1. Faculty and Staff
  2. Faculty
  3. Christopher P. Monterola, PhD

Expertise

  • Complexity Science, Neural Networks, Computational and Statistical Physics, Social Networks, Biological Physics, Predictive Analytics, Project Management, Data Analytics, Machine Learning, Forecasting

Research Interests

  • Complex systems, Neural networks, Network science, Computational and statistical physics
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Christopher P. Monterola, PhD

Professor
Aboitiz Chair in Data Science
Head, Aboitiz School of Innovation, Technology, and Entrepreneurship
Executive Managing Director, ACCeSs@AIM Academician, NAST

Academic Background

  • Ph.D. in Physics, (Most Outstanding PhD Student (College of Science), Edgardo Gomez Award for Excellence in Dissertation Research, Batch Valedictorian), University of the Philippines Diliman
  • Master of Science in Physics, (Most outstanding MS student (College of Science), Batch Valedictorian), University of the Philippines Diliman
  • Bachelor of Science in Applied Physics, (Magna cum Laude, Dean’s medallion, Best Thesis), University of the Philippines Diliman

Professional and Academic Experience

  • Senior Scientist and Capability Group Manager (CGM) of the Complex Systems (CxSy) Capability Group, Computing Science Department, Institute of High Performance Computing, A*STAR, Singapore (founding CGM, August 2013-present)
  • Adjunct Senior Research Fellow, Complexity Institute, Nanyang Technological University, Singapore. (October 2015-present)
  • Principal Investigator, Complex Systems Programme, A*STAR Urban Systems Initiative (2012-present)

Affiliations, Awards, and Honors

  • 2020 Academician by the National Academy of Science and Technology 
  • 2016, Country Prize Winner (Singapore), United Nation Global Pulse's The Big IDEAS Competition for Sustainable Cities and Urban Communities, June 2016. EF Legara, C Monterola, JF Valenzuela.
  • 2016, SMRT Most Innovative Solution Award, IHPC (from CxSy MA Ramli, V Jayaraman, G Lee, C Monterola), SMRT Vendors'; Day, 30 March 2016.
  • 2014, ICCS Best Workshop Paper. N Othman, EF Legara, V Selvam, C Monterola, "Simulating Congestion Dynamics of Train Rapid Transit using Smart Card Data," International Conference on Computational Science, Cairns, Australia, 9-12 June 2014.
  • 2013, IEEE SCALE Challenge. First Prize Winner. H Kasim, T Hung, EF Legara, C Monterola, G Lee, X Li, BS Lee, S Lu, L Wang, and V Jayaraman, "Scalable Complex System Modeling for Sustainable City," Sixth IEEE International Scalable Computing Challenge (SCALE 2013), Delft, Netherlands, 14-16 May 2013.
  • 2009-present, Conferred the scientist rank 1 by the University of the Philippines System
  • 2008-2012, University of the Philippines Centennial Professorial Chair

