James D. McCaffrey

James D. McCaffrey
James D. McCaffrey
Occupation(s)	Software engineer, author
Employer	Microsoft Research
Known for	Machine learning
Website	jamesmccaffrey.wordpress.com

Last updated August 10, 2024

James D. McCaffrey is an American research software engineer at Microsoft Research known for his contributions to machine learning, combinatorics, and software test automation.

Education

McCaffrey earned a BA in experimental psychology from the University of California, Irvine, a B.A. in applied mathematics from California State University, Fullerton, an M.S. in computer science information systems from Hawaii Pacific University, and a Ph.D. in interdisciplinary computational statistics and cognitive psychology from the University of Southern California.^[1]

Career

Prior to joining Microsoft, McCaffrey was the Associate Vice President of Research at Volt Information Sciences in Redmond, Washington, supporting the needs of software engineers at Microsoft.^{[ citation needed ]} He joined Microsoft as a software engineer in 2006 and worked on various Microsoft products, including Exchange Server, Azure, and Bing.^{[ citation needed ]} He then became a research software engineer at Microsoft Research, where he directs the internal Microsoft AI School, focusing on creating machine learning and artificial intelligence algorithms. He is the Senior Technical Editor for Microsoft's Visual Studio Magazine.^[1]

His research at Microsoft primarily focuses on machine learning. His other research interests include combinatorics, especially when applied to human behavior such as sports betting and Blackjack Switch, as well as "software systems which have designs influenced by the behavior of biological systems such as swarm intelligence optimization and simulated bee colony algorithms and their application to data mining.^[1]

Selected bibliography

McCaffrey, J.D., "Using the Multi-Attribute Global Inference of Quality (MAGIQ) Technique for Software Testing", Proceedings of the 6th International Conference on Information Technology New Generations, April 2009, pp. 738–742.
McCaffrey, J.D., "An Empirical Study of the Effectiveness of Partial Antirandom Testing", Proceedings of the 18th International Conference on Software Engineering and Data Engineering, June 2009, pp. 260–265.
McCaffrey, J.D. and Czerwonka, J., "An Empirical Study of the Effectiveness of Pairwise Testing", Proceedings of the 2009 International Conference on Software Engineering Research and Practice, July 2009, pp. 186–191.
McCaffrey, J.D., "Generation of Pairwise Test Sets using a Genetic Algorithm", Proceedings of the 33rd IEEE International Computer Software and Applications Conference, July 2009, pp. 626–631.
McCaffrey, J.D., "Generation of Pairwise Test Sets using a Simulated Bee Colony Algorithm", Proceedings of the 2009 IEEE International Conference on Information Reuse and Integration, August 2009, pp. 115–119.
McCaffrey, J.D. and Dierking, H., "An Empirical Study of Unsupervised Rule Set Extraction of Clustered Categorical Data using a Simulated Bee Colony Algorithm", Proceedings of the 3rd International Symposium on Rule Interchange and Applications, November 2009, pp. 182–192.
McCaffrey, J.D., "An Empirical Study of Categorical Dataset Visualization using a Simulated Bee Colony Algorithm", Proceedings of the 5th International Symposium on Visual Computing, December 2009, pp. 179–188.
McCaffrey, J.D., "Keras Succinctly for Syncfusion",^[2] An eBook focused on Keras, an open-source, neural-network library written in the Python language., September 2018.
McCaffrey, J.D., "Introduction to CNTK Succinctly for Syncfusion",^[3] An eBook focused on Microsoft CNTK (Cognitive Toolkit, formerly Computational Network Toolkit), an open source code framework that enables you to create deep learning systems, such as feed-forward neural network time series prediction systems and convolutional neural network image classifiers., April 2018.
McCaffrey, J.D., "Bing Maps V8 Succinctly for Syncfusion",^[4] The Bing Maps V8 library is a very large collection of JavaScript code that allows web developers to place a map on a webpage, query for data, and manipulate objects on a map, creating a geo-application. August 2017.
McCaffrey, J.D., "R Programming Succinctly for Syncfusion",^[5] The R programming language on its own is a powerful tool that can perform thousands of statistical tasks, but by writing programs in R, you gain tremendous power and flexibility to extend its base functionality. June 2017.
McCaffrey, J.D., "SciPy Programming Succinctly for Syncfusion",^[6] SciPy Programming Succinctly offers readers a quick, thorough grounding in knowledge of the Python open source extension SciPy. September 2016.
McCaffrey, J.D., "Machine Learning Using C# Succinctly for Syncfusion",^[7] In Machine Learning Using C# Succinctly, you'll learn several different approaches to applying machine learning to data analysis and prediction problems. October 2014.
McCaffrey, J.D., "Neural Networks Using C# Succinctly for Syncfusion",^[8] Neural networks are an exciting field of software development used to calculate outputs from input data. While the idea seems simple enough, the implications of such networks are staggering—think optical character recognition, speech recognition, and regression analysis. July 2014.

