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Education

08-2018Expected 04-2020

Master of Science in Computer Engineering

Wright State University

Focus: Geo-Aware Trajectory Generation for SUMO

Advisor: Dr. Derek Doran

08-201404-2018

Bachelors of Science in Computer Engineering with a Minor in Mathematics

Wright State University

Summa Cum Laude

GPA: 3.95 (on a 4-point scale)

08-201005-2014

High School Diploma

Heritage Homeschool

GPA: 3.98 (on a 4-point scale)

Work Experience

08-2018PRESENT

Graduate Research Assistant

KNO.E.SIS Laboratory at Wright State University

Graduate Research Assistant in the Machine Learning and Complex Systems (MLaCS) Laboratory. Research focuses on generating synthetic trajectory data using parameters extracted from the dataset itself. Delivering a CRAN-ready R package which is capable of interfacing with SUMO (Simulation of Urban Mobility) is a key deliverable of this project.

08-2014PRESENT

Owner

Jameson Morgan Enterprises

Owner of a small technology company that provides a wide range of on-demand services, including, technical support, marketing and design services, as well as website design.

08-201704-2018

Undergraduate Research Assistant

KNO.E.SIS Laboratory at Wright State University

Undergraduate Research Assistant for the Machine Learning and Complex Systems (MLaCSLaboratory tasked with providing fast visualization of large geospatial networks as well as integrating a DNN into an industry produced software package.

08-201607-2017

Teaching Assistant

Wright State University

Teaching Assistant for the class Discrete Mathematics and Computing with weekly grading and teaching responsibilities.

08-201408-2015

Secretary

Village Family Dental

Secretary for Village Family Dental in Powell, Ohio. Handled patient payments, insurance claims and scheduling.

Research and Publications

GEO-AWARE TRAJECTORY GENERATION FOR SUMO - August 2018 - PRESENT

  • Role: Graduate Research Assistant
  • Advisor: Derek Doran
  • Graduate Researchers: Jameson Morgan
  • Key ContributionsResearch focuses on generating synthetic trajectory data using parameters extracted from the dataset itself. Delivering a CRAN-ready R package which is capable of interfacing with SUMO (Simulation of Urban Mobility) is a key deliverable of this project.

GEO-NET: A FRAMEWORK FOR INTRINSIC GEOSPATIAL ANOMALY DETECTION (POSTER) - May 2019

  • Role: Presenter/Graduate Research Assistant
  • Advisor: Derek Doran
  • Graduate Researchers: Jameson Morgan, Matt Piekenbrock and Jace Robinson
  • Conference: SIAM NS19
  • Key Contributions: Refactoring of GeoNet data preprocessor and web-based GUI, assisting in framework definition and poster presentation
  • Link to Poster Abstract: https://meetings.siam.org/sess/dsp_talk.cfm?p=100696

    RELATING INPUT CONCEPTS TO CONVOLUTIONAL NEURAL NETWORK DECISIONS - March 2018 - April 2018

    • Role: Undergraduate Research Assistant
    • Advisor: Derek Doran
    • Graduate Researchers: Ning Xie
    • Other Contributors: Pramit Choudhary (Skater package)
    • Key Contributions: Performed proof-of-concept integration into the Skater package for a novel CNN deconvolution algorithm as well as creating a basic region of interest (ROI) selector for highlighting portions of an image.

    TOWARDS GEOSPATIAL AWARENESS USING DYNAMIC NETWORK REPRESENTATIONS - August 2017 - February 2018

    • Role: Undergraduate Research Assistant
    • Advisor: Derek Doran
    • Graduate Researchers: Matt Piekenbrock and Jace Robinson
    • Key Contributions: Designed data preprocessor for geospatial network data, designed web-based GUI tool for interacting with geospatial networks and contributed to project poster

    Technical Skills

    R

    Intermediate Experience (1-2 years)

    Markdown

    Intermediate Experience (1-2 years)

    HTML / CSS

    Intermediate Experience (5+ years)

    C/C++

    Basic Experience (1-2 years)

    Latex

    Basic Experience (1-2 years)

    Data Structures

    Basic Experience (<1 year)

    Java

    Basic Experience (<1 year)

    Python

    Basic Experience (<1 year)

