CV

Education, experience, and skills

Geophysicist and SciML researcher bridging theoretical deep learning with practical geophysical applications. 5+ years of industry R&D leadership and 4+ years of graduate research.

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Education

PhD in Geophysics

King Fahd University of Petroleum & Minerals · Saudi Arabia · Expected September 2026
  • GPA: 3.9/4.0
  • Thesis: “Towards real-time microseismic monitoring using deep learning”
  • Advisor: Dr. Umair Bin Waheed
  • Key coursework: Deep Learning, Machine Learning, Wave Propagation, Signal Analysis

Master of Science in Geosciences

Colorado State University · USA · August 2012
  • GPA: 4.0/4.0
  • Specialization: Applied Geophysics
  • Thesis: “Geophysical constraints on the flexural subsidence of the Denver Basin”
  • Advisor: Dr. Dennis Harry

Bachelor of Science in Geophysics

Kazan Federal University · Russia · June 2010
  • GPA: 4.9/5.0
  • Specialization: Well-Log Data Analysis
  • Thesis: “Application of well-log data analysis for assessment of porosity types of a carbonate reservoir”
  • Advisor: Dr. Victor Kosarev

Professional experience

Graduate Research Assistant

Smart Earth Exploration and Monitoring Research Group, KFUPM · Saudi Arabia · August 2022 – Present
  • Methodological innovation: Developed a novel Diffusion model framework for joint parameter inversion, incorporating uncertainty quantification for highly ill-posed geophysical problems
  • Algorithm efficiency & generalization: Engineered lightweight Fourier Neural Operators (FNO) for real-time edge computing, extensively validating generalizability across out-of-distribution benchmark datasets (e.g., OpenFWI, Marmousi)
  • Academic collaboration: Collaborated cross-functionally to integrate advanced deep learning architectures into traditional seismic workflows, resulting in multiple high-impact journal submissions

Lead Expert

Department of Oil and Gas Field Development and Exploration, TATNEFT · Russia · July 2017 – July 2022
  • R&D leadership & mentorship: Managed and mentored a cross-functional team of 8 engineers, leading end-to-end technical projects from conceptualization to field deployment
  • Commercial impact: Successfully screened and secured internal funding for 12 R&D projects with an aggregate Expected Monetary Value (EMV) exceeding $280M
  • Technology deployment: Directed the field piloting of new technologies for difficult-to-recover reserves, ensuring alignment with corporate efficiency and sustainability targets

Graduate Research Assistant

Crustal Deformation and InSAR Research Group, KAUST · Saudi Arabia · September 2012 – June 2017
  • Data assimilation & digital twins: Integrated satellite interferometry data using Ensemble Kalman Filters (EnKF)
  • HPC environment: Utilized supercomputing resources for large-scale geophysical modeling

Technical skills

Scientific Deep Learning

Generative Models (DDPM, I2SB), Neural Operators (FNO, GNO), Explainable AI (SHAP, GradCAM), Physics-Informed Machine Learning

Signal Processing & Inverse Theory

Ill-posed inverse problems, Data Assimilation (EnKF), High-fidelity data compression, Time-series analysis, FFT

Computing & Software

PyTorch, CUDA Optimization, HPC environments, Python (NumPy, SciPy, ObsPy), Linux/Unix

Training & certifications

  • Summer School of Machine Learning by Skoltech (2025)
  • Scientific Machine Learning for the E&P (2025)
  • AWS Educate Machine Learning Foundations (2025)
  • SLB AI Academy (2024)
  • Geothermal Energy Fundamentals (2024)