Bio


J. Sebastian Hernandez-Suarez is a recent PhD graduate in Biosystems Engineering from Michigan State University. He is now a postdoctoral scholar working with Dr. Steven Gorelick in water rights markets modeling in the Upper Colorado River Basin. Originally from Bogota, Colombia, he showed an early interest in humans' relationship with natural resources, especially water. This interest motivated him to obtain a bachelor's degree in Civil Engineering and then a master's in Water Resources Engineering. Before pursuing his PhD, Sebastian worked for the Colombian government in environmental policy-making related to ecological flows and watershed management. His research interests include numerical modeling, artificial intelligence, and multi-objective optimization to support multicriteria decision-making.

Honors & Awards


  • Fitch H. Beach Award for Outstanding Research, Department of Biosystems and Agricultural Engineering, Michigan State University (2020)
  • Outstanding Graduate Student, Department of Biosystems and Agricultural Engineering, Michigan State University (2019)
  • Environmental Science and Policy Program (ESPP) network fellowship, Michigan State University (2018)
  • Merle and Catherine Esmay Scholarship, Department of Biosystems and Agricultural Engineering, Michigan State University (2017, 2018)
  • Fulbright-MinCiencias Scholarship, Fulbright and the Colombian Ministry of Science, Technology and Innovation (2016)
  • Master’s Thesis with “Outstanding” distinction, Academic Council – Universidad Nacional de Colombia (2015)
  • Teaching Assistant Scholarship, Universidad Nacional de Colombia, Bogota (2011, 2012)
  • Young Researchers and Innovators Scholarship, Colombian Ministry of Science, Technology and Innovation, Universidad Nacional de Colombia (2010)
  • Fourth best national score - Colombian Higher Education Quality Test for civil engineering program, Colombian Institute for Education Evaluation (2009)
  • 7 academic periods (out of 10) with exemption of undergraduate tuition fees, Universidad Nacional de Colombia, Bogota (2006-2010)

Professional Education


  • Doctor of Philosophy, Michigan State University (2021)
  • Master of Engineering, Universidad Nacional De Colombia (2015)
  • Bachelor of Engineering, Universidad Nacional De Colombia (2010)

Stanford Advisors


All Publications


  • Harnessing Machine Learning Techniques for Mapping Aquaculture Waterbodies in Bangladesh REMOTE SENSING Ferriby, H., Nejadhashemi, A., Hernandez-Suarez, J., Moore, N., Kpodo, J., Kropp, I., Eeswaran, R., Belton, B., Haque, M. 2021; 13 (23)

    View details for DOI 10.3390/rs13234890

    View details for Web of Science ID 000734638500001

  • Quantification of resilience metrics as affected by conservation agriculture at a watershed scale AGRICULTURE ECOSYSTEMS & ENVIRONMENT Eeswaran, R., Nejadhashemi, A., Kpodo, J., Curtis, Z. K., Adhikari, U., Liao, H., Li, S., Hernandez-Suarez, J., Alves, F., Raschke, A., Jha, P. 2021; 320
  • A novel multi-objective model calibration method for ecohydrological applications ENVIRONMENTAL MODELLING & SOFTWARE Hernandez-Suarez, J., Nejadhashemi, A., Deb, K. 2021; 144
  • Multidimensional Aspects of Sustainable Biofuel Feedstock Production SUSTAINABILITY Raschke, A., Hernandez-Suarez, J., Nejadhashemi, A., Deb, K. 2021; 13 (3)

    View details for DOI 10.3390/su13031424

    View details for Web of Science ID 000615606000001

  • Modeling the persistence of viruses in untreated groundwater SCIENCE OF THE TOTAL ENVIRONMENT Dean, K., Wissler, A., Hernandez-Suarez, J., Nejadhashemi, A., Mitchell, J. 2020; 717: 134599

    Abstract

    Several factors can affect virus behavior and persistence in water sources. Historically linear models have been used to describe persistence over time; however, these models do not consider all of the factors that can affect inactivation kinetics or the observed patterns of decay. Meanwhile, applying the appropriate persistence model is critical for ensuring that decision makers are minimizing human health risk in the event of contamination and exposure to contaminated groundwater. Therefore, to address uncertainty in predictions of decay or virus concentrations over time, this study fit seventeen different linear and nonlinear mathematical models to persistence data from a previously conducted sampling study on drinking water wells throughout the United States. The models were fit using Maximum Likelihood Estimation and the best fitting models were determined by the Bayesian Information Criterion. The purpose of the study was to identify the best model for estimating decay of viruses in groundwater and to determine if model uncertainty contributes to erroneous predictions of viral contamination when only conventional models are considered. For the datasets analyzed in this study, the Juneja and Marks models and the exponential damped model were more representative of the persistence of viruses in groundwater than the traditionally used linear models. The results from this study were then evaluated with classification trees in order to identify more relevant modeling methodology for future research. The classification trees aid in narrowing the scope of appropriate persistence models based on characteristics of the experimental conditions and water sampled.

