Mario Inostroza-Ponta

Director Departamento

(+56) 2 2718 0901

Grado Académico
Institución
Año
PhD in Computer Science
The University of Newcastle
2008
Magíster en Ingeniería Informática
Universidad de Santiago de Chile
2000
Ingeniero Civil en Informática
Universidad de Santiago de Chile
1999
Licenciado en Ciencias de la Ingeniería
Universidad de Santiago de Chile
1998
Reseña
Mario Inostroza Ponta es Ingeniero Civil en Informática y Magister en Ingeniería Informática de de nuestra Universidad y obtuvo su PhD Computer Science el año 2008 en la Universidad de Newcastle, Australia. Es miembro del cuerpo académico del DIINF desde el año 2002. Tiene experiencia de trabajo en la industria tecnológica y de telecomunicaciones. Su principal experiencia es en ingeniería informática y ciencias de la computación, específicamente en áreas de inteligencia artificial y ciencias de datos, aplicadas a problemas de optimización combinatoria, bioinformática, energías renovables, entre otras. Ha sido autor de más de 15 artículos científicos en revistas y conferencias de alto impacto, dirección de proyectos de investigación tanto nacionales como internacionales, e invitado como relator principal en eventos en Latinoamérica. Ha desempeñado roles de administración académica a nivel gobierno central en la Universidad y del Departamento de Ingeniería Informática, contribuyendo al desarrollo del a Unidad y de la Institución.
Asignaturas que imparte
PREGRADO

• Estructura de Datos

• Taller de Investigación

• Trabajo de Título

POSTGRADO

• Optimización en Ingeniería

• Algoritmos Avanzados

• Metaheurísticas

• Bioinformática

EDUCACIÓN CONTINUA

• Académico Diplomado en Ciencia de Datos Aplicada

Áreas de interés

• Informática aplicada en Biología y Medicina

• Bioinformática y Biología Computacional

• Informática aplicada en la Industria

• Manufactura avanzada

• Informática aplicada en la Sociedad

• Energías Renovables

• Informática aplicada a la Educación

• Informática Educativa

• Informática aplicada en la Ciencia

• Astroinformática

proyectos
Big Data Analytics Tool to Assess the Dynamic Security of Inertial and Frequency Response in the Chilean Power System
Investigador Responsable: HÉCTOR CHAVEZ
Estado: En ejecución

1. – Manuel Villalobos-Cid, César Rivera, Eduardo I. Kessi-Pérez, Mario Inostroza-Ponta, A multi-modal algorithm based on an NSGA-II scheme for phylogenetic tree inference, Biosystems, Volume 213, 2022, https://doi.org/10.1016/j.biosystems.2022.104606

2.- Villalobos-Cid M, Salinas F, Inostroza-Ponta M. Total evidence or taxonomic congruence? A comparison of methods for combining biological evidence. Journal of Bioinformatics and Computational Biology Vol. 18, No. 6, 2020

3.- Parraga-Alava J, Inostroza-Ponta M. Influence of the go-based semantic similarity measures in multi-objective gene clustering algorithm performance. Journal of Bioinformatics and Computational Biology, Vol. 18, No. 6, 2020.

4.- Mario Inostroza-Ponta, Márcio Dorn, Iván Escobar, Leonardo de Lima Correa, Erika Rosas, Nicolás Hidalgo and Mauricio Marin, Exploring the high selectivity of 3-D protein structures using distributed memetic algorithms, in Journal of Computational Science, Volume 41, 2020

5.- Román, J.; González, D.; Inostroza-Ponta, M.; Mahn, A. Molecular Modeling of Epithiospecifier and Nitrile-Specifier Proteins of Broccoli and Their Interaction with Aglycones. Molecules 2020, 25, 772.

6.- Villalobos-Cid, M.; Salinas, F.; Kessi-Pérez, E.I.; De Chiara, M.; Liti, G.; Inostroza-Ponta, M.; Martínez, C. Comparison of Phylogenetic Tree Topologies for Nitrogen Associated Genes Partially Reconstruct the Evolutionary History of Saccharomyces cerevisiae. in Microorganisms 2020, 8, 32.

7.- M. Villalobos-Cid, M. Dorn, R. Ligabue-Braun and M. Inostroza-Ponta,“A Memetic Algorithm Based on an NSGA-II Scheme for Phylogenetic Tree Inference,” in IEEE Transactions on Evolutionary Computation, vol. 23, no. 5, pp. 776-787, Oct. 2019,
doi: 10.1109/TEVC.2018.2883888.

