Experience

• Hardware Design Engineer @ Creative Motion(2019-2020)
• iOS Developer @ Creative Motion(2020-2021)
• Web Developer @ Creative Motion(2020-2021)
• Software Engineer Intern @ Accenture(2022)
• Co-Founder & CTO @ RTNF Group(2023)

Certifications

• 
  Google Cloud Associate Engineer
  
• 
  Web Programming with Python and JS
  
• 
  Solidworks CSWA
  
• 
  Altium Designer Advanced
  

Research - Preprints

• SCAR: Enhancing LPWAN Sustainability through Solar-Energy-Aware Routing(2024)

  • Developed a new routing protocol for LPWANs
  • Implemented the protocol in EMB-C on the STM32L4 Platform
  • Achieved a 9.1% increase in network lifetime
  • Paper

  Why? Or What use case?

  • I have mounted up an array of LoraWAN sensors for the purposes of natural preservation in the Leaota mountains, around Runcu, Dambovita in Romania. The end goal was to better track animal population and fauna growth through the tracking of various sensor and image data. All 25 sensors are equipped with solar panels, and the lifetime of the network is crucial to a low-maintenece setup.

• Multi-dimensional Signaling: Circular Polarization, Dynamic Polarization Control, and OFDMA for Ultra-High Spectral Efficiency(2023)

  • Developed a new signaling scheme for 5G
  • Simulated the scheme in Matlab
  • Achieved a 12% increase in spectral efficiency
  • Paper

  Why? Or What use case?

  • Just a theoretical experiment to see if the OFDMA implemented in the latest Wifi 6/5G can be transposed using Circular Polarisation as well in order to further divide the frequency spectrum. Scope is to achieve peak spectral efficiency. Only issue is that the equipment needed to get this done in practise is pretty inconvenient.

• MetalNetwork - Graph Implementation on Metal(2024-2025)

  • Developed a graph implementation on Metal in C++
  • Achieved performance gains similar to CUDA on Apple Devices
  • Incorporates Clustering to allow for intra-cluster communication via TB3/TB4 for computation
  • Paper

  Why? Or What use case?

  • Apple's memory is more cost effective than Nvidia's for HPC applications. If time is not a constraint the Apple Silicon memory can be used pretty efficiently for computational problems that can be solved in a high-parallelised way. Essentially, treating Apple's Metal just as CUDA in some regard. Used it to play around with huge Railway Network Graphs.