Related Research Articles

In machine learning, a neural network is a model inspired by the structure and function of biological neural networks in animal brains.

Microsoft Developer Network (MSDN) was the division of Microsoft responsible for managing the firm's relationship with developers and testers, such as hardware developers interested in the operating system (OS), and software developers developing on the various OS platforms or using the API or scripting languages of Microsoft's applications. The relationship management was situated in assorted media: web sites, newsletters, developer conferences, trade media, blogs and DVD distribution.

Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data and thus perform tasks without explicit instructions. Recently, artificial neural networks have been able to surpass many previous approaches in performance.

Microsoft Translator is a multilingual machine translation cloud service provided by Microsoft. Microsoft Translator is a part of Microsoft Cognitive Services and integrated across multiple consumer, developer, and enterprise products, including Bing, Microsoft Office, SharePoint, Microsoft Edge, Microsoft Lync, Yammer, Skype Translator, Visual Studio, and Microsoft Translator apps for Windows, Windows Phone, iPhone and Apple Watch, and Android phone and Android Wear.

Learning to rank or machine-learned ranking (MLR) is the application of machine learning, typically supervised, semi-supervised or reinforcement learning, in the construction of ranking models for information retrieval systems. Training data may, for example, consist of lists of items with some partial order specified between items in each list. This order is typically induced by giving a numerical or ordinal score or a binary judgment for each item. The goal of constructing the ranking model is to rank new, unseen lists in a similar way to rankings in the training data.

<span class="mw-page-title-main">Symbolic regression</span> Type of regression analysis

Symbolic regression (SR) is a type of regression analysis that searches the space of mathematical expressions to find the model that best fits a given dataset, both in terms of accuracy and simplicity.

Eclipse Deeplearning4j is a programming library written in Java for the Java virtual machine (JVM). It is a framework with wide support for deep learning algorithms. Deeplearning4j includes implementations of the restricted Boltzmann machine, deep belief net, deep autoencoder, stacked denoising autoencoder and recursive neural tensor network, word2vec, doc2vec, and GloVe. These algorithms all include distributed parallel versions that integrate with Apache Hadoop and Spark.

The following table compares notable software frameworks, libraries and computer programs for deep learning.

<span class="mw-page-title-main">Microsoft Cognitive Toolkit</span> Deep learning framework by Microsoft Research

Microsoft Cognitive Toolkit, previously known as CNTK and sometimes styled as The Microsoft Cognitive Toolkit, is a deprecated deep learning framework developed by Microsoft Research. Microsoft Cognitive Toolkit describes neural networks as a series of computational steps via a directed graph.

Keras is an open-source library that provides a Python interface for artificial neural networks. Keras was first independent software, then integrated into the TensorFlow library, and later supporting more. "Keras 3 is a full rewrite of Keras [and can be used] as a low-level cross-framework language to develop custom components such as layers, models, or metrics that can be used in native workflows in JAX, TensorFlow, or PyTorch — with one codebase." Keras 3 will be the default Keras version for TensorFlow 2.16 onwards, but Keras 2 can still be used.

Chainer is an open source deep learning framework written purely in Python on top of NumPy and CuPy Python libraries. The development is led by Japanese venture company Preferred Networks in partnership with IBM, Intel, Microsoft, and Nvidia.

The following outline is provided as an overview of and topical guide to machine learning:

Caffe is a deep learning framework, originally developed at University of California, Berkeley. It is open source, under a BSD license. It is written in C++, with a Python interface.

The Open Neural Network Exchange (ONNX) [] is an open-source artificial intelligence ecosystem of technology companies and research organizations that establish open standards for representing machine learning algorithms and software tools to promote innovation and collaboration in the AI sector. ONNX is available on GitHub.

<span class="mw-page-title-main">ML.NET</span> Machine learning library

ML.NET is a free software machine learning library for the C# and F# programming languages. It also supports Python models when used together with NimbusML. The preview release of ML.NET included transforms for feature engineering like n-gram creation, and learners to handle binary classification, multi-class classification, and regression tasks. Additional ML tasks like anomaly detection and recommendation systems have since been added, and other approaches like deep learning will be included in future versions.