    View details for DOI 10.1016/j.scitotenv.2019.134599

    View details for Web of Science ID 000519994800082

    View details for PubMedID 31836219

  • Analyzing the Variability of Remote Sensing and Hydrologic Model Evapotranspiration Products in a Watershed in Michigan JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION Herman, M. R., Nejadhashemi, A., Hernandez-Suarez, J., Sadeghi, A. M. 2020; 56 (4): 738-755
  • Evaluation of Multi- and Many-Objective Optimization Techniques to Improve the Performance of a Hydrologic Model Using Evapotranspiration Remote-Sensing Data JOURNAL OF HYDROLOGIC ENGINEERING Herman, M. R., Hernandez-Suarez, J., Nejadhashemi, A., Kropp, I., Sadeghi, A. M. 2020; 25 (4)
  • An investigation of spatial and temporal drinking water quality variation in green residential plumbing BUILDING AND ENVIRONMENT Salehi, M., Odimayomi, T., Ra, K., Ley, C., Julien, R., Nejadhashemi, A., Hernandez-Suarez, J., Mitchell, J., Shah, A. D., Whelton, A. 2020; 169
  • Multi-site watershed model calibration for evaluating best management practice effectiveness in reducing fecal pollution HUMAN AND ECOLOGICAL RISK ASSESSMENT Hernandez-Suarez, J., Woznicki, S. A., Nejadhashemi, A. 2020; 26 (10): 2690-2715
  • A review of macroinvertebrate- and fish-based stream health modelling techniques ECOHYDROLOGY Hernandez-Suarez, J., Nejadhashemi, A. 2018; 11 (8)

    View details for DOI 10.1002/eco.2022

    View details for Web of Science ID 000451861100011

  • Food Footprint as a Measure of Sustainability for Grazing Dairy Farms ENVIRONMENTAL MANAGEMENT Rojas-Downing, M., Nejadhashemi, A., Elahi, B., Cassida, K. A., Daneshvar, F., Hernandez-Suarez, J., Abouali, M., Herman, M. R., Al Masraf, S., Harrigan, T. 2018; 62 (6): 1073-1088

    Abstract

    Livestock productions require significant resources allocation in the form of land, water, energy, air, and capital. Meanwhile, owing to increase in the global demand for livestock products, it is wise to consider sustainable livestock practices. In the past few decades, footprints have emerged as indicators for sustainability assessment. In this study, we are introducing a new footprint measure to assess sustainability of a grazing dairy farm while considering carbon, water, energy, and economic impacts of milk production. To achieve this goal, a representative farm was developed based on grazing dairy practices surveys in the State of Michigan, USA. This information was incorporated into the Integrated Farm System Model (IFSM) to estimate the farm carbon, water, energy, and economic impacts and associated footprints for ten different regions in Michigan. A multi-criterion decision-making method called VIKOR was used to determine the overall impacts of the representative farms. This new measure is called the food footprint. Using this new indicator, the most sustainable milk production level (8618 kg/cow/year) was identified that is 19.4% higher than the average milk production (7215 kg/cow/year) in the area of interest. In addition, the most sustainable pasture composition was identified as 90% tall fescue with 10% white clover. The methodology introduced here can be adopted in other regions to improve sustainability by reducing water, energy, and environmental impacts of grazing dairy farms, while maximizing the farm profit and productions.

    View details for DOI 10.1007/s00267-018-1101-y

    View details for Web of Science ID 000450496000007

    View details for PubMedID 30310973

  • Evaluation of the impacts of hydrologic model calibration methods on predictability of ecologically-relevant hydrologic indices JOURNAL OF HYDROLOGY Hernandez-Suarez, J., Nejadhashemi, A., Kropp, I. M., Abouali, M., Zhang, Z., Deb, K. 2018; 564: 758-772
  • Evaluating the role of evapotranspiration remote sensing data in improving hydrological modeling predictability JOURNAL OF HYDROLOGY Herman, M. R., Nejadhashemi, A., Abouali, M., Hernandez-Suarez, J., Daneshvar, F., Zhang, Z., Anderson, M. C., Sadeghi, A. M., Hain, C. R., Sharifi, A. 2018; 556: 39-49