Kidist Demessie (PhD Candidate)

Professional Summary

Kidist Demessie is an Ethiopian hydraulic engineer, hydroclimatologist, lecturer, data scientist and AI/ML water-resources specialist. Her academic foundation includes a BSc in Soil and Water Engineering, an MSc in Hydraulic Engineering, and a PhD in Surface Water Resource Engineering and Management. She brings more than 10 years of academic and professional experience, including university teaching at Haramaya University, hydroclimatic research, water-resource modelling, geospatial analysis, machine-learning model development and international remote AI research work with Ready Tensor, Inc. Her technical profile is exceptionally strong for the future of African water science because she applies machine learning, time-series analysis, satellite data, Python, GIS, AI tools and statistical modelling to water and climate problems such as rainfall analysis, evaporation prediction, reservoir water loss, groundwater potential mapping, aridity assessment, evapotranspiration, malaria outbreak prediction and data-driven decision support. She represents a powerful new generation of African women experts combining hydraulic engineering, hydroclimatology, artificial intelligence, machine learning and geospatial water intelligence.

Key Expertise

Major Projects and Contributions

• Lecturer and Hydroclimatology Researcher – Haramaya University
  Serves as Lecturer at Haramaya University, designing and delivering course content, mentoring students, contributing to academic research, supporting curriculum development and strengthening water, climate, hydrology, hydraulic engineering and engineering education.
• Research Assistant – Ready Tensor Time-Series Benchmarking Project
  Works remotely with Ready Tensor, Inc. on time-series machine-learning benchmarking, executing experimental workflows on AWS EC2 infrastructure, organizing experimental results, supporting statistical analysis, research writing and collaboration with data scientists and engineers.
• Large-Scale Machine Learning Benchmarking Across 132 Datasets
  Managed and consolidated results from more than 100 experiments involving over 50 machine-learning models across 132 datasets, supporting improved predictive accuracy, reproducible workflows and AI research documentation.
• AI/ML Outreach and Community Engagement – Ready Tensor
  Supports dissemination of AI/ML publications, competitions and certification activities, helping communicate AI and machine-learning goals to large professional audiences and supporting community-building on ReadyTensor.ai.
• Interpretable Machine Learning for Reservoir Evaporation Prediction
  Developed and evaluated interpretable machine-learning models including Random Forest, Gradient Boosting, Decision Tree, KNN and XGBoost to predict daily lake evaporation from reservoirs in the Awash River Basin, Ethiopia.
• Remote Sensing and Machine Learning for Reservoir Evaporation Estimation
  Contributed to research estimating reservoir evaporation by fusing remotely sensed solar radiation and temperature features with machine-learning algorithms, supporting improved water-loss assessment and reservoir management.
• Groundwater Potential Zone Mapping in Gambela Plain
  Contributed to research applying advanced machine-learning algorithms and geospatial techniques for groundwater potential zone mapping in Gambela Plain, Ethiopia, supporting groundwater exploration and water-resource planning.
• CMIP6 Climate Modelling, Aridity Indices and Potential Evapotranspiration
  Contributed to the characterization of aridity indices and potential evapotranspiration using CMIP6 global climate models in two distinct regions of Ethiopia, strengthening climate-impact and hydroclimatic assessment.
• Predicting Future Malaria Outbreaks Using Interpretable Machine Learning
  Developed and contributed to machine-learning analysis for predicting future malaria outbreaks using recent malaria data collected after construction of an irrigation dam, linking public health, irrigation development, climate and interpretable AI.
• Amharic Named Entity Recognition and LLM Fine-Tuning for E-Commerce Data
  Developed a centralized e-commerce data project for Ethiopia by fine-tuning LLMs for Amharic Named Entity Recognition in Telegram channels, extracting product names, prices and locations for real-time business intelligence.
• GFM Investment Portfolio Time-Series Forecasting Project
  Developed time-series forecasting models using historical financial data to predict market trends, optimize asset allocation and support data-driven investment portfolio management.
• Academic Leadership – Associate Director for Undergraduate Program
  Served as Associate Director for the undergraduate program at Haramaya University, coordinating operations across 10 departments and supporting curriculum delivery, academic quality, student success and program development.
• Peer-Reviewed Publications in Water, Climate, Groundwater and AI
  Published and contributed to peer-reviewed research on interpretable machine learning for evaporation prediction, groundwater potential mapping, aridity indices, potential evapotranspiration, reservoir evaporation and malaria dynamics using interpretable machine learning.
• Journal Peer Review and Global Disaster Risk Contribution
  Contributed to scientific review and knowledge-building, including work with UNDRR/ISC Hazard Information Profiles and peer-review service for journals in computer science, climatology, atmospheric science and hydrology.
• AI, Data, Web and Project Management Certification
  Completed professional certifications in artificial intelligence fundamentals, data fundamentals, web development fundamentals and project management fundamentals, strengthening her technical and interdisciplinary profile.