• build  Skills
  • code Programming



    Arduino C++

    Android Java

  • Electronics

    Benchtop Equipment: function generators, oscilloscope, power sources, digital multimeter.

    Eagle for PCB design

    PCB manufacturing through-hole and surface-mount component boards.

  •   Education
  • University of Colorado Boulder

    M.S. Electrical Engineering with an emphasis in embedded systems and industrial IoT.

  • Iowa State University

    B.S. Mechanical Engineering, minor in Data Science.

  •   Career Experience
  • Iowa Aquaponics, LLC

    July 2011 - May 2015
    An IoT business I founded in which I designed custom embedded systems to connect to a scalable cloud application for commercial greenhouses.

  • United Parcel Service - Supervisor

    Jan 2006 - Aug 2009
    Shift supervisor managing 30 employees in the safe and accurate unloading of trucks to meet or exceed company production quotas and safety standards.

  • star  Certificates
  • explore  Activities
  • Engineering Leaders of Tomorrow

    A professional development conference hosted by various engineering leadership organizations on campus.

  • American Institute of Aeronautics and Astronautics

    As the IT Executive, I was responsible for managing the digital assets of the ISU AIAA student branch. This included maintaining the website, engaging members and the general public through social media and developing sponsorship leads.

  • NASA Aerospace Scholars Program

    In this national program, STEM-focused community college students interested in NASA related careers participate in a five-week online learning experience. Top scoring scholars receive invitations to attend a four-day workshop at a NASA center. I completed my on-site experience at Marshall Space Flight Center in Huntsville, AL, where I was challenged with a team robotics competition and presentation.

  • assignment  MSEE Courses
  • Sensor and Sensor Circuit Design

    Understand how to specify the proper thermal, flow, or rotary sensor for taking real-time process data. Implement thermal sensors into an embedded system in both hardware and software. Add the sensor and sensor interface into a microprocessor based development kit. Create hardware and firmware to process signals and feed data to a microprocessor for further evaluation. Study sensor signal noise and apply proper hardware techniques to reduce it to acceptable levels.

  • assignment  ISU Courses
  • Industrial Automation

    Overview of electrical circuit theory and its relationship to industrial control systems. Theory and application of transducers in the form of sensors and actuators, with applications in manufacturing, distribution and mechanical systems. Programmable Logic Controllers (PLC), their programming and use for automation solutions. Introduction of automated identification systems such as Radio Frequency Identification (RFID) and Bar Coding technologies.

  • Applied Data Modeling and Predictive Analytics

    Elements of predictive analysis such as training and test sets; feature extraction; survey of algorithmic machine learning techniques, e.g. decision trees, Naïve Bayes, and random forests; survey of data modeling techniques, e.g. linear model and regression analysis; assessment and diagnostics: overfitting, error rates, residual analysis, model assumptions checking; communicating findings to stakeholders in written, oral, verbal and electronic form, and ethical issues in data science. Participation in a multi-disciplinary team project.

  • Data Analytics and Machine Learning for Cyber-Physical Systems Applications

    Introduction to data analytics and machine learning driven solutions to cyber-physical systems problems such as design and verification, anomaly detection, fault diagnostics, event classification, prediction and mitigation. The course involves hands-on learning of various data science techniques for various problem solving steps such as data preprocessing/variable selection, feature extraction, modeling, inference and visualization tasks with a special focus on advanced tools such as deep learning and probabilistic graphical models. Applications include diverse cyber-physical systems - smart buildings and grid, transportation, manufacturing, agriculture and energy systems.

  • Engineering Problem Solving with R

    Statistical analysis and engineering problem solving using R programming language. Data manipulation. Exploratory data analysis. Statistical quality assurance. Basic statistical analysis. R Markdown. Simulation. Conditional expressions, loops, and functions. Matrices. High level data visualizations. Data extraction from text. Optimization. Logistic regression. High performance computing tools.

  • Data Acquisition and Exploratory Data Analysis

    Data acquisition: file structures, web-scraping, database access; ethical aspects of data acquisition; types of data displays; numerical and visual summaries of data; pipelines for data analysis: filtering, transformation, aggregation, visualization and (simple) modeling; good practices of displaying data; data exploration cycle; graphics as tools of data exploration; strategies and techniques for data visualizations; basics of reproducibility and repeatability; web-based interactive applets for visual presentation of data and results. Programming exercises.

  • Intro to Data Science

    Data Science concepts and their applications; domain case studies with appliations in various fields; overview of data analysis; major components of data analysis pipelines; computing concepts for data science; descriptive data analysis; hands-on data analysis experience; communicating findings to stakeholders, and ethical issues in data science.

  • Optimization Methods for Complex Designs

    Optimization involves finding the 'best' according to specified criteria. Review of a range of optimization methods from traditional nonlinear to modern evolutionary methods such as Genetic algorithms. Examination of how these methods can be used to solve a wide variety of design problems across disciplines, including mechanical systems design, biomedical device design, biomedical imaging, and interaction with digital medical data. Students will gain knowledge of numerical optimization algorithms and sufficient understanding of the strengths and weaknesses of these algorithms to apply them appropriately in engineering design. Experience includes code writing and off-the-shelf routines. Numerous case-studies of real-world situations in which problems were modeled and solved using advanced optimization techniques.

  • Spreadsheets and Databases

    Using Microsoft Excel spreadsheets and Microsoft Access databases to input, store, process, manipulate, query, and analyze data for business and industrial applications.

  • Engineering Statistics

    Statistics for engineering problem solving. Principles of engineering data collection; descriptive statistics; elementary probability distributions; principles of experimentation; confidence intervals and significance tests; one-, two-, and multi-sample studies; regression analysis; use of statistical software; team project involving engineering experimentation and data analysis.

  • Engineering MATLAB

    This course provides students with a solid foundation in structured programming skills for the solution of engineering problems. Students will analyze problems, design solution algorithms, translate the algorithm to MATLAB and Simulink computer code and present the solutions of the problems.

  • Electric Circuits

    Emphasis on mathematical tools. Circuit elements (resistors, inductors, capacitors) and analysis methods including power and energy relationships. Network theorems. DC, sinusoidal steady-state, and transient analysis. AC power. Frequency response. Two port models. Diodes, PSPICE. Laboratory instrumentation and experimentation.