Hello! I am an AI Software Engineering Manager. I enjoy building cutting-edge generative AI, Large Language Model (LLM), and computer vision solutions across multiple industries. I have experience in AI code development, cybersecurity, and the energy industry. You can find samples of my work here through the links I provide to videos, code, and articles.

• YouTube and Recorded Content - Here are some examples of my recorded technical talks, covering topics like distributed fine-tuning of LLMs in the cloud, using neural networks and PyTorch for computer vision tasks, and Kubeflow pipelines.
• Medium Articles - Here are some of my published articles on Medium, covering topics like fine-tuning LLMs, automatic speech recognition (ASR), stable diffusion, quantization, computer vision, and PyTorch.
• Conference Talks - Samples of my speaking engagements at technical conferences.
• Publications - A list of my formal research publications, primarily composed of my work in deep learning and subsurface imaging.
• Hugging Face Contributions - Some of the model cards and other materials I have contributed to Hugging Face.
• Kaggle Contributions - Here are a few of my Python notebooks I have published on Kaggle, including one detailing the hardware available on the platform.
• GitHub Activity - A sample of my direct contributions to the GitHub open-source community.
• Podcasts - Podcasts where I was a guest speaker.
• Work Experience - My journey in work has mostly been focused around AI and geophysics.
• Education - Starting from a strong foundation of mathematics, I moved into teaching and then a thesis-based geophysics degree.
• Contact - How to reach me.

Here are some examples of my recorded technical talks, covering topics like distributed fine-tuning of LLMs in the cloud, using neural networks and PyTorch for computer vision tasks, Kubernetes, and Kubeflow.

Video Link	Description
How to Set Up Cloud-Based Distributed Training to Fine-Tune an LLM	Learn how to fine-tune nanoGPT (124M parameter) model on a cluster of CPUs on Google Cloud Platform. The model is trained on the OpenWebText dataset in a distributed setting, using 4th Gen. Intel® Xeon® Scalable CPUs. The project builds upon the initial codebase of nanoGPT, by Andrej Karpathy. The objective is to understand how to set up distributed training so that you can fine-tune to your specific objective. The end result of training here will result in a base LLM that can generate words, or tokens, but it will only be suitable for your use-case when you fine-tune it on your specific task and dataset.
Seismic Data to Subsurface Models with OpenFWI: Training an AI Model with PyTorch	Obtaining an accurate “picture” of the subsurface is not as simple as snapping a picture on a smartphone. Seismic exploration is a key component in creating images of the subsurface and finding essential minerals in the subsurface. Building images of the subsurface is akin to ultrasound technology used to image the human body. Learn how to train a neural network with PyTorch on a CPU, going directly from seismic data to a subsurface model.
Find the Humor in Text Data: NLP with Intel & Habana*	Learn how to train a binary classification natural language processing (NLP) model on a humor dataset, where each statement is labeled as humorous or not humorous. The training is performed on a powerful Intel Gaudi GPU. Also learn how to quantize a model to speed up inference by 1.8x, taking it from FP32 format to INT8 format without significant accuracy loss.
CPU accelerated fine-tuning for image segmentation using PyTorch	Fine-tuning neural networks has historically been quite slow and cumbersome on CPUs. However, with mixed precision BF16 training, the Intel Extension for PyTorch has made fine-tuning training feasible on a CPU and perhaps even preferred where cost and availability are key factors. In this tutorial, I will walk you through a real-world example of training an AI image segmentation model using PyTorch 1.13.1 (with ResNet34 architecture); the model will learn to identify roads and speed limits only from satellite images.
Underground Salt Domes! Fun With Deep Learning	Interpreting salt bodies in the subsurface is a challenging manual task that can take weeks to complete. Obtaining accurate picks of salt is very important, because errors in the placement of salt can result in severe degradation of the seismic image. The U-Net architecture proved robust with the placement of salt at 98% accuracy. Beyond accuracy, U-Net also proved to be the fastest, requiring only 3.5 hours to predict salt on the 3D seismic volume. The results presented here along with other recent studies of deep learning for salt interpretation represent a clear shift in the seismic interpretation workflow.
Physics and Deep Learning for First Breaks	Microseismic monitoring is a crucial element to understanding hydraulic fracturing operations prior to oil and gas production. One of the more tedious quality control (QC) measures that must often be performed following a microseismic processing workflow is a visual inspection of seismic data to determine whether the data contain microseismic events or only noise. We propose using a supervised deep learning algorithm, a convolutional neural network (CNN), to automatically classify microseismic events from noise. Using our deep learning approach, we show that the time for QC can be reduced from weeks to hours with high accuracy.
XGBoost* Kubeflow* Pipeline: Intel® Cloud Optimization Modules for Microsoft	Learn how to build secure, scalable, and accelerated XGBoost pipelines on an Azure Kubernetes service cluster, leveraging Intel SGX. This tutorial walks you through the process from setting up the container to building the full Kubeflow Pipeline using an example application. Access the full source code on GitHub.

Here are some of my published articles on Medium, covering topics like fine-tuning LLMs, automatic speech recognition (ASR), stable diffusion, quantization, computer vision, and PyTorch.

Samples of my speaking engagements at technical conferences.

A list of my formal research publications, primarily composed of my work in deep learning and subsurface imaging.

Some of the model cards, articles, and spaces I have contributed to Hugging Face.

Here are a few of my Python notebooks I have published on Kaggle, including one detailing the hardware available on the platform.

A sample of my direct contributions to the GitHub open-source community.

My journey in work has mostly been focused around AI and geophysics. For more detail, please visit my LinkedIn profile.

Starting from a strong foundation of mathematics, I moved into teaching and then a thesis-based geophysics degree.

How to reach me.

If you would like to connect with me to collaborate or to ask me questions, feel free to reach out to me over LinkedIn.