    Linear Algebra

    Basic Experience (1 semester)

    MS Office

    Advanced Experience (5+ years)

    Technical Writing

    Advanced Experience (4+ years)

    Research

    Intermediate Experience (2 years)

    Computer Support

    Intermediate Experience (5+ years)

    Selected Graduate Coursework

    Algorithm Design and Analysis

    This course introduced concepts related to the design and analysis of algorithms. Topics included recurrence relations (and their role in asymptotic and probabilistic analysis of algorithms), greedy strategies, divide-and-conquer techniques, dynamic programming, and the max flow - min cut theory. Topics were emphasized through the illustration of well-known problems and applications. Homework and tests were used to demonstrate competency. (Description modified from the course syllabus.)

    Systems Simulation

    This course provided an introduction to simulation concepts and techniques, including random number generation, empirical and statistical modeling, and discrete and continuous simulations. Knowledge was tested through homework assignments, a final simulation project, and a midterm.

    Embedded Systems

    A class discussing microprocessor-based embedded systems. Topics include system architecture, embedded processors, field programmable gate arrays, hardware and software co-design, and real-time scheduling and operating systems. Topics reinforced through weekly labs and a final project where teams were tasked to design their own oscilloscope/digital logic analyzer. Hardware used included the DE0-Nano and the Arduino Uno.

    Selected Undergraduate Coursework

    Digital Systems Design

    This course used digital design principles in order to illustrate the proper method of decomposing problems. Topics discussed included, logic gates, sequential and combinational circuit design, timing and analysis, and register-level design. The capstone project involved creating a simulated mini-computer capable of performing various pre-defined operations.

    Discrete Mathematics and Computing

    This course provided the needed mathematical background for various areas in computer science. Topics included logic statements, predicate quantifiers, formal proofs, first and second principle of mathematical induction, recursive algorithms, trees, graphs and Boolean algebra.

    Secure Computing Practices

    This course provided a detailed look at various computing practices aimed at security and privacy. Specific topics included cryptography, information hiding, VPNs, SSH, iptables, SNORT, wireless networks, sandboxes, Man-in-the-Middle Attacks, stack-smashing and legal concerns of secure computing practices.

    Statistics for Engineers

    This course looked at the field of statistics from an engineer’s perspective. Topics discussed included data collection, sample testing, axioms of probability, correlation/regression and the analysis of variances technique.

    Microprocessor-based Embedded Systems

    This course provided an in-depth look at the topic of embedded systems. Areas of focus included the basics of microprocessors, engineering considerations for microprocessor based systems, performance characteristics and issues exclusive to microprocessors. Weekly labs used either the Arduino UNO or the TI MSP432, and reinforced lecture material.

    VLSI Design

    This course introduced the concept of VLSI design through in-class lectures and weekly laboratory exercises. Topics discussed include CMOS theory, circuit design techniques, basic gate design, circuit design rules and fabrication. Timing and power dissipation were also considered. Weekly laboratory exercises focused on topics discussed throughout the class.

    Operating System Internals and Design

    This course was the capstone of several prior classes which focused on computer architecture, memory design and embedded systems. Topics discussed included basic operating system functionality, semaphore and mutex design/operation, process and thread structures, virtual and physical memory and file systems. Topics reinforced through two projects which focused on semaphore/mutex usage and implementing a y86 emulator.

    Circuit Analysis I

    This course laid the groundwork for analyzing circuits (through sinusoidal circuits). Class included weekly lectures that focused on nodal analysis, the mesh technique, resistor models, capacitor models, inductor models, first and second-order circuits and sinusoidal circuits. Weekly lab exercises reinforced topics covered in-class through hands-on experience.

    Devices and Circuits

    This course explored electrical devices such as diodes, Zener diodes, bipolar junction transistors, and field effect transistors. Mathematical models were derived and utilized for solving circuit design problems. Such designs included source followers, voltage dividers, differential amplifiers and operational amplifiers.  Frequency analysis was also discussed as well as carrier flow within semi-conductors and p-n junction theory.

    Activities and Organizations

    • FIRST® LEGO® League (FLL) Volunteer
    • IEEE Student Member
    • ACM Student Member

    Other

    • U.S. Citizen