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Ryan Peach

Ryan Peach

Bowling Green, United States
Member since September 29, 2016
Ryan is an electrical engineering and computer science graduate student, who has worked for several years in R&D for various clients—in topics ranging from math and scientific testing applications, hardware and circuit design, and most recently machine learning, AI, and computer vision applications. He is a talented Python/C++/LabVIEW developer and keeps up to date on the latest cutting edge research in the field of CS.
Ryan is now available for hire
  • Electrical Design, 5 years
  • C++, 4 years
  • Machine Learning (ML), 3 years
  • LabVIEW, 3 years
  • Python, 3 years
  • Neural Networks, 2 years
  • Computer Vision, 1 year
Bowling Green, United States
Preferred Environment
Machine Learning, Computer Vision, PCB Design
The most amazing...
...AI I have built was capable of solving Raven's Progressive Matrices intelligence tests at a human level using knowledge-based AI techniques.
  • Electrical Engineer, Automation
    Nasco NHK
    2015 - PRESENT
    • Programmed PLCs and robotics for a variety of industrial control and automation applications.
    • Designed electronics with AutoCAD, implemented hardware upgrades to equipment.
    • Diagnosed and maintained industrial electrical equipment.
    • Chose and ordered necessary parts and created budget reports.
    • Worked in teams with electrical maintenance and upper management, including Japanese engineers.
    Technologies: Ladder Logic, PLCs, Industrial Electrical Design
  • Electrical Engineer, R&D
    WKU Science Labs
    2014 - 2015
    • Worked with physics researchers in designing electronics for experimentation.
    • Helped design applications of Atomic Force Microscopes, Piezoelectric material, Chemical Detection, Solar Design, Motor Control, Signal Amplifiers, and Terra-hertz Lasers. Designed PCBs and programmed FPGAs.
    • Ordered parts and worked in CAD to design electronics.
    • Delivered a product from scratch independently.
    • Worked with the client directly to determine project requirements.
    • Designed measurement rigs for experimentation of project deliverables. Created scientific computing environments and software for use with the product.
    Technologies: Control Systems Engineering, Precision Amplification Circuitry, CAD, LabVIEW, VHDL, Verilog, Python, MATLAB, C++
  • Student Software Developer
    Department​ ​of​ ​Chemistry,​ ​WKU
    2014 - 2014
    • Designed software to control advanced multimeters via LabVIEW computer interface.
    • Created applications for graphing voltage, resistance, and currents in real-time.
    • Designed a testing platform and product enclosure for a solar array. Created the PCB design for interfacing test equipment with lab equipment.
    • Worked with international team collaboratively on research goals.
    • Created a user friendly design and instructions made for easy lab access.
    Technologies: LabVIEW, Oscilloscopes, Multimeters, Solar Panels, DAQs
  • Electrical Engineering Student
    Applied Physics Institute
    2014 - 2014
    • Designed PCBs, C++, and LabVIEW programming for scientific and embedded purposes.
    • Implemented machine learning algorithms for advanced mutli-chemical detection.
    • Researched and implemented new electrochemical and photovoltaic early detection units for air filtration systems.
    • Performed air current testing and designed rigs for experimentation.
    • Gave presentations and frequent project reports to supervisor. Worked in multidisciplinary team to create a consumer product worthy good.
    Technologies: C++, Arduino, Atmel, Eagle PCB, LabVIEW, Machine Learning, PCB Design
  • Robotics Engineer | Teacher
    Center for Gifted Studies
    2012 - 2014
    • Taught Mindstorm robotics for many years at the middle school level.
    • Designed and implemented advanced programming challenges such as maze solving algorithms and synchronous dynamics.
    • Taught basic mechanical and electrical concepts.
    Technologies: Lego Mindstorms, Electronics, Physics, Mathematics, Teaching
  • DHS-STEM Student Internship
    Pacific Northwest National Laboratory
    2013 - 2013
    • Ran discrete-time Fourier analysis, digital filtering algorithms, and statistical analyses on large time-series datasets in Python and MATLAB.
    • Implemented machine learning spectral algorithms on large NLP datasets.
    • Attended weekly progress report meetings and presented research proposals for the future of the project.
    • Presented findings to the public both in research paper format and on-stage presentation.
    • Delivered project deliverables to DHS on the completion of the project.
    Technologies: Python Data Analytics, Fourier Analysis, Social Media Analysis, Machine Learning
  • Raven's Progressive Matrices AI (Development)

