Man

JUAN RICARDO NANCULEF ALEGRIA

Full Time Professor

Universidad Técnica Federico Santa María

Santiago, Chile

Líneas de Investigación


Lecturer in Computer Science at UTFSM, Campus San Joaquín, from 03-2014. Before: Postdoc at University of Bristol, UK. Specialized in Machine Learning, Optimization and Statistics. Doing research in support vector machines, non-linear convex optimization, randomized algorithms, dimensionality reduction and applications to text analysis.

Educación

  •  Doctor en Ingeniería Informática, Universidad Técnica Federico Santa María. Chile, 2011
  •  Magíster en Ciencias de la Ingeniería Informática, Universidad Técnica Federico Santa María. Chile, 2006
  •  Ingeniero Civil Informático, Universidad Técnica Federico Santa María. Chile, 2006

Experiencia Académica

  •   Profesor Instructor Full Time

    Universidad Técnica Federico Santa María

    Santiago, Chile

    2014 - A la fecha

  •   Researcher Full Time

    Universidad Técnica Federico Santa María

    Valparaíso, Chile

    2011 - 2014

  •   Research Assistant Full Time

    Universidad Técnica Federico Santa María

    Valparaíso, Chile

    2005 - 2008

  •   Part-Time Professor Part Time

    Universidad Técnica Federico Santa María

    Valparaíso, Chile

    2006 - 2008

  •   Part-Time Professor Part Time

    Universidad Técnica Federico Santa María

    Valparaíso, Chile

    2011 - 2011

  •   Part-Time Professor Part Time

    Universidad Tecnológica de Chile - INACAP

    Valparaíso, Chile

    2006 - 2008

  •   Part-Time Professor Part Time

    Universidad Adolfo Ibáñez

    Valparaíso, Chile

    2008 - 2008

  •   Research Assistant Part Time

    Universidad de Bologna

    Bologna, Italia

    2009 - 2010

  •   Research Assistant Part Time

    Universidad Técnica Federico Santa María

    Valparaíso, Chile

    2010 - 2011

  •   Co-researcher Project Fondecyt 1070220 Other

    Universidad Técnica Federico Santa María

    Valparaíso, Chile

    2007 - 2007

Experiencia Profesional

  •   Post-doctoral Researcher

    University of Bristol

    Bristol, Reino Unido

    2012 - 2013

Formación de Capital Humano


As a full-time professor at the Department of Informatics of the Federico Santa Maria University I teach to students in a regular basis. Typically, I give the course of Computational Statistics in the Campus Santiago San Joaquín of the university, to undergraduate students, each semester. In addition I give more specialized courses like: Machine Learning and Intelligent Data Analysis, to undergraduate and postgraduate students, at least once per year. I usually supervise students for the completition of their graduation projects (something like a thesis, but for undergraduates). Till now I have only (co-)supervised one post-graduate Master thesis. In the next years I should supervise other Master thesis as well as PhD thesis.
In the last year, I had two students working as research assistants. Their graduation projects will be related to topics of this proposal.


Premios y Distinciones

  •   Best Paper Award

    15th Iberoamercian Congress on Pattern Recognition

    Brasil, 2010

    Best paper award in the 15th Iberoamercian Congress on Pattern Recognition, Sao Paulo, 2010

  •   Valedictorian of the Class 2006 - Informatics Engineering

    UNIVERSIDAD TECNICA FEDERICO SANTA MARIA

    Chile, 2006

    Highest ranking among the graduating class of year 2006 in Informatics Engineering.


 

Article (10)

A novel Frank–Wolfe algorithm. Analysis and applications to large-scale SVM training
Efficient classification of multi-labeled text streams by clashing
TRAINING SUPPORT VECTOR MACHINES USING FRANK-WOLFE OPTIMIZATION METHODS
Training regression ensembles by sequential target correction and resampling
AD-SVMs: A light Extension of SVMs for Multicategory Classification
ENSEMBLE LEARNING WITH LOCAL DIVERSITY
Local negative correlation with resampling
Moderated innovations in self-poised ensemble learning
Self-poised ensemble learning
ROBUST BOOTSTRAPPING NEURAL NETWORKS

BookSection (2)

Support Vector Classification via Computational Geometry Methods
Ensembles Methods for Machine Learning

ConferencePaper (12)

An ensemble method for incremental classification in stationary and non-stationary environments
Two One-Pass Algorithms for Data Stream Classification Using Approximate MEBs
A new algorithm for training SVMs using approximate minimal enclosing balls
A Sequential Minimal Optimization Algorithm for the All-Distances Support Vector Machine
Single-Pass Distributed Learning of Multi-Class SVMs using Core-Sets
L2-SVM training with distributed data
Multicategory SVMs by minimizing the distances among convex-hull prototypes
Bagging with asymmetric costs for misclassified and correctly classified examples
Probabilistic aggregation of classifiers for incremental learning
Robust alternating AdaBoost
Two bagging algorithms with coupled learners to encourage diversity
Multiresolution fuzzy rule systems
26
JUAN NANCULEF

Full Time Professor

Universidad Técnica Federico Santa María

Santiago, Chile

6
Héctor Allende

Professor

Computer Science

UNIVERSIDAD TÉCNICA FEDERICO SANTA MARÍA

Valparaíso, Chile

4
Carlos Valle

Académico Jornada Completa

Computación e Informática

Universidad de Playa Ancha

Valparaíso, Chile

1
Rodrigo Salas

Profesor Titular Jornada Completa

UNIVERSIDAD DE VALPARAÍSO

Valparaíso, Chile