Publications, Presentations, & Technical Papers

Machine learning (ML) continues to reshape business, technology, science, and research across all industries, with its adoption enabling systems to learn from data, automate decisions, and generate insights. This paper presents a structured roadmap through three core domains of machine learning that are increasingly adopted by organizations: supervised, unsupervised, and generative learning. Along this roadmap, readers will identify key algorithms and architectures within each domain and understand the role of parameters and hyperparameters in mitigating overfitting and underfitting. The discussion includes examples of predictive modeling on labeled data using supervised algorithms, knowledge discovery from unlabeled data using unsupervised algorithms, and the extension of these capabilities through generative learning, which enables systems to extract insights and produce new content or data representations. The paper concludes by introducing three generative architectures that define the state of AI in 2025: encoder models (BERT), decoder models (LLMs), and encoder-decoder models (T5), and describes how each supports advanced AI tasks including representation learning, language generation, natural language processing (NLP), text summarization, and translation.

Machine learning is experiencing a golden age of investment, democratization, and accessibility across all domains in the life sciences, natural sciences, and social sciences with applications to industry for business decision-making, risk management, consumer marketing, clinical trials, financial forecasting, security recognition, video remastering, digital twin simulations, and more. But what exactly is machine learning (ML)? How is it connected to artificial intelligence (AI)? And most importantly, how can data scientists, programmers, software engineers, and/or researchers start their endeavors into machine learning? This paper answers these questions, and more, by providing a roadmap to help navigate the complexities of machine learning in an application-oriented guide. This paper covers the main aspects of machine learning including supervised, unsupervised, and semi-supervised approaches as well as deep learning. The roadmap for supervised machine learning starts with linear regression and progressively builds towards more complex and flexible algorithms with discussions about the advantages and disadvantages of using certain models over others. This paper discusses the real-world applications of both labeled and unlabeled data; supervised and unsupervised machine learning algorithms; overfitting and underfitting; cross-validation; and the importance of hyperparameter tuning to better fit algorithms to their data.

Supercharge your data science workflow with Git, GitHub, and Data Version Control (DVC)! This practical session dives into essential version control tools every data team should master — featuring hands-on tips, real-world examples, and integration strategies to efficiently track changes to both code and data. Discover how Git enables clean branching, purposeful commits, and streamlined collaboration. Push those local commits to GitHub to unlock team-based workflows with pull requests, protected branches, and remote repository management. DVC then extends Git by tracking large datasets and machine learning (ML) models stored in local systems or external servers and cloud storage providers — without bloating your Git repository. From making meaningful and informative commits to safely stashing changes and managing parallel branches, this session delivers actionable tips, tricks, and techniques to help your team version smarter, work in parallel, reduce merge conflicts, and collaborate more effectively across the stack.

Data scientists, statistical programmers, machine learning engineers, and researchers are increasingly leveraging a growing number of open-source tools, libraries, and programming languages that can enhance and seamlessly integrate with their existing data workflows. One of these integrations, built into SAS® Viya®, is its pre-configured Python runtime integration, PROC PYTHON, that gives SAS programmers access to Python's open-source data science libraries for wrangling and modeling structured and unstructured data alongside the validated procedures provided in SAS. This paper demonstrates how to install and import external Python libraries into their SAS Viya sessions; generate Python scripts containing methods that can import, process, visualize, and analyze data; and execute those Python methods and scripts using SAS Viya's PYTHON procedure. By integrating the added functionalities of Python's libraries for data processing and modeling with SAS procedures, SAS programmers can enhance their existing data workflows with Python's open-source data solutions.

Capable of accepting and mapping complex relationships hidden within structured and unstructured data, neural networks are built from layers of neurons and activation functions that interact, preserve, and exchange information between layers to develop highly flexible and robust predictive models. Neural networks are versatile in their applications to real-world problems; capable of regression, classification, and generating entirely new data from existing data sources, neural networks are accelerating recent breakthroughs in Deep Learning methodologies. Given the recent advancements in graphical processing unit (GPU) cards, cloud computing, and the availability of interpretable APIs like the Keras interface for TensorFlow, neural networks are rapidly moving from development to deployment in industries ranging from finance, healthcare, climatology, video streaming, business analytics, and marketing given their versatility in modeling complex problems using structured, semi-structured, and unstructured data. This paper explores fundamental concepts associated with neural networks including their inner workings, their differences from traditional machine learning algorithms, and their capabilities in supervised, unsupervised, and generative AI workflows. It also serves as an intuitive, example-oriented guide for developing Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) architectures using Python's Keras and TensorFlow libraries for regression and image classification tasks.

Organizations around the globe are truly facing a paradigm shift with the type of software, the quantity and availability of software technologies, including open source, and the creative ways the many technologies live, play, and thrive in the same sand box together. We’ll explore the many benefits, challenges, and opportunities with open source technologies in the 21st century. We’ll also describe the challenges facing user communities as they find ways to integrate open source software and technologies, handle compatibility and vulnerability issues, address security limitations, manage intellectual property and warranty issues, and address inconsistent development practices. Plan to join us for an informative presentation about the benefits, challenges, and opportunities confronting open source user communities around the world, including the application and current state of Python, R, SQL, database systems, cloud computing, software standards, and the collaborative nature of community in the 21st century.

Google Earth Engine is a powerful cloud-based storage platform for accessing publicly available geospatial data from third party sources, including satellite imagery, geophysical, socioeconomic, climatological, census, and meteorological data measured over time for academic-use, personal-use, research, and business applications. Through a combination of beautiful visualizations and easy-to-implement Python code, users will be given the tools to conduct their own analysis with Google Earth Engine. Using the intuitive Python API, along with a suite of visualization packages and map-making libraries available for Python, this paper showcases methods for accessing, querying, extracting, and visualizing Earth Engine's spatiotemporal data to develop interactive maps. Optimized techniques permitting intensive spatiotemporal analysis on large, complex datasets are introduced through server-side operations in Google Earth Engine. By the end of this Paper, users will feel comfortable setting-up, configuring, and linking Earth Engine to Python, become acquainted with commonly-used formats for storing various types of spatial data, understand methods for querying, selecting, uploading, and exporting datasets from Earth Engine, effectively visualize high resolution spatiotemporal data using Python's Geemap package, and be able to conduct analysis using server-side operations to efficiently complete resource-intensive tasks.