About

I am a data scientist and Ph.D. candidate in Computing (Data Science) at Boise State University, specializing in artificial intelligence (AI), machine learning, high-performance computing (HPC), hydrology, and climate data science. My research develops scalable AI and deep learning models for computer vision and Earth system simulations, with a focus on uncertainty quantification and efficient emulation of physics models like CLM5. I prioritize interoperability, ensuring that tools, data, and models integrate smoothly across disciplines. I build reproducible workflows and modular machine learning surrogates that support open, collaborative science. Driven by real-world impact, my work bridges the fields of AI and environmental research to address challenges in climate resilience and water resource management.

Research Interests

• Machine Learning Emulators for Climate Models
  I develop deep learning surrogates (e.g., CNNs, LSTMs) to emulate complex climate simulations such as CLM5. These models significantly reduce computational costs while maintaining physical fidelity, enabling faster and scalable climate scenario analysis.

• Uncertainty Quantification & Interpretability
  I apply evidential deep learning to quantify prediction uncertainty in climate and hydrological models. Tools like SHAP and Sobol indices help ensure that model outputs are transparent, interpretable, and scientifically robust.

• Geospatial Pattern Analysis
  My work involves identifying and analyzing spatial climate patterns using machine learning and statistical techniques. I implement scalable workflows using Self-Organizing Maps (SOMs) and Empirical Orthogonal Functions (EOFs) in Python/Dask to detect anomalies and validate climate models.

• High-Performance Computing (HPC) & Big Data
  I optimize environmental data workflows on multi-node GPU-accelerated clusters using tools like Dask, Xarray, Docker, and Singularity. This includes efficient job scheduling, parallel processing, and large-scale data engineering for climate and Earth system modeling.

• Hybrid Climate Modeling with AI
  I explore integrated modeling approaches that combine physics-based climate models (e.g., CLM5, WRF) with machine learning. This hybrid methodology improves prediction reliability and supports actionable insights for climate mitigation and decision-making.

Technical Skills

• Programming & Scripting
  Python (NumPy, Pandas, SciPy, Xarray), R, MATLAB, Java, Scala, PySpark, Bash, SQL

• Machine Learning & AI
  PyTorch, TensorFlow, Keras, scikit-learn; experience with deep learning (CNNs, RNNs), generative AI, and large language models

• Big Data & HPC
  Dask, Apache Spark, Hadoop; GPU-accelerated computing, Slurm-based job scheduling, parallel processing, memory optimization; cloud platforms including AWS and Google Cloud

• Geospatial & Climate Tools
  GIS software, Xarray & Dask for multi-dimensional arrays, NetCDF/Zarr formats, climate modeling tools like WRF and CLM5, and operators like CDO and NCO

• DevOps & Data Engineering
  Docker, Singularity, CI/CD pipelines, Git/GitHub for version control, Linux shell scripting for automation, and reproducible environments

• Data Visualization
  Matplotlib, Cartopy, HoloViews, Plotly, Tableau; experienced in creating insightful and publication-ready graphics for complex scientific data

Education

• Ph.D. in Computing (Data Science)
  Boise State University, Boise, ID, USA — Expected Fall 2025

• M.Sc. in Mathematical Sciences (Climate Data Science)
  African Institute for Mathematical Sciences (AIMS), Kigali, Rwanda — 2020–2021

• B.Sc. in Physics
  Copperbelt University, Kitwe, Zambia — 2015–2019

Recent Updates

• June 2025
  Awarded the AWWA Pacific Northwest Section Scholarship for graduate research excellence in water and climate data science, recognizing leadership in water resource analytics.

• Summer 2025
  Received a Travel Award to attend the NCAR CESM Tutorial in Boulder, CO, for advanced training in Earth system modeling and simulation.

• September 2024
  Selected as a William Averette Anderson Fund Fellow, with a focus on hazard mitigation and disaster resilience. Participated in national workshops on community-engaged research.

• July 2024
  Presented machine learning research on climate pattern analysis at the SIAM Annual Meeting and SIAM Mathematics of Data Science (MDS) conference, supported by SIAM Travel Awards.

• June 2024
  Completed a Graduate Research Fellowship at NCAR Advanced Study Program, developing adaptive learning techniques for climate model calibration and evaluation.

Awards & Fellowships

• 2025
  • AWWA Pacific Northwest Scholarship Recipient
  • NCAR CESM Tutorial Travel Award – Funding for advanced CESM model training
• 2024
  • William Averette Anderson Fund Fellow – National fellowship in hazard & disaster mitigation
  • NCAR ASP Graduate Research Fellow – NSF-supported climate research residency
  • SIAM Travel Awards (AN24, MDS24) – For presentations at major applied mathematics/data science conferences
• 2021
  • Boise State GEM Scholarship – Merit-based graduate award
  • Graduate Assistantship – Research funding from Boise State University
  • AIMS Master’s Scholarship (Mastercard Foundation) – Full scholarship for climate-focused graduate studies
• 2015
  • Zambia National Merit Scholarship – Full undergraduate scholarship based on academic excellence at Copperbelt University