Career Profile

I am a Master’s student at Georgia Institute of Technology specializing in Machine Learning. I currently work as a student research assistant at the High Performance Architecture Lab at Georgia Tech.

Below is a list of my areas of experience:

  • Artificial Intelligence and Machine Learning: PyTorch, TensorFlow, NumPy, Pandas, Keras, Scikit-Learn
  • Containers, Microservices, and Distributed Computing: Docker, Kubernetes, Microsoft Orleans, Terraform, Pulumi
  • Database: MySQL, MSSQL, PostgreSQL