8.- Parraga-Alava, J., Dorn, M., Inostroza-Ponta, M. A multi-objective gene clustering algorithm guided by apriori biological knowledge with intensification and diversification strategies (2018) BioData Mining, 11 (1), art. no. 16, DOI: 10.1186/s13040-018-0178-4

9.- Correa, L., Borguesan, B., Farfan, C., Inostroza-Ponta, M., Dorn, M. A memetic algorithm for 3D protein structure prediction problem (2018) IEEE/ACM Transactions on Computational Biology and Bioinformatics, 15 (3), pp. 690-704. DOI: 10.1109/TCBB.2016.2635143

10.- Borguesan, B., Inostroza-Ponta, M., Dorn, M. NIAS-Server: Neighbors Influence of Amino acids and Secondary Structures in Proteins (2017) Journal of Computational Biology, 24 (3), pp. 255-265. DOI: 10.1089/cmb.2016.0074

11.- Villalobos-Cid, M., Chacón, M., Zitko, P., Instroza-Ponta, M. A New Strategy to Evaluate Technical Efficiency in Hospitals Using Homogeneous Groups of Casemix: How to Evaluate When There is Not DRGs? (2016) Journal of Medical Systems, 40 (4), art. no. 103, pp. 1-12. DOI: 10.1007/s10916-016-0458-9

12.- Borguesan, B., E Silva, M.B., Grisci, B., Inostroza-Ponta, M., Dorn, M. APL: An angle probability list to improve knowledge-based metaheuristics for the three-dimensional protein structure prediction (2015) Computational Biology and Chemistry, 59, pp. 142-157. DOI: 10.1016/j.compbiolchem.2015.08.006

13.- Ramírez-Castrillón, M., Mendes, S.D.C., Inostroza-Ponta, M., Valente, P. (GTG)5 MSP-PCR fingerprinting as a technique for discrimination of wine associated yeasts? (2014) PLoS ONE, 9 (8), art. no. e105870. DOI: 10.1371/journal.pone.0105870

14.- Clark, M.B., Johnston, R.L., Inostroza-Ponta, M., Fox, A.H., Fortini, E., Moscato, P., Dinger, M.E., Mattick, J.S. Genome-wide analysis of long noncoding RNA stability (2012) Genome Research, 22 (5), pp. 885-898. DOI: 10.1101/gr.131037.111

15.- Inostroza-Ponta, M., Berretta, R., Moscato, P. QAPgrid: A two level QAP-based approach for large- scale data analysis and visualization (2011) PLoS ONE, 6 (1), art. no. e14468. DOI: 10.1371/journal.pone.0014468

16.- Riveros, C., Mellor, D., Gandhi, K.S., Mckay, F.C., Cox, M.B., Berretta, R., Vaezpour, S.Y., Inostroza-Ponta, M., Broadley, S.A., Heard, R.N., Vucic, S., Stewart, G.J., Williams, D.W., Scott, R.J., Lechner-Scott, J., Booth, D.R., Moscato, P. A Transcription Factor Map as Revealed by a Genome- Wide Gene Expression Analysis of Whole-Blood mRNA Transcriptome in Multiple Sclerosis (2010) PLoS ONE, 5 (12), art. no. e14176. DOI: 10.1371/journal.pone.0014176

17.- Capp, A., Inostroza-Ponta, M., Bill, D., Moscato, P., Lai, C., Christie, D., Lamb, D., Turner, S., Joseph, D., Matthews, J., Atkinson, C., North, J., Poulsen, M., Spry, N.A., Tai, K.-H., Wynne, C., Duchesne, G., Steigler, A., Denham, J.W. Is there more than one proctitis syndrome? A revisitation using data from the TROG 96.01 trial (2009) Radiotherapy and Oncology, 90 (3), pp. 400-407. DOI: 10.1016/j.radonc.2008.09.019

1.- Felipe-Andrés Bello-Robles, M. Villalobos-Cid, R. B. Panerai, M. Inostroza-Ponta (2021) A multi-objective optimisation approach for the linear modelling of cerebral autoregulation system. Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB 2021). Accepted for publication.

2.- S. Aliaga-Rojas, M. Villalobos-Cid, M. Dorn and M Inostroza-Ponta (2021) A multi-objective approach for the protein structure prediction problem 40th International Conference of the Chilean Computer Science Society (SCCC). Accepted for publication.

3.- J. Fernández Goycoolea, M. Inostroza-Ponta, M. Villalobos-Cid, M. Marin (2021) Single-solution based metaheuristic approach to a novel restricted clustering problem. 40th International Conference of the Chilean Computer Science Society (SCCC). Accepted for publication.