    In Knowledge-Based AI, an intensely project-and-research-based graduate course, we each created our own research level artificial intelligence—each of which should be capable of solving every standard set of the Raven's Progressive Matrices intelligence test, verbally at first, visually by the end of the class, and were graded on our agent's performance on these tests.

    My final agent scored 19/24 on the hardest problem sets, purely via visual information. My first project reflection was selected as a top 10 best in class, and my agent overall scored very well relative to my peers. We were allowed no outside code from other students, and worked with Python using only Numpy and PIL libraries. All in all, this has been my favorite class by far overall, and it inspired much of my interests in AI going forward.

  • "SALSA" SociAL Sensor Analytics at PNNL (Development)

    While working for PNNL, I was given the task of constructing a mathematical model to increase the detection of Twitter-traffic spikes in a large web-traffic time-series. Over the course of several months, working with a mathematician Ph.D. and 2 undergraduate students of differing specialties, I learned how to write fluently in Python—using several different mathematical and graphical libraries and wrote thousands of lines of well-documented code for every task imaginable. My primary line of research was in the area of Fast Fourier Transforms (FFT), which required a lot of rigorous mathematical study and code manipulation on my part.

    I spent months writing and testing highly complex algorithms for mathematical analyses, and even more time on interpreting the data and trying to improve the graphical outputs my code would generate. In the end, the experience was highly rewarding, and it taught me how to learn and research independently, as well as how to function well in a business environment.

  • OpenAI Gym Projects (Development)

    In my spare time, I work on reinforcement learning problems on OpenAI Gym.

    The following is a sample of a Q Learner with documentation that I have recently written for the site; which has a very high ranking and quick learning time for the environment, and is highly generalizable to other problem sets.

  • GitHub Open Source (Development)

    My entire GitHub profile is available online.

    I have participated in leading edge open-source projects such as OpenNARS at Temple University and frequently submitted code snippets to scientific computing archives such as NumPy and SciPy.

  • Active Contributor to Sklearn-deap—a Genetic Algorithm Library for Interfacing with Sklearn (Other amazing things)

    I upgraded their development tools to the most recent Sklearn standards and constantly answer issues that arise.

  • Kaggle Data Analysis (Development)

    I did some work on data analysis after the recent election and received some much-appreciated acclaim for my post on Kaggle for using their datasets.

  • Languages
    Python, Common Lisp (CL), Go, Java, C++
  • Libraries/APIs
    Pandas, NumPy, SciPy, OpenCV, TensorFlow, Sklearn
  • Tools
    LabVIEW, CAD
  • Paradigms
    Object-oriented Programming (OOP), Concurrent Programming, Data Science, Functional Programming
  • Misc
    Electrical Design, Artificial Intelligence (AI), PLC, Allen-Bradley PLCs, PCB Design, Mathematics, Robotics, Machine Learning (ML), Localization, Deep Learning, Computer Vision, Neural Networks, Reinforcement Learning, Algorithms, Data Structures, FPGA
  • Platforms
    Arduino, Debian Linux, Windows, Android
  • Master's degree in Computer Science, Robotics, and AI
    Georgia Tech - Atlanta, GA, USA
    2016 - 2018
  • LabVIEW CLAD Certification in Computer Engineering
    National Instruments - Bowling Green, KY, USA
    2013 - 2015
  • Bachelor of Science in Electrical Engineering with a minor in Mathematics
    Western Kentucky University - Bowling Green, KY, USA
    2010 - 2015
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