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J. L. Jara

Académico

+(56) 2 2718 0914

Grado Académico
Institución
Año

Ph.D. in Computer Science

The York University, Inglaterra

2006
Magíster en Ingeniería Informática

Universidad de Santiago de Chile, Chile

1997
Ingeniero Civil en Informática

Universidad de Santiago de Chile, Chile

1997
Licenciado en Ciencias de la Ingeniería

Universidad de Santiago de Chile, Chile

1995
Reseña
J.L. Jara obtiene su maestría el año 1997 en la Universidad de Santiago de Chile y su PhD el año 2006 en The University of York, Inglaterra. Es miembro del Departamento de Ingeniería Informática de la Universidad de Santiago de Chile desde el año 1998. Se ha dedicado principalmente a la ingeniería informática y ciencia de la computación aplicada a la biología y medicina. Además ha tenido experiencia en la industria desarrollando aplicaciones de ventas.
Asignaturas que imparte
PREGRADO

• Inferencia y modelos estadísticos / Tópicos de especialidad 1

POSTGRADO

• Técnicas de Ingeniería en Software

• Técnicas de Ingeniería en Software

• Técnicas de Ingeniería en Software

• Técnicas de Ingeniería en Software

EDUCACIÓN CONTINUA

• Técnicas de Ingeniería en Software

• Técnicas de Ingeniería en Software

• Técnicas de Ingeniería en Software

• Técnicas de Ingeniería en Software

Áreas de interés

• Informática aplicada en Biología y Medicina

• Informática aplicada en Biología y Medicina

• Biología computacional

• Informática aplicada en Biología y Medicina

• Biología computacional

• Informática aplicada a la Educación

• Informática aplicada en Biología y Medicina

• Biología computacional

proyectos
featured
Models of cerebral hemodynamics to detect Parkinson’s Disease and Multiple System Atrophy
Investigador Responsable: MAX CHACÓN PACHECO
Estado: Finalizado
LEER MAS
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Mejorando la Especificidad de la Evaluación de la Calidad de la Autorregulación Sanguínea Cerebral Usando PCC y RAP
Investigador Responsable: JOSÉ LUIS JARA
Estado: En ejecución
LEER MAS

  1. Jara, J. L., Morales-Rojas, C., Fernández-Muñoz, J., Haunton, V. J., & Chacon, M. (2021). Using complexity-entropy planes to detect Parkinson’s disease from short segments of haemodynamic signals. Physiological Measurement, 42(8), 084002. https://doi.org/10.1088/1361-6579/ac13ce

  2. Cavieres, R., Landerretche, J., Jara, J. L., & Chacon, M. (2021). Analysis of cerebral blood flow entropy while listening to music with emotional content. Physiological Measurement, 42(5), 055002. https://doi.org/10.1088/1361-6579/abf885

  3. Elting, J. W., Sanders, M. L., Panerai, R. B., Aries, M., Bor-Seng-Shu, E., Caicedo, A., Chacon, M., Gommer, E. D., Van Huffel, S., Jara, J. L., Kostoglou, K., Mahdi, A., Marmarelis, V. Z., Mitsis, G. D., Müller, M., Nikolic, D., Nogueira, R., Payne, S. J., Puppo, C., Shin, D. C., Simpson, D. M., Tarumi, T., Yelicich, B., Zhang, R., & Claassen, J. A. (2020). Assessment of dynamic cerebral autoregulation in humans: Is reproducibility dependent on blood pressure variability? PLoS ONE 15(1), e0227651. https://doi.org/10.1371/journal.pone.0227651

  4. Sanders, M. L., Elting, J. W. J., Panerai, R. B., Aries, M., Bor-Seng-Shu, E., Caicedo, A., Chacon, M., Gommer, E. D., Van Huffel, S., Jara, J. L., Kostoglou, K., Mahdi, A., Marmarelis, V. Z., Mitsis, G. D., Müller, M., Nikolic, D., Nogueira, R. C., Payne, S. J., Puppo, C., Shin, D. C., Simpson, D. M., Tarumi, T., Yelicich, B., Zhang, R., & Claassen, J. A. H. R. (2019). Dynamic cerebral autoregulation reproducibility is affected by physiological variability. Frontiers in Physiology, 10, 865. https://doi.org/10.3389/fphys.2019.00865

  5. Sanders, M. L., Claassen, J. A. H. R., Aries, M., Bor-Seng-Shu, E., Caicedo, A., Chacon, M., Gommer, E. D., Van Huffel, S., Jara, J. L., Kostoglou, K., Mahdi, A., Marmarelis, V. Z., Mitsis, G. D., Müller, M., Nikolic, D., Nogueira, R. C., Payne, S. J., Puppo, C., Shin, D. C., Simpson, D. M., Tarumi, T., Yelicich, B., Zhang, R., Panerai, R. B., & Elting, J. W. J. (2018). Reproducibility of dynamic cerebral autoregulation parameters: a multi-centre, multi-method study. Physiological Measurement, 39(12), 125002. https://doi.org/10.1088/1361-6579/aae9fd