4.- A. C. González, J. Lillo, M. Inostroza-Ponta and M. Villalobos-Cid, (2020) Evaluating the categorisation of the public hospitals in Chile according to case-mix complexity: a genetic algorithm approach, 39th International Conference of the Chilean Computer Science Society (SCCC), Coquimbo, Chile, 2020, pp. 1-9.

5.- C. Rivera, M. Inostroza-Ponta and M. Villalobos-Cid, (2020) A multimodal multi-objective optimisation approach to deal with the phylogenetic inference problem, 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Viña del Mar, Chile, 2020, pp. 1-7.

6.- L. Corrêa, L. Arantes, P. Sens, M. Inostroza-Ponta and M. Dorn, (2020) A dynamic evolutionary multi-agent system to predict the 3D structure of proteins, 2020 IEEE Congress on Evolutionary Computation (CEC), Glasgow, UK, 2020, pp. 1-8.

7.- M. Villalobos-Cid, M. Orellana, O. C. Vásquez, E. Pinto-Sothers and M. Inostroza-Ponta (2019) Dealing with the Balanced Academic Curriculum Problem considering the Chilean Academic Credit Transfer System, 38th International Conference of the Chilean Computer Science Society (SCCC), Concepcion, Chile, pp. 1-7

8.- J. Giglio, M. Inostroza-Ponta and M. Villalobos-Cid, (2019) A multi-objective optimisation evolutionary approach for the Multidimensional Scaling Problem, 38th International Conference of the Chilean Computer Science Society (SCCC), Concepcion, Chile, pp. 1-8.

9.- J. Parraga-Alava, R. A. Caicedo, J. M. Gómez and M. Inostroza-Ponta, (2019) An Unsupervised Learning Approach for Automatically to  Categorize Potential Suicide Messages in Social Media, 2019 38th International Conference of the Chilean Computer Science Society (SCCC), Concepcion, Chile, 2019, pp. 1-8.

10.- M Villalobos-Cid, M Dorn, M Inostroza-Ponta, Understanding the Relationship Between Decision and Objective Space in the Multi-Objective Phylogenetic Inference Problem (2018) IEEE Congress on Evolutionary Computation (CEC), 1-8. DOI: 10.1109/CEC.2018.8477689

11.- M Villalobos-Cid, M Dorn, M Inostroza-Ponta, Performance Comparison of Multi-Objective Local Search Strategies to Infer Phylogenetic Trees (2018) IEEE Congress on Evolutionary Computation (CEC), 1-8. DOI: 10.1109/CEC.2018.8477666

12.- B Borguesan, PH Narloch, M Inostroza-Ponta, M Dorn, A Genetic Algorithm Based on Restricted Tournament Selection for the 3D-PSP Problem (2018) IEEE Congress on Evolutionary Computation (CEC), 1-8. DOI: 10.1109/CEC.2018.8477721

13.- J. Parraga-Alava, G. M. Garzón, R. Alcívar Cevallos and M. Inostroza-Ponta (2018) Unsupervised Pattern Recognition for Geographical Clustering of Seismic Events Post MW 7.8 Ecuador Earthquake, 2018 37th International Conference of the Chilean Computer Science Society (SCCC), Santiago, Chile, pp. 1-8.

14.- Sandoval-Soto, R., Villalobos-Cid, M., Inostroza-Ponta, M. Tackling the bi-objective quadratic assignment problem by characterizing different memory strategies in a memetic algorithm (2018) Proceedings – International Conference of the Chilean Computer Science Society, SCCC, 2017-October, pp. 1-12. DOI: 10.1109/SCCC.2017.8405140

15.- Ruiz-Tagle, B., Villalobos-Cid, M., Dorn, M., Inostroza-Ponta, M. Evaluating the use of local search strategies for a memetic algorithm for the protein-ligand docking problem (2018) Proceedings – International Conference of the Chilean Computer Science Society, SCCC, 2017-October, pp. 1-12. DOI: 10.1109/SCCC.2017.8405141

16.- Villalobos-Cid, M., Vega-Araya, D., Inostroza-Ponta, M. Application of different multi-objective decision making techniques in the phylogenetic inference problem (2018) Proceedings – International Conference of the Chilean Computer Science Society, SCCC, 2017-October, pp. 1-9. DOI: 10.1109/SCCC.2017.8405145

17.- De Lima Corrêa, L., Inostroza-Ponta, M., Dorn, M. An evolutionary multi-agent algorithm to explore the high degree of selectivity in three-dimensional protein structures (2017) 2017 IEEE Congress on Evolutionary Computation, CEC 2017 – Proceedings, art. no. 7969431, pp. 1111-1118. DOI: 10.1109/CEC.2017.7969431

