Curriculum Vitae
Dr. Muhammad Amar Gul
Applied AI/ML Research Scientist · Mineral Discovery & GeoAI · Geochemical Intelligence
Jeddah, Saudi Arabia · amar_geologist@yahoo.com
GitHub · Google Scholar · LinkedIn · AIMEX Lab
Professional summary
PhD-trained geoscientist and applied AI/ML researcher with 10+ years in geoscience and 5+ years of research and development applying data science and machine learning to mineral-exploration problems. My work spans mineral-system discrimination, geochemical anomaly intelligence, prospectivity, remote sensing, geostatistics, spatial uncertainty, scientific software, and exploration decision support.
I combine domain geology with explainable AI, grouped/spatial validation, uncertainty analysis, reproducible software development, and operational exploration workflows. My publication record includes 20+ peer-reviewed papers across mineral geochemistry, GeoAI, economic geology, sedimentary geochemistry, tectonics, and subsurface geoscience.
Current professional experience
Senior Geoscientist (Data & AI) — Erity Pty Ltd.
Research-to-product GeoAI: design and develop in-house exploration systems spanning GeoAnomalyAI, target detection, remote-sensing/spectral workflows, deposit-type interpretation, automated QA, uncertainty products, and decision support.
Anomaly intelligence: develop multivariate geochemical workflows using correlation structure, outlier diagnostics, clustering, manifold learning, anomaly scoring, spatial confidence, and target ranking.
Exploration delivery: support Mauritania, Tanzania, and Saudi Arabia programs through AI-assisted interpretation, target-confidence assessment, map QA, automation, geostatistical outputs, and field-ready deliverables.
Project Geologist (AI & ML) — China National Geological & Mining Corporation
Arabian Shield targeting: lead and support mineral-potential and prospectivity workflows integrating geology, geochemistry, geophysics, structures, lithology, spectral remote sensing, GIS, and 3D geological interpretation.
Exploration intelligence: support technical screening, prospect ranking, licensing evaluation, maps, reports, and management-ready decision outputs.
Data systems: contribute to geological data modelling, ETL, metadata standards, dashboards, and reproducible technical reporting.
Research experience
Research Geoscientist — Geochemical AI
Developed global pyrite, galena, and sphalerite machine-learning studies for deposit classification, tectonic-setting discrimination, ore-genesis interpretation, and critical-metal assessment using LA-ICP-MS mineral chemistry, S-Pb isotopes, global deposit databases, explainable AI, imbalance treatment, and grouped validation.
Research & innovation leadership
Founder & Director — AIMEX Lab
Founded AIMEX Lab as an independent research initiative for artificial intelligence in mineral exploration, reproducible benchmarking, collaborative GeoAI research, and scientifically defensible exploration methodology.
Selected research impact
- Global pyrite metallogenic typing: ~5,200 analyses from 138 global deposits/settings spanning major ore-system classes, with deposit-scale validation and interpretable machine learning.
- Galena metallogenic AI: 37 Pb-Zn deposits evaluated with Random Forest, Gradient Boosting, MLP, and SVM workflows, including imbalance handling and high-dimensional visualization.
- Gunga Pb-Zn research: pyrite and sphalerite geochemistry integrated with S-Pb isotopes, global comparison datasets, machine/deep learning, ore-genesis interpretation, and critical-metal assessment.
- Sedimentary pyrite tectonic-setting research: 43 global locations evaluated using CNN/XGBoost and interpretable high-dimensional analysis; manuscript currently under revision.
- Critical-metal research: contribution to research describing the first reported Ge-rich deposit in Pakistan.
Selected GeoAI systems & computational R&D
- GeoAnomalyAI — multivariate anomaly intelligence, correlation structure, clustering, anomaly scoring, spatial validation, threshold sensitivity, and target ranking.
- GeoSpectraAI — multimodal remote-sensing/spectral and geoscience evidence integration for prospectivity screening and confidence assessment.
- DepositTypeAI / Sulfide ML applications — pyrite, galena, and sphalerite research applications for deposit-type classification, metallogenic typing, tectonic-setting prediction, and critical-metal interpretation.
- Target-detection workflows — geochemical evidence, target ranking, uncertainty products, geostatistical outputs, map QA, batch execution, and field-ready delivery.
- Geostatistical / GPU R&D — variography, kriging, Python SGSIM, spatial simulation, uncertainty analysis, and CUDA/GPU experimentation.
Selected exploration applications
Saudi Arabia — Arabian Shield
Mineral-potential modelling, Round 9/10/11-style technical screening, target ranking, and integrated geological, geochemical, geophysical, structural, spectral, and remote-sensing interpretation.
Mauritania — Sahara Gold
AI-assisted gold targeting using soil geochemistry, remote sensing, geological interpretation, anomaly detection, target confidence, trench/drill evidence, geostatistical products, and report automation.
Tanzania — Mpanda-Mbozi-Karema
Multi-element QA/QC, sample-type interpretation, detection-limit and outlier review, spatial-pattern analysis, anomaly confidence, and target-consistency checks.
Technical capabilities
AI / machine learning
Python, scikit-learn, XGBoost, SVM, Random Forest, gradient boosting, MLP, CNN, PyTorch/CUDA experimentation, SHAP, LIME, PCA, t-SNE, UMAP, Isolation Forest, clustering, SMOTE, undersampling, grouped/LOGO validation, K-fold CV.
Data & research engineering
Streamlit, Gradio, FastAPI/React-style prototypes, ETL, workflow automation, dashboards, RAG/LLM-assisted reporting, evidence inventories, QA gates, batch runners, Git/GitHub, reproducible delivery pipelines.
Geoscience & spatial
Economic geology, LA-ICP-MS, sulfide mineral chemistry, S-Pb isotopes, lithogeochemistry, pathfinders, critical metals, mineral prospectivity mapping, ArcGIS Pro, QGIS, Leapfrog, ioGAS, ENVI, remote sensing, variography, kriging, SGSIM, uncertainty modelling, 3D geological interpretation.
Education
- PhD, Geology — University of Science and Technology of China (USTC), 2024. Research focus: geochemical big-data analytics, machine learning, mineral geochemistry, metallogenic discrimination, ore genesis, and critical metals.
- MS, Geological Sciences, 2013–2016.
- BS (Honours), Geology, 2008–2012.
Selected peer-reviewed research
See the Publications page for current publication status and DOI links. Selected recent outputs include:
- global pyrite metallogenic typing — Journal of Geochemical Exploration (2026);
- galena geochemistry and machine learning — Mathematical Geosciences (2026);
- Gunga sphalerite deep learning and critical-metal enrichment — Journal of Geochemical Exploration (2025);
- Gunga pyrite machine learning and isotopic signatures — Journal of Geochemical Exploration (2025).
Public research repositories
- Pyrite AI metallogenic typing
- Galena geochemistry & ML
- Gunga pyrite machine learning
- Gunga sphalerite deep learning
Languages
English (Fluent) · Urdu (Native) · Chinese (Basic) · Arabic (Basic)