santiago, Chile
Bayesian reconstruction of Fourier Pairs
30 Oct 2020: Seminar series of Astroinformatics, ALERCE, U. de Chile, Santiago, Chile
Data Science projects at the Center for Mathemtical Modeling
16 Oct 2020: Entel, Data Science Division
Sharing the experience of an Electrical Engineer in the Academia
8 Oct 2020: Electrotutores, Department of Electrical Engineering, U. de Chile, Santiago, Chile
Band-limited Gaussian processes: The sinc kernel
6 Dec 2019: Conferencia Latinoamericana de Probabilidades y Estadística, Mérida, México
13 Dec 2019: NeurIPS conference (poster presentation only)
Multi-output Gaussian processes: Generative models and toolbox demonstration
8 Aug 2019: Pucón Data Science Symposium, Puerto Varas, Chile
An introduction to Artificial Intelligence and Machine Learning
24 May 2019: Chile-Italy Forum, U. de Concepción, Concepción, Chile
Round Table participation: Challenges in Data Science, from fundamentals to applications
9 May 2019: 80 anniversary CNRS: CNRS in SouthAmerica. PUC, Santiago, Chile
Bayesian Nonparametric Spectral Estimation
19 June 2019: Mathematical Modelling Seminar, Pontificia U. de Chile.
4 Dec 2018: The Neural Information Processing Systems Conference, Montreal, Canada (slides)
14 Dec 2018: Escuela de Verano en Inteligencia Computacional (EVIC), Santiago, Chile (slides)
A Gentle Introduction to Gaussian Proceses with Applications
19 June 2018: Grupo de Aprendizaje de Máquinas en Biomedicina y Salud, U. de Chile, Santiago, Chile.
24 August 2018: Instituto Fundamentos de los Datos, U. de Chile, Santiago, Chile.
6 Nov 2018: U. de Valparaíso, Dept of Statistics, Valparaíso, Chile.
7 Nov 2018: U. de Valparaíso, Dept of Engineering, Valparaíso, Chile.
3) SCIENTIFIC CONSULTANCY
CMM & PSI-net & Codelco (PI, 12/2019 - 12/2020)
Detection of operational risk from video recordings of mining operation.
CMM & BancoEstado (PI, 8/2017 - 9/2017)
Design of forecasting algorithms for costs and profit in small companies.
Innovaxxión & Advanced mining technology center (Research Assoc. 6/2016 - 4/2018)
Mineral detection and classification based on hyperspectral analysis.
CMM & Uplanner (Research Assoc. 8/2016 - 4/2017)
Demand forecasting on higher-education courses.
CMM & Mutual de Seguridad (Research Assoc. 10/2016)
High-Dimensional Kernel Regression: A Guide for Practitioners |
ADVANCES ON GENERATIVE MODELS FOR STATISTICAL MACHINE LEARNING: THEORY AND PRACTICE |
Detection of neonatal seizures from EEG: A continual & multi-channel approach |
AI for Everyone: benefitting from and building trust in the technology |
On the relationship between Gaussian process regression and spectral estimation |
Sistema de detección y clasificación mineralógica rápida basado en análisis híper espectral |
Machine Learning Meets Signal Processing |
Machine Learning for Time-Series Data |
SEQUENTIAL MONTE CARLO METHODS AND FEEDBACK CONCEPTS APPLIED TO FAULT DIAGNOSIS AND FAILURE PROGNOSIS IN NONLINEAR, NON-GAUSSIAN DYNAMIC SYSTEMS |