I am currently a machine learning and analytics engineer working at Occidental Petroleum based in Houston, Texas. My work involves using cutting edge machine- and deep learning techniques to solve problems in subsurface understanding for hydrocarbon exploration, environment monitoring, and sustainability applications.
I hold a PhD degree in Electrical Engineering at the Georgia Institute of Technology with a minor in Maths. My dissertation was completed under the supervision of Professor Ghassan AlRegib at the OLIVES lab. My research focused on developing novel deep learning techniques to address the problem of label sparsity for subsurface characterization. Prior to this, I obtained my Masters degree (also in Electrical Engineering) at the Georgia Institute of Technology.
My research lies at the intersection of machine learning and seismic interpretation for a variety of subsurface applications. I have worked on solving seismic inverse problems through integration of multi-modal subsurface data, developed an active learning technique to reduce dependency on labeled training examples for ML-based interpretation, and worked on modeling human visual attention to account for incomplete, missing training labels to train deep models for subsurface interpretation applications. Additionally, I am also working on understanding the interaction of seismic amplitude anomalies for reduce drilling uncertainty for hydrocarbon exploration using Explainable AI. My research has featured in top-tier peer reviewed academic journals, conference proceedings, and technical meeting presentations. For a fuller list of my research contributions and associated codes, please check out the publications page.
IEEE Transactions of Geoscience and Remote Sensing (TGRS)
Geophysics
Geophysics
Geophysics
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Machine learning-assisted seismic interpretation tasks require large quantities of labeled data annotated by expert interpreters, which is a costly and time-consuming process. Where existing works to minimize dependence on labeled data assume the data annotation process to already be completed, active learning—a field of machine learning—works by selecting the most important training samples for the interpreter to annotate in real time simultaneously with the training of the interpretation model itself, thereby reducing cost and effort to produce annotated training samples while minimizing the negative impact on performance. We develop a unique and first-of-a-kind active learning framework for seismic facies interpretation using the manifold learning properties of deep autoencoders.
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Accurate interpretation of visual data for relevant information forms an important component of many real-world applications such as medical disease diagnosis, geological hazard assessment, hydrocarbon exploration, etc. Producing fine-grained annotations on images is an expensive, laborious, and time-consuming process. The human brain is wired to selectively focus its attention on certain aspects of the visual scene. This perception mechanism is driven both by low-level signal cues, such as changes in color, contrast, intensity, shapes etc., as well as high-level cognitive factors such as one’s prior knowledge, goals, expectations, and constraints with respect to the task at hand.
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The ultimate lightweight Matplotlib-based seismic volume viewer with multi-view support and horizon visualization capabilities.
You are more than welcome to reach out to me with for any questions or advice (if you feel I am qualified to give some based off my experience) on any of my emails below.
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