Relative Geological Time Extraction
Deep learning system for extraction of Relative Geological Time representations from volumetric seismic data.
AI Systems for Volumetric Scientific Data
Applied AI for seismic, spatial and scientific data.
Deep learning, scientific computing and large-scale volumetric processing.
AI Engineer / Applied Scientist with a PhD in Computer Science specializing in deep learning for large-scale volumetric and spatial datasets.
Experienced in developing machine learning systems for seismic interpretation, structural segmentation, geological representation learning, and high-performance scientific computing.
Technical Capabilities
Specialized in machine learning systems for scientific and spatial data processing, combining research-oriented algorithm development with production software engineering.
Selected Technical Work
Fault Segmentation
3D deep learning system for automatic fault detection and structural segmentation in volumetric seismic data. Deployment included ONNX-based inference and integration into a CUDA-accelerated C# application.
Synthetic Data & Validation
Development of synthetic geological data generation methods used for validation, controlled experiments, and robustness analysis of deep learning models operating on seismic volumes.
The video presents Relative Geological Time extraction on a synthetic validation dataset generated for controlled experiments and model evaluation.
Angular Unconformity Detection
Experimental deep learning approach for detection of angular unconformities in volumetric seismic data. Focused on identification of large-scale structural discontinuities in real seismic datasets.
Open Source & Research
Implementation of probabilistic sequence models from scratch, including Hidden Markov Models, Baum–Welch training, Gaussian and Gaussian Mixture emissions, and continuous observation distributions. View Project.
PhD research focused on local learning algorithms and geometric partitioning methods for high-dimensional feature spaces, including implementation of the proposed learning framework and algorithms in C#.
Collaboration & Contact
Interested in technically challenging contract-based collaborations involving scientific machine learning, volumetric and spatial data processing, geospatial systems, earth observation, satellite imagery analysis, and high-performance scientific computing.
Primarily interested in remote contract work with flexible collaboration models.
Email, GitHub, and LinkedIn links are available in the sidebar.