18.- Párraga-Álava, J., Dorn, M., Inostroza-Ponta, M. Using local search strategies to improve the performance of NSGA-II for the Multi-Criteria Minimum Spanning Tree problem (2017) 2017 IEEE Congress on Evolutionary Computation, CEC 2017 – Proceedings, art. no. 7969432, pp. 1119-1126. DOI: 10.1109/CEC.2017.7969432

19.- Escobar, I., Hidalgo, N., Inostroza-Ponta, M., Marin, M., Rosas, E., Dorn, M. Evaluation of a combined energy fitness function for a distributed memetic algorithm to tackle the 3D protein structure prediction problem (2017) Proceedings – International Conference of the Chilean Computer Science Society, SCCC, art. no. 7836019. DOI: 10.1109/SCCC.2016.7836019

20.- Parraga-Alava, J., Inostroza-Ponta, M. A bi-objective model for gene clustering combining expression data and external biological knowledge (2017) Proceedings of the 2016 42nd Latin American Computing Conference, CLEI 2016, art. no. 7833327. DOI: 10.1109/CLEI.2016.7833327

21.- Warren, C., Inostroza-Ponta, M., Moscato, P. Using the QAP grid visualization approach for biomarker identification of cell-specific transcriptomic signatures (2017) Methods in Molecular Biology, 1526, pp. 271-297. DOI: 10.1007/978-1-4939-6613-4_16

22.- Harris, M., Berretta, R., Inostroza-Ponta, M., Moscato, P. A Memetic Algorithm for the Quadratic Assignment Problem with parallel local search (2015) 2015 IEEE Congress on Evolutionary Computation, CEC 2015 – Proceedings, art. no. 7256978, pp. 838-845. DOI: 10.1109/CEC.2015.7256978

23.- Inostroza-Ponta, M., Farfán, C., Dorn, M. A memetic algorithm for protein structure prediction based on conformational preferences of aminoacid residues (2015) GECCO 2015 – Companion Publication of the 2015 Genetic and Evolutionary Computation Conference, pp. 1403-1404. DOI: 10.1145/2739482.2764682

24.- Dorn, M., Inostroza-Ponta, M., Buriol, L.S., Verli, H. A knowledge-based genetic algorithm to predict three-dimensional structures of polypeptides (2013) 2013 IEEE Congress on Evolutionary Computation, CEC 2013, art. no. 6557706, pp. 1233-1240. DOI: 10.1109/CEC.2013.6557706

25.- Meneses, H., Inostroza-Ponta, M.Evaluating memory schemas in a memetic algorithm for the quadratic assignment problem (2012) Proceedings – International Conference of the Chilean Computer Science Society, SCCC, pp. 14-18. DOI: 10.1109/SCCC.2011.3

26.- Arefin, A.S., Inostroza-Ponta, M., Mathieson, L., Berretta, R., Moscato, P. Clustering nodes in large-scale biological networks using external memory algorithms (2011) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7017 LNCS (PART 2), pp. 375-386. DOI: 10.1007/978-3-642-24669-2_36

27.-Inostroza-Ponta, M., Mendes, A., Berretta, R., Moscato, P. An integrated QAP-based approach to visualize patterns of gene expression similarity (2007) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4828 LNAI, pp. 156-167. DOI: 10.1007/978-3-540-76931-6_14

28.- Inostroza-Ponta, M., Berretta, R., Mendes, A., Moscato, P. An automatic graph layout procedure to visualize correlated data (2006) IFIP International Federation for Information Processing, 217, pp. 179-188. DOI: 10.1007/978-0-387-34747-9_19

29.- M. Solar and M. Inostroza (2004), A Scheduling Algorithm to Optimize Real World Applications. ICDCSW 04: Proceedings of the 24th International Conference on Distributed Computing Systems Workshops-W7: EC (ICDCSW04). IEEE Computer Society, pp. 858-862.

1.- Inostroza-Ponta, M., de Vries, N.J., Moscato, P. (2018) World’s best universities and personalized rankings. Handbook of Heuristics.

2.- Warren, C., Inostroza-Ponta, M., Moscato, P. (2017) Using the QAP grid visualization approach for biomarker identification of cell-specific transcriptomic signatures. Methods in Molecular Biology.

1.- O Rojas, M Mendoza, M Marín, M Inostroza-Ponta (2010) Framework de evaluación de crawling focalizado distribuido, Workshop de Sistemas Distribuidos y Paralelismo, (WSDP-JCC 2010). Antofagasta,Chile.