  6. Chacon M., Jara, J. L., Katsogridakis, E., Miranda, R., & Panerai, R. B. (2018). Non-linear models for the detection of impaired cerebral blood flow autoregulation. PLoS ONE, 13(1), e0191825. https://doi.org/10.1371/journal.pone.0191825

  7. Jara, J. L., Saeed, N. P., Panerai, R. B., & Robinson, T. G. (2018). Increasing the contrast-to-noise ratio of MRI signals for regional assessment of dynamic cerebral autoregulation. In: Heldt T. (eds) Intracranial Pressure & Neuromonitoring XVI. Acta Neurochirurgica Supplement, vol 126. Springer, Cham. https://doi.org/10.1007/978-3-319-65798-1_32

  8. Chacon, M., Noh, S-H., Landerretche, J., & Jara, J. L. (2018). Comparing models of spontaneous variations, maneuvers and indexes to assess dynamic cerebral autoregulation. In: Heldt T. (eds) Intracranial Pressure & Neuromonitoring XVI. Acta Neurochirurgica Supplement, vol 126. Springer, Cham. https://doi.org/10.1007/978-3-319-65798-1_33

  9. Panerai, R. B., Jara, J. L., Saeed, N. P., Horsfield, M. A., & Robinson, T. G. (2016). Dynamic cerebral autoregulation following acute ischaemic stroke: comparison of transcranial doppler and magnetic resonance imaging techniques. Journal of Cerebral Blood Flow & Metabolism, 36(12), 2194‒2202. https://doi.org/10.1177/0271678X15615874

  10. Chacon, M., Jara, J. L., Varas, N., & Panerai, R. B. (2015). Analysis of the influence of systemic and intracranial pressure in patients with severe head injury using linear and non-linear models. IFMBE Proceedings, 49, 544‒547. https://doi.org/10.1007/978-3-319-13117-7_139

  11. Chacon, M., Jara, J. L., & Panerai, R. B. (2014). A new model-free index of dynamic cerebral blood flow autoregulation. PLoS ONE, 9(10), e108281. https://doi.org/10.1371/journal.pone.0108281

  12. Jara, J., & Chacon, M. (2014). The effect of different body positions on the assessment of dynamic cerebral autoregulation. The FASEB Journal, 28(S1), 1184.4. https://doi.org/10.1096/fasebj.28.1_supplement.1184.4

  13. Chacon, M., Bello, F., Jara, J. L., & Panerai, R. B. (2014). Comparison of autoregulatory indexes on spontaneous variations with linear support vector machines. The FASEB Journal, 28(S1), 1184.9. https://doi.org/10.1096/fasebj.28.1_supplement.1184.9

  14. Horsfield, M. A., Jara, J. L., Saeed, N. P., Panerai, R. B., & Robinson, T. G. (2013). Regional differences in dynamic cerebral autoregulation in the healthy brain assessed by magnetic resonance imaging. PLoS ONE, 8(4), e62588. https://doi.org/10.1371/journal.pone.0062588

  15. Saeed, N. P., Horsfield, M. A., Jara, J. L., Panerai, R. B., & Robinson, T. G. (2013). Dynamic cerebral autoregulation assessed by magnetic resonance imaging and transcranial Doppler: a comparative study in acute ischaemic stroke. Cerebrovascular Diseases, 35(suppl. 2), 20. https://doi.org/10.1159/000351746

  16. Jara, J. L., Chacon, M., & Zelaya, G. (2011). Empirical evaluation of three machine learning method for automatic classification of neoplastic diagnoses. Ingeniare. Revista chilena de ingeniería, 19(3). https://doi.org/10.4067/S0718-33052011000300006

  1. Jara, J. L., Morales-Rojas, C., Fernández-Muñoz, J., Haunton, V. J., & Chacon, M. (2021). Using complexity-entropy planes to detect Parkinson’s disease from short segments of haemodynamic signals. 10th International Meeting on Cerebral Haemodynamic Regulation (CARNet meeting). Shenzhen, China. 

  2. Bello, F. A., Jara, J. L., Panerai, R. B., Haunton, V. J., & Chacon, M. (2021). A new CrCP sensitivity index under hypocapnic conditions using support vector machines. 10th International Meeting on Cerebral Haemodynamic Regulation (CARNet meeting). Shenzhen, China. 

