UplinkRobotics designs innovative, dual-sided inspection robots that navigate tight, dark spaces to improve safety and efficiency for home inspectors, SWAT teams, contractors, and engineers. Since winning the 2022 Ellbogen Competition at the University of Wyoming, the company has expanded rapidly, serving clients in more than 40 states and 4 countries. Dedicated to fostering local talent, UplinkRobotics sponsors senior design projects and employs Wyoming university interns to support the next generation of engineers.

At VMAccel, I led the prototype development of innovative immersion cooling systems. My work involved designing 3D models and technical drawings in Fusion 360, collaborating with fabricators to ensure cost-effective and manufacturable solutions. By applying fluid dynamics and creating low-cost prototypes, I helped advance VMAccel’s product line for scalable cooling in high-demand computing environments.

● Collaborated with a multidisciplinary team to locate sediment sources behind Willwood Dam in northwest Wyoming’s Shoshone river using remote sensing with PlanetScope satellite imagery, and communicated the findings to local workgroups for sediment reduction actions. ● Applied custom Python scripts and GIS software such as QGIS to analyze high-resolution PlanetScope imagery and remotely estimate river turbidity values using remote sensing techniques. ● Built a machine learning random forest model to use remote sensing techniques to detect sediment plumes in satellite imagery with a high coefficient of determination (R^2) of .93. ● Created an autonomous plume locator that measured the downstream distance from Buffalo Bill dam and identified potential tributaries of interest.You can read more about the project at nasa.gov:https://appliedsciences.nasa.gov/what-we-do/projects/quantifying-sediment-input-shoshone-river-wyoming-using-soil-and-water

● Developed VHDL code, simulations, Python scripts, and production designs for multi-device systems to be utilized in final commercial designs using various proprietary toolsets as an FPGA firmware engineer.

● Developed computer vision scripts for motion tracking, object detection, and facial recognition using OpenCV and Python. ● Built machine learning neural network algorithms with image processing systems to autonomously detect vehicles in thermal frames during adverse weather conditions. ● Worked on a human anatomy model for medical training using the Microsoft HoloLens Augmented Reality Headset and Unity. ● Completed various projects involving physical computing and the internet of things (IoT).

● Assisted in teaching EE2800, Design Experience with Devices for the Computer and Electrical Engineering Department for two semesters. The class EE2800 allows students to learn about physical computing using a Raspberry Pi, sensors, and Python programming.

● Developed a low voltage differential signaling (LVDS) de-serializer, LVDS serializer, and FIFO reader IP cores using Verilog and integrated them into a Microblaze soft-processor design. ● Explored an FPGA design using an Artix-7 to run custom IP cores and PetaLinux embedded OS on a Microblaze soft-processor for LVDS and SPI communication with an ASIC. ● Worked on and explored Core Flight System (CFS) software applications for time synchronization and ASIC control.

● Created a functional radio occultation payload system to be mounted on a radiosonde weather balloon as a lower cost method of retrieving atmospheric dry temperature versus low earth satellites.● Used a Septentrio Mosaic 3 triple band GPS receiver to collect doppler and C/N (carrier to noise) measurements on L1C/A, L2P, and L2C GPS bands of the GPS constellation of GNSS.● Created a satellite location estimation program using Python w/ Skyfield and SGP4 libraries utilizing the jacobian calendar time format and almanac data in two line element (TLE) format.● Created Python script to convert comma separated variable (CSV) formatted measurements and satellite location estimation coordinates into netCDF4 file format.● Process GPS refractivity data in ROMSAF's Radio Occultation Processing Package written in Fortran 90/95 using Ubuntu Linux & Windows Subsystem for Linux.● Use a Raspberry Pi microcomputer with type-T thermocouple & BME680 sensor to collect local radiosonde data.