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    Sarah Bullard
    Consultant

    Sarah Bullard prepares expert reports based on analyses of disputed source code, databases, and computer systems.

    Phone: 301.251.6313 ext. 103
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    SOFTWARE EXPERIENCE

    • Programming Languages & Databases

      C#, C, C++, Java, JavaScript, Python, HTML, CSS, SQL, Microsoft SQL Server, Prolog, Scala, OCaml, MATLAB, Ruby, Bash, among others

    • Frameworks & Data Analysis Tools

      Git; Bitbucket; Apache Spark; Jupyter Notebook; Python pandas, Matplotlib, Scikit-learn, CherryPy, Beautiful Soup

    • Operating Systems & Servers

      Windows, Linux, Unix

    • Mobile Devices & Applications

      Android

    As a Consultant at DisputeSoft, Sarah assists in preparing expert, rebuttal, and investigative reports by reviewing disputed source code, databases, and computer systems, and by conducting data analyses regarding source code quality and architecture. Her thorough understanding of object-oriented programming, data structures, and Unix systems allow her to effectively assist clients in the technical aspects of software-related matters, including matters involving allegations of software misappropriation.

    Sarah also develops and tests software applications to support both the work DisputeSoft performs for its clients and DisputeSoft’s internal business processes. Most recently, Sarah assisted in the development of DisputeSoft’s proprietary static code analysis tool leveraged in our Code ACE™ service, which is capable of comparing and analyzing millions of lines of source code.

    Prior to DisputeSoft, Sarah developed extensive programming skills through a variety of courses in such subjects as data science, human-computer interaction, data analytics, and advanced data structures. Sarah acquired internship experience, both with a federal contracting company and in the federal government itself. Her notable projects include the implementation of a property graph to hold cancer data, a database conversion program, and a prototype website to monitor the health of various computer systems.

    Representative Experience

    Patent Holding Company v. Technology Company

    Nature of Suit: Patent infringement matter regarding video codec patents
    Role: Consulting expert

    • Sarah reviewed client C++ source code to analyze video encoding algorithms and determine whether certain claimed components involving operations on motion vectors were present
    • Sarah ran keyword searches, performed source code traces, and reviewed documentation and literature about video encoding algorithms
    • Sarah drafted a comprehensive summary of code review findings

    Knowmadics v. Cinnamon

    Nature of Suit: Copyright infringement matter regarding location tracking and media sharing software
    Role: Consulting expert

    • Sarah performed a source code review of software written in C#, C++, ASP.NET, and JavaScript, and between T-SQL databases
    • Sarah investigated evidence of literal and non-literal copying
    • Sarah assisted in drafting an expert report

    Software Development Company A v. Software Development Company B

    Nature of Suit: Patent infringement and trade secret misappropriation matter regarding tax preparation software
    Role: Consulting expert

    • Sarah performed a source code review of taxation software to ascertain whether certain functionality was present
    • Sarah analyzed functionality within Java and JavaScript code files, including those using Java Message Service, Hibernate, and Spring
    • Sarah drafted a code review analysis detailing the presence or absence of certain functionality, as well as functionality that could be quickly and easily added with small changes

    EDUCATION

    • B.S., The University of Maryland

    Top Posts

    Post
    This is the third installment of a four-part series on technology assisted review (TAR). This article compares TAR with exhaustive manual review— review performed solely by human beings with knowledge of the subject matter.
    Post
    This is the second installment of a four-part series on technology assisted review (TAR). This article explores two machine learning techniques pertinent to TAR: optical character recognition and natural language processing.
    Post
    This is the first installment of a four-part series on technology assisted review (TAR), a process that uses machine learning to increase efficiency and decrease the cost of document review in litigation. This article explores differences between supervised and unsupervised machine learning algorithms.

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