2.- Inostroza-Ponta y P. Moscato (2008), Estudio de robustez del algoritmo de clustering basado engrafos MSTkNN, Encuentro Chileno de Computación, JCC2008. Nov 10-15. Punta Arenas, Chile.

3.- Inostroza, Bastías S., Villanueva M. y Ortiz C. (1999), Metaheurísticas para Coloración de Aristas de Grafos, III Congreso Chileno de Investigación Operativa OPTIMA 99, Arica. Chile.

1.- Unsupervised techniques for the analysis of gene expression data, Instituto de Informática, Universidade Federal do Rio Grande so Sul (UFGRS), 28th January 2010, Porto Alegre, Brasil.

2.- Unidad de Bioinformática, Centro de Genómica Nutricional Agroacuícola (CGNA), 7th of January 2010, Temuco, Chile.

3.- A Graph Approach to Visualize Correlated Data. Bioinformatics Student Symposium organized by The Bioinformatics Institute (New Zealand) and The Australian Research Council Centre in Bioinformatics (ACB), July 11 14, 2006, Auckland University, Auckland, New Zealand.

4.- Discovering Shared Information from DNA Sequences and Documents: A Graph Drawing Approach.Poster Presentation, Summer Symposium in Bioinformatics: BioInfoSummer 2004, December 6 10, Australian National University, Canberra, Australia.

1.- Project STIC-AMSUD cdigo 17-STIC-05 (2017-2018)
Title: PaDMetBio: Parallel and Distributed Metaheuritics for Structural Bioinformatic

2.- Project: DICYT-USACH (2016-2018) Investigador Principal.
Title: Metaheurísticas robustas para enfrentar problemas de optimización multiobjetivos en bioinformática

3.- Project CEBIB Centre for biotechnology and bioengineering, Young Associate Resesarcher
Fondo Basal, 2013, CONICYT.

4.- Project: STIC-AMSUD 13STIC-09 (2013-2014)
Title: Federated Cloud Computing for Bioinformatics: Infrastructure, Algorithms and Applications.

5.- Project: FONDECYT INICIACION 11121288 (2012-2015)
Title: Designing and constructing a scalable pipeline of graph-based methods for the analysis of gene expression data.

6.- Project: DICYT-USACH (2009-2010)
Title: Uso de algoritmos de grafos, optimización combinatorial y bases de datos biológicas para el análisis de datos de expresión genética.

1.- M. Inostroza-Ponta, R. Berretta and P. Moscato. (2010) A Memetic Algorithm for the Visualization of Relationships in Gene Expression Data Sets. ALIO-INFORMS International Meeting. Buenos Aires, Argentina. 6-9 June.

2.- M. Inostroza-Ponta, R. Berretta and P. Moscato. (2010) Incorporating Memory in a StructuredPopulation Based MA for the QAP. ALIO-INFORMS International Meeting. Buenos Aires, Argentina.6-9 June.

3.- N. Pozo-Rojas, M. Inostroza-Ponta and P. Moscato. (2010) Towards a taxonomy of centrality measures for gene expression data sets. ISCB LatinAmerican 2010. Montevideo Uruguay. 13-16 March.

4.- P. Pinacho, M. Solar, M. Inostroza and R. Muñoz (2004), Using Genetic Algorithms and Tabu Search Parallel Models to Solve the Scheduling Problem, IFIP 18th World Computer Congress. Toulouse, France. August.

5.- M. Inostroza and M. Solar (2003), Un Algoritmo de Scheduling para Multiprocesadores con Memoria Compartida, Conferencia Latinoamericana de Informática (CLEI 2003), Oct 3 -5, La Paz, Bolivia.

6.- M. Solar and M. Inostroza (2002), An Automatic Selection of Scheduling Algorithms. PAREO 2002: Euro Working Group on Parallel Processing in Operations Research, Guadaloupe, Francia (Caribe), May.

7.- M. Solar and M. Inostroza (2002), The Scheduling Problem in Parallel Programming. Optimization Days 2002, Montreal, Canada, May.

8.- M. Solar and M. Inostroza (2001), A Scheduler for Parallel Shared-Memory Machine, Optimization Days 2001, Montreal, Canada, May.

9.- M. Inostroza and M. Solar (2001), Selección Automática de Algoritmos de Scheduling, Conferencia Latinoamericana de Informática (CLEI 2001), Mérida, Venezuela, Sept

10.- M. Inostroza, B. Villalón, F. Kri, V. Parada and M. Solar (2000), A Parallel Model for the Simulated Annealing Technique, Optimization Days 2000, Montreal, Canada, May