I am a dual Ph.D. candidate in Computer Science and Biosystems Engineering at Michigan State University. I am fortunate to be advised by Dr. Pouyan Nejadhashemi and Dr. Parisa Kordjamshidi.
My primary research is multidisciplinary and sits at the intersection of Natural Language Processing (NLP) and Process-based Crop Modeling. I design and develop neurosymbolic systems that combine domain-specific language models with deterministic crop and water models to address complex agricultural water-management (AWM) challenges in agricultural Extension.
In the Computational Ecohydrology Group at MSU, I am currently leading efforts in developing (1) the DSSAT-LM platform for the Smart Extension Agent (SEA) project and (2) benchmarking state-of-the-art LLMs across several AWM tasks via AWM-Bench.
I also specialize in developing decision-making software, both independently and for research groups, including CECO’s Decision Support & Informatics Lab, where I am a Research Assistant.
Michigan State University, East Lansing, MI, USA
Abdalaal, Y., Ghane, E., Kpodo, J., Nejadhashemi, A. P., Youssef, M. A., Falasy, A., Askar, M., Katuwal, S., Johnson, G. M., Rogovska, N., and Isenhart, T. M. Development and testing of a DRAINMOD-based decision-support tool for designing and evaluating saturated buffers. In: Agricultural Water Management 326 (2026), p. 110201. DOI
Kpodo, J., Nejadhashemi, A. P., and Eeswaran, R. Evaluating the logical and mathematical reasoning capabilities of language models in agricultural water management. Computers and Electronics in Agriculture 326 (2026), p. 110201. DOI
Kpodo, J., & Nejadhashemi, A. P. (2025). Navigating challenges/opportunities in developing smart agricultural extension platforms: Multi-media data mining techniques. Artificial Intelligence in Agriculture. DOI.
Razavi, M. A., Nejadhashemi, A. P., Majidi, B., Razavi, H. S., Kpodo, J., Eeswaran, R., Ciampitti, I., & Prasad, P. V. V. (2024). Enhancing crop yield prediction in Senegal using advanced machine learning techniques and synthetic data. Artificial Intelligence in Agriculture, 14, 99–114. DOI
Banda, E., Rafiei, V., Kpodo, J., Nejadhashemi, A. P., Singh, G., Das, N. N., Kc, R., & Diallo, A. (2024). Millet yield estimations in Senegal: Unveiling the power of regional water stress analysis and advanced predictive modeling. Agricultural Water Management, 291. DOI.
Ferriby, H., Nejadhashemi, A.P., Hernandez-Suarez, J.S., Moore, N., Kpodo, J., Kropp, I., Eeswaran, R., Belton, B. and Haque, M.M., 2021. Harnessing Machine Learning Techniques for Mapping Aquaculture Waterbodies in Bangladesh. Remote Sensing, 13(23), p.4890.