<span class="mw-page-title-main">Microsoft SEAL</span>

Simple Encrypted Arithmetic Library or SEAL is a free and open-source cross platform software library developed by Microsoft Research that implements various forms of homomorphic encryption.

LightGBM, short for Light Gradient-Boosting Machine, is a free and open-source distributed gradient-boosting framework for machine learning, originally developed by Microsoft. It is based on decision tree algorithms and used for ranking, classification and other machine learning tasks. The development focus is on performance and scalability.

Owl Scientific Computing is a software system for scientific and engineering computing developed in the Department of Computer Science and Technology, University of Cambridge. The System Research Group (SRG) in the department recognises Owl as one of the representative systems developed in SRG in the 2010s. The source code is licensed under the MIT License and can be accessed from the GitHub repository.

A graph neural network (GNN) belongs to a class of artificial neural networks for processing data that can be represented as graphs.

References

1 2 3 "James McCaffrey: Senior Research Software Engineer". Microsoft Research. Microsoft. Retrieved January 8, 2022.
↑ "Syncfusion Free Ebooks | Keras Succinctly". www.syncfusion.com. Retrieved February 17, 2021.
↑ "Syncfusion Free Ebooks | Introduction to CNTK Succinctly". www.syncfusion.com. Retrieved February 17, 2021.
↑ "Syncfusion Free Ebooks | Bing Maps V8 Succinctly". www.syncfusion.com. Retrieved February 17, 2021.
↑ "Syncfusion Free Ebooks | R-Programming Succinctly". www.syncfusion.com. Retrieved February 17, 2021.
↑ "Syncfusion Free Ebooks | SciPy Programming Succinctly". www.syncfusion.com. Retrieved February 17, 2021.
↑ "Syncfusion Free Ebooks | Machine Learning Using C# Succinctly". www.syncfusion.com. Retrieved February 17, 2021.
↑ "Syncfusion Free Ebooks | Neural Networks Using C# Succinctly". www.syncfusion.com. Retrieved February 17, 2021.

Introduced a description and C# language implementation of the factoradic, in fact a type of factorial number system, in "Using Permutations in .NET for Improved Systems Security", McCaffrey, J. D., August 2003, MSDN Library. See http://msdn2.microsoft.com/en-us/library/aa302371.aspx and "String Permutations", MSDN Magazine, June 2006 (Vol. 21, No. 7).
Laisant, Charles-Ange (1888), "Sur la numération factorielle, application aux permutations", Bulletin de la Société Mathématique de France (in French), 16: 176–183; a previous description of a factorial number system.

Introduced a description and C# language implementation of the combinadic, in fact a type of combinatorial number system, in "Generating the mth Lexicographical Element of a Mathematical Combination", McCaffrey, J. D., July 2004, MSDN Library. See http://msdn2.microsoft.com/en-us/library/aa289166(VS.71).aspx.
Applied Combinatorial Mathematics, Ed. E. F. Beckenbach (1964), pp. 27−30; a previous description of a combinatorial representation of integers.
McCaffrey, James D., ".NET Test Automation Recipes", Apress Publishing, 2006. ISBN 1-59059-663-3.

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[microsoft-1] 1 2 3 "James McCaffrey: Senior Research Software Engineer". Microsoft Research. Microsoft. Retrieved January 8, 2022.

[2] "Syncfusion Free Ebooks | Keras Succinctly". www.syncfusion.com. Retrieved February 17, 2021.

[3] "Syncfusion Free Ebooks | Introduction to CNTK Succinctly". www.syncfusion.com. Retrieved February 17, 2021.

[4] "Syncfusion Free Ebooks | Bing Maps V8 Succinctly". www.syncfusion.com. Retrieved February 17, 2021.

[5] "Syncfusion Free Ebooks | R-Programming Succinctly". www.syncfusion.com. Retrieved February 17, 2021.

[6] "Syncfusion Free Ebooks | SciPy Programming Succinctly". www.syncfusion.com. Retrieved February 17, 2021.

[7] "Syncfusion Free Ebooks | Machine Learning Using C# Succinctly". www.syncfusion.com. Retrieved February 17, 2021.

[8] "Syncfusion Free Ebooks | Neural Networks Using C# Succinctly". www.syncfusion.com. Retrieved February 17, 2021.

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