  3. Cavieres, R., Landerretche, J., Jara, J. L., & Chacon, M. (2021). Analysis of cerebral blood flow entropy when listening music with emotional content. 10th International Meeting on Cerebral Haemodynamic Regulation (CARNet meeting). Shenzhen, China. 

  4. Cavieres, R., Landerretche, J., Jara, J. L., & Chacón, M. (2021). Analysis Of Cerebral Blood Flow Complexity when Listening Music with Emotional Content. 12th International Multi-Conference on Complexity, Informatics and Cybernetics, IMCIC 2021. Orlando, USA.

  5. Jara, J. L., Chacon, M., Lobos-Vasquez, M. F., Alcibar-Cevallos, R., & Panerai R. B. (2019). Simple measures to detect impaired cerebral autoregulation. 9th International Meeting on Cerebral Haemodynamic Regulation (CARNet meeting). Leuven, Belgium.

  6. Chacon, M., Jara, J. L., Alcibar-Cevallos, R., & Panerai R. B. (2019). Novel Evaluation of Cerebral Autoregulation models. 9th International Meeting on Cerebral Haemodynamic Regulation (CARNet meeting). Leuven, Belgium.

  7. Jara, J. L., Saeed, N. P., Panerai, R. B., & Robinson, T. G. (2018). A preliminary study of entropy and complexity measures to assess cerebral autoregulation. 8th International Meeting on Cerebral Haemodynamic Regulation (CARNet meeting). Oxford, UK.

  8. Chacon, M., Alcibar, R., Jara, J. L., & Panerai R. B. (2018). Spontaneous fluctuations hemodynamic signal classification using complexity-entropy plane. 8th International Meeting on Cerebral Haemodynamic Regulation (CARNet meeting). Oxford, UK.

  9. Jara, J. L., Chacon, M., Hernandez, M., & Panerai R. B. (2017). Assessing non-invasive CrCP estimation methods by their ability to rebuild original physiological signals. 7th International Meeting on Cerebral Haemodynamic Regulation (CARNet meeting). Berlin, Germany.

  10. Chacon, M., Gajardo, N., Jara, J. L., & Panerai R. B. (2017). Required duration of spontaneous variation signals to assess cerebral dynamic autoregulation with non-linear models. 7th International Meeting on Cerebral Haemodynamic Regulation (CARNet meeting). Berlin, Germany.

  11. Jara, J. L., Saeed, N. P., Panerai, R. B., & Robinson, T. G. (2016). Increasing the contrast-to-noise ratio of MRI signals for regional assessment of dynamic cerebral autoregulation. 6th International Meeting on Cerebral Haemodynamic Regulation (CARNet meeting). Cambridge, USA.

  12. Chacon, M., Noh, S-H., Landerretche, J., & Jara, J. L. (2016). Comparing models of spontaneous variations, maneuvers and indexes to assess dynamic cerebral autoregulation. I6th International Meeting on Cerebral Haemodynamic Regulation (CARNet meeting). Cambridge, USA.

  13. Jara, J. L., Chacon, M., & Panerai R. B. (2015). Assessment of dynamic cerebral autoregulation without blood pressure measurement. 5th International Meeting on Cerebral Haemodynamic Regulation (CARNet meeting). Southampton, UK.

  14. Chacon, M., Noh, S-H., & Jara, J. L. (2015). A new index for dynamic cerebral autoregulation applied to the sit-to-stand maneuver. 5th International Meeting on Cerebral Haemodynamic Regulation (CARNet meeting). Southampton, UK.

  15. Chacon, M., Jara, J. L., Varas, N., & Panerai, R. B. (2015). Analysis of the influence of systemic and intracranial pressure in patients with severe head injury using linear and non-linear models. 6th Latin American Congress on Biomedical Engineering, CLAIB 2014. Paraná, Argentina.

  16. Jara, J., & Chacon, M. (2014). The effect of different body positions on the assessment of dynamic cerebral autoregulation. 4th International Meeting on Cerebral Haemodynamic Regulation (CARNet meeting). San Diego, USA.

  17. Chacon, M., Bello, F., Jara, J. L., & Panerai, R. B. (2014). Comparison of autoregulatory indexes on spontaneous variations with linear support vector machines. 4th International Meeting on Cerebral Haemodynamic Regulation (CARNet meeting). San Diego, USA.

  18. Saeed, N. P., Horsfield, M. A., Jara, J. L., Panerai, R. B., & Robinson, T. G. (2013). Dynamic cerebral autoregulation assessed by magnetic resonance imaging and transcranial Doppler: a comparative study in acute ischaemic stroke. 3rd Meeting of the Cerebral Autoregulation Network (CARNet meeting). Porto, Portugal.

  19. Mark A. Horsfield, Nazia P. Saeed, J.L. Jara, Amit K. Mistri, Tom G. Robinson & Ronney B. Panerai. Magnetic Resonance Imaging Measurement of Cerebral Blood Flow Response to the Thigh Cuff Manoeuvre. Poster session presented at the Twenty-fifth International Symposium on Cerebral Blood Flow, Metabolism and Function and the Tenth International Conference on Quantification of Brain Function with PET, 2011, Barcelona, Spain

  20. Oporto, CA.; Lazo, FI.; Rivera, GA.; Holmes, DS.; Jara, JL.; Quatrini, R. (2009). New insights on relevant cellular processes of bioleaching microbial consortia gained from fucntional association network analysis. 18th International Biohydrometallurgy Symposium. Bariloche, Argentina.

  21. Jara, J. L., & Acevedo-Crespo, R. (2009). Crisp Classifiers vs. Fuzzy Classifiers: A Statistical Study. 9th International Conference on Adaptive and Natural Computing Algorithms (ICANNGA 2009). Kuopio, Finland.

  22. Lazo, F. I., Oporto, C. A., Rivera, G. A., Holmes, D. S., Jara, J. L., & Quatrini, R. (2008) Functional association networks derived from the structural information embedded in the genome of Escherichia coli K-12. 5th International Congress of the Red Iberoamericana de Bioinformática. Santiago, Chile.

  23. Jara, J. L., & Pizarro, R. (2008). Repairing Medical Diagnoses Written in Natural Language. 3er Congreso Latinoamericano de Informática Médica. Buenos Aires, Argentina.

  24. De Boni, M., Jara, J. L., & Manandhar, S. (2002). The YorkQA Prototype Question Answering System.  11th Text Retrieval Conference. Gaithersburg, Maryland, USA.

  25. Alfonseca, E., De Boni, M., Jara, J. L., & Manandhar, S. (2001). A ProtoType Question Answering System Using Syntactic and Semantic Information for Answer Retrieval. 10th Text Retrieval Conference. Gaithersburg, Maryland, USA.

  26. Kri, M. Solar, V. Parada & J.L. Jara. Modelos paralelos para algoritmos genéticos. Primer Encuentro Latinoamericano de Optimización (I ELIO) y Segundo Congreso Chileno de Investigación Operativa (Optima 97), 1997, Concepción, Chile

  1. Bello, F. A., Köhler, J., Hinrechsen, K., Araya, V., Hidalgo, L., & Jara, J. L. (2020). Using machine learning methods to identify significant variables for the prediction of first-year Informatics Engineering students dropout. 21th Chilean Congress of ICTs for Education (TICXED 2020). Coquimbo, Chile. https://doi.org/10.1109/SCCC51225.2020.9281280
  2. Köhler, J., Bello, F. A., & Jara, J. L. (2020). Predictive model for estimating internal transfer of Informatics Engineering students. 21th Chilean Congress of ICTs for Education (TICXED 2020). Coquimbo, Chile. https://doi.org/10.1109/SCCC51225.2020.9281230
  3. Marinkovic Febré, E., & Jara, J. L. (2010). Modelo de sistema tutorial inteligente orientado al desarrollo de competencias. XI Conference On Higher Education In Computer Science, Jornadas Chilenas de Computación. Antofagasta, Chile.
  4. Jara, J. L., & Jara, G. (2008). Modelo de sistema tutorial inteligente orientado al desarrollo de competencias. Workshop de Inteligencia Artificial, Jornadas Chilenas de Computación. Punta Arenas, Chile.
  5. Jara, J. L., & Baladron, J. (2008). Estudio de una arquitectura multiagente para el control de señales de tránsito. Workshop de Inteligencia Artificial, Jornadas Chilenas de Computación. Punta Arenas, Chile.
  6. Pizarro, R., & Jara, J. L. (2007). Una propuesta para apoyar la clasificación de diagnóstico médicos escritos en lenguaje natural. Workshop de Inteligencia Artificial, Jornadas Chilenas de Computación. Iquique, Chile.
  7. Jara, J. L., & Zelaya, G. (2007). Desafíos en la codificación de diagnósticos médicos. Jornadas Chilenas de Ingeniería Biomédica. Viña del Mar, Chile.
  8. Cordero, P., & Jara, J. L. (2005). Evaluación empírica de algoritmos para modelación de Entropía Máxima. XIII Encuentro Chileno de Computación, Jornadas Chilenas de Computación. Valdivia, Chile.

J.L. Jara & Rodrigo Acevedo-Crespo. Crisp Classifiers vs. Fuzzy Classifiers: A Statistical Study. In M. Kolehmainen, P. Toivanen & B. Beliczynski, editor(s). Adaptive and Natural Computing Algorithms. Lecture Notes in Computer Science 5495:440-447. Springer-Verlag 2009.

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