Peer-reviewed Journals

  • Dorosan, M., Dailisan, D., Valenzuela, J.F., & Monterola, C. P. (2024). Use of machine learning in understanding transport dynamics of land use and public transportation in a developing city. Cities, 114, 104587. https://doi.org/10.1016/j.cities.2023.104587
  • Liponhay, M. P., Valerio, A. V., & Monterola, C. P. (2024). Time-delayed causal network analysis of meteorological variables and air pollutants in Baguio City. Atmospheric Pollution Research, 15(6), 102095. https://doi.org/10.1016/j.apr.2024.102095
  • Liponhay M, Valerio, A, Fornan, G, Alis, C, & Monterola, C. P. (2024). Dynamic assessment of urban carrying capacity load number using the enhanced UCCLN Model. Sustainability, 16(1):35. https://doi.org/10.3390/su16010035
  • Liponhay M, Valenzuela, J.F., Dorosan, M, Dailisan, D, & Monterola, C. P. (2023). A dynamic urban mobility index from clustering of vehicle speeds in a tourist-heavy city. Applied Sciences, 13(23):12763. https://doi.org/10.3390/app132312763
  • Ibañez, S.C., & Monterola, C.P. (2023). A global forecasting approach to large-scale crop production prediction with time series transformers. Agriculture, 13, 1855. https://doi.org/10.3390/agriculture13091855
  • Javier, P. J. E. A., Liponhay, M. P., Dajac, C. V. G., & Monterola, C. P. (2022). Causal network inference in a dam system and its implications on feature selection for machine learning forecasting. Physica A: Statistical Mechanics and its Applications, 604, https://doi.org/10.1016/j.physa.2022.127893
  • Tan, H. E., Hong, W. O., Othman, N. B., Legara, E. F., Monterola, C. P., & Ramli, M. A. (2022). Quantifying the resilience of rapid transit systems: A composite index using a demand-weighted complex network model. Plos One, 17(4), e0267222. https://doi.org/10.1371/journal.pone.0267222
  • Dailisan, D., Liponhay, M., Alis, C. M., & Monterola, C. P. (2022). Amenity counts significantly improve water consumption predictions. PLoS ONE, 17(3), e0265771. https://doi.org/10.1371/journal.pone.0265771
  • Valenzuela, J. F., Legara, E.F., & Monterola, C. P. (2022). Typology, network features and damage response in worldwide urban road systems. PLoS ONE, 17(3), e0264546. https://doi.org/10.1371/journal.pone.0264546
  • Ibañez, S. C., Dajac, C. V. G., Liponhay, M. P., Legara, E. F. T., Esteban, J. M. H., & Monterola, C. P. (2022). Forecasting reservoir water levels using deep neural networks: A case study of Angat Dam in the Philippines. Water, 14(1), 34. https://doi.org/10.3390/w14010034.
  • Alis, C., Legara, E.F., & Monterola, C. P. (2021). Generalized radiation model for human migration. Scientific Reports, 11, 22707. https://doi.org/10.1038/s41598-021-02109-1
  • Goswami, K., Giarmatzi, C., Monterola, C. P., Shrapnel, S., Romero, J., & Costa, F. (2021). Experimental characterization of a non-Markovian quantum process. Physical Review A, 102(2), https://doi.org/10.1103/PhysRevA.104.022432
  • Yang, B., Ren, S., Legara, E. F., Li, Z., Ong, E. Y. X., Lin, L., & Monterola, C. P. (2020). Phase transition in taxi dynamics and impact of ridesharing. Transportation Science, 54(1), 1-297. https://pubsonline.informs.org/doi/10.1287/trsc.2019.0915.
  • Zhong, X., Lu, Y., Gao, Q., Nyunt, M. S. Z., Fulop, T., Monterola, C. P., Tong, J. C., Larbi, A., & Ng, T. P. (2020). Estimating biological age in the Singapore longitudinal aging study. Journals of Gerontology: Series A, 75(10), 1913-1920. https://doi.org/10.1093/gerona/glz146.
  • Legara, E. F., & Monterola, C. P. (2018). Inferring passenger types from commuter eigentravel matrices. Transportmetrica B: Transport Dynamics, 6(3), 230-250. https://doi.org/10.1080/21680566.2017.1291377.
  • Valenzuela, J. F., Monterola, C. P., Tong, V. J. C., Ng, T. P., & Larbi, A. (2017). Health and disease phenotyping in old age using a cluster network analysis. Scientific Reports, 7, 15608. https://doi.org/10.1038/s41598-017-15753-3.
  • Yang, B., & Monterola, C. P. (2016). Efficient intersection control for minimally guided vehicles: A self-organised and decentralised approach. Transportation Research Part C: Emerging Technologies,72, 283-305. https://doi.org/10.1016/j.trc.2016.10.004.
  • Hu, N., Legara, E. F., Lee, K. K., Hung, G. G., & Monterola, C. P. (2016). Impacts of land use and amenities on public transport use, urban planning and design. Land Use Policy, 57, 356-367. https://doi.org/10.1016/j.landusepol.2016.06.004.
  • Yang, B., & Monterola, C. P. (2015). Classification and unification of the microscopic deterministic traffic models. Physical Review E, 92(4), 042802. https://doi.org/10.1103/PhysRevE.92.042802.
  • Narang, V., Ramli, M. A., Singhal, A., Kumar, P., de Libero, G., Poidinger, M., & Monterola, C. P. (2015). Automated identification of core regulatory genes in human gene regulatory networks. PLoS Computational Biology. https://doi.org/10.1371/journal.pcbi.1004504.
  • Decraene, J., Monterola, C. P., Lee, G. K., Hung, T. G., & Batty, M. (2013). The emergence of urban land use patterns driven by dispersion and aggregation mechanisms. PLOS ONE, 8(12), e80309. https://doi.org/10.1371/journal.pone.0080309.
  • Legara, E. F., Monterola, C. P., & David, C. (2013). Complex network tools in building expert systems that perform framing analysis. Expert Systems with Applications, 40(11), 4600-4608. https://doi.org/10.1016/j.eswa.2013.01.064.
  • David, C. C., Atun, J. M., Legara, E. F. & Monterola, C. P. (2011). Finding frames: Comparing two methods of frame analysis. Communication Methods and Measures, 5(4), 329-351. https://doi.org/10.1080/19312458.2011.624873.
  • Juanico, D. E., Longjas, A., Batac, R., & Monterola, C. P. (2008). Avalanche statistics of driven granular slides in a miniature mound. Geophysical Research Letters, 35(19), L19403. https://doi.org/10.1029/2008GL035567.
  • Monterola, C. P., & Saloma, C. (2002). Noise-driven manifestation of learning in mature neural networks. Physical Review Letters, 89(18), 188102. https://doi.org/10.1103/PhysRevLett.89.188102.
  • Quito, M., Monterola, C. P., & Saloma, C. (2001). Solving N-body problems with neural networks. Physical Review Letters, 86(21), 4741. https://doi.org/10.1103/PhysRevLett.86.4741.
  • Monterola, C. P., & Saloma, C. (1998). Characterizing the dynamics of constrained physical systems with an unsupervised neural network. Physical Review E, 57(2), R1247(R). https://doi.org/10.1103/PhysRevE.57.R1247.

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