Curriculum Vitae

Laxman Bokati — Postdoctoral Research Scholar, Arizona State University laxman.bokati@asu.edu · (915) 240-8129 · Google Scholar · ORCID · Scopus · LinkedIn

A printable PDF version is available here.

Education

Ph.D. in Computational Science — The University of Texas at El Paso — 2019–2022 — GPA: 4.00/4.00 Dissertation: “Decision Making Under Uncertainty With Special Emphasis On Geosciences And Education.”

M.S. in Computational Science — The University of Texas at El Paso — 2017–2019 — GPA: 4.00/4.00 Thesis: “Decision Making Under Uncertainty With Applications To Geosciences And Finance.”

B.Sc. in Electrical Engineering — Institute of Engineering, Tribhuvan University, Kathmandu, Nepal — 2008–2012 — GPA: 3.66

Experience

Postdoctoral Research Scholar

Arizona State University, Tempe, AZ — 2022–Present

• Led the integration of diverse multi-decadal geospatial soil datasets, developing unification methods to support comprehensive soil analysis.
• Developed and implemented a temporal-adjustment framework that produces present-day-equivalent values of Soil Organic Carbon (SOC), enhancing the accuracy of harmonized soil profile data.
• Compiled and structured remotely sensed data from multiple sources for advanced geospatial AI/ML modeling.
• Developed and applied AI/ML-driven models for soil health and carbon assimilation potential.
• Created high-resolution continental-scale baseline and attainable SOC maps and developed downscaling techniques.
• Led the Attainable-ET project, designing farm-scale evapotranspiration benchmarks using remote sensing, crop yield classification, and AI/ML models to optimize agricultural water use.
• Modeled dynamic streamflow forecasting for Arizona streams, integrating conventional forecast models with AI-driven approaches to capture forest fires, extreme heat, and other rapid hydrologic drivers.
• Partnered with researchers at Jackson State University to assess heatwave impacts and enhance climate resilience in highly populated U.S. cities.
• Developed research proposals, secured project funding, and presented findings at AGU, ASA-CSSA-SSSA, EWRI, and CMWR.
• Assisted with graduate courses in Remote Sensing and Remote Sensing for Water Resources.
• Mentored graduate and undergraduate students on ML, data science, and geospatial modeling.

Teaching Assistant — Computational Science Program & Department of Computer Science

The University of Texas at El Paso, El Paso, TX — 2017–2022

• Supported preparation, teaching, and grading for: Calculus I–III, Matrix Algebra, Introduction to Analysis, Numerical Analysis, Discrete Mathematics, Elementary Statistical Methods, Automata, Computability and Formal Languages, Multivariate Data Analysis, Theory of Computation.
• Supported faculty in laboratory sessions and undergraduate research projects.
• Tutored at the Math Resource Center for Students.

Project Engineer

Aastha Engineering Solution Pvt. Ltd., Kathmandu, Nepal — 2013–2016

• Conducted feasibility studies and site identification for wind–solar hybrid projects (Alternative Energy Promotion Centre, Nepal).
• Performed an extensive study on carbon credit potential in Nepalese industries (Ministry of Industry, Nepal).
• Promoted eco-friendly technologies including the “Matribhumi Improved Cooking Stove” and “Low-Energy Solar Water Pump.”
• Managed design and implementation of “Aashrya” temporary shelters in earthquake-affected districts.
• Prepared proposals, managed resources, and authored reports.

Selected publications

• Bokati L., Somenahally A.C., Kumar S., Perepi R., Sarkar R., Talchabhadel R., Robatjazi J. — “Temporal adjustment approach for high-resolution continental-scale modeling of soil organic carbon” — Scientific Reports, 2025, Vol. 15, Art. 6483.
• Bokati L., Kumar S., Somenahally A.C. — “Soil Carbon Modeling at Crossroads: Building Reliable Methods for Policy and Practice” — European Journal of Soil Science, 2026, Vol. 77, No. 2, Art. e70295.
• Somenahally A.C., Bokati L., Kumar S. — “Estimating soil organic carbon deficits at the continental scale using legacy-data-driven dynamic baseline and attainable projections” — Geoderma, 2025, Vol. 462, Art. 117515.
• Bhattarai S., Bokati L., Sharma S., Talchabhadel R. — “Understanding spatiotemporal variation of heatwave projections across US cities” — Scientific Reports, 2025, Vol. 15, Art. 10643.
• Dahal K., Gupta A., Bokati L., Kumar S. — “Ensemble Streamflow Forecasting With Diverse Loss Functions” — Applied Soft Computing, 2026, Vol. 198, Art. 115276.
• Thai S., Somenahally A., Robatjazi J., Bokati L., Talchabhadel R., Kumar S. — “Mapping Soil pH Baselines, Trends, and Time-to-Critical Crop Risk across CONUS Using Harmonized Legacy Datasets: A Large-Scale Assessment” — Journal of Environmental Management, 2026, Vol. 404, Art. 129508.

Full list with descriptions on the Publications page.

Grants

• PI — Identify “Attainable-ET” Benchmarks for Arizona Agriculture. Arizona Water Innovation Initiative, 2025.
• Co-PI — Dynamic Streamflow Forecasting for Arizona Streams. Arizona Water Innovation Initiative, 2024.
• Travel Grant — AGU Chapman Conference: Remote Sensing of the Water Cycle, Honolulu, HI, 2024.

Awards

Academic and Research Excellence

Precommencement Awards, College of Science, UTEP

2022

Outstanding Paper Award

NAFIPS 2022, Halifax, Nova Scotia, Canada — for “How to elicit complex-valued fuzzy degrees”

2022

Best Student Paper Award — Interval Session

NAFIPS 2021 — for “How Much For a Set: General Case of Decision Making Under Set-Valued Uncertainty”

2021

Selected presentations

• Identifying Attainable-ET Benchmarks to Guide Irrigation Water Optimization in Arizona Agriculture — 2026 Watershed Management Conference, 2026 (accepted).
• Overlooked Biases in Machine-Learning Soil-Carbon Maps: Depth Autocorrelation, Circular Density Logic, and Validation Gaps — AGU 2025, New Orleans, LA, Dec 2025.
• Using Convolutional Neural Networks and Transfer Learning for Soil Organic Carbon and Plant Available Water Modeling in Data-Sparse Regions — EWRI 2025, Anchorage, AK, May 2025.
• Predicting Soil Organic Carbon and Essential Soil Parameters Using Remote Sensing: Implications on Regenerative Agriculture and Food Security — AGU Chapman Conference, Honolulu, HI, Feb 2024.
• Advancing Scalable Spatial and Temporal Predictions of Soil Organic Carbon — ASA-CSSA-SSSA International Annual Meeting, St. Louis, MO, Oct–Nov 2023.

Full list of presentations is in the downloadable PDF CV.

Skills

• Programming: Python, R, MATLAB, C.
• AI/ML & Deep Learning: PyTorch, TensorFlow/Keras, scikit-learn, XGBoost, CatBoost; LSTM, CNN, Temporal Fusion Transformer, transfer-learning architectures applied to soil, hydrology, and climate.
• Geospatial & Remote Sensing: Google Earth Engine, ArcGIS Pro, ENVI; GDAL, Rasterio, GeoPandas; continental-scale remote-sensing integration and downscaling.
• Workflow & Reproducibility: Git/GitHub, Conda/virtualenv, Docker, Jupyter/Google Colab, ASU Sol HPC cluster.
• Technical Writing: Articles, proposals, reports, posters, presentations; LaTeX, MS Office.
• Languages: English (professional), Hindi (professional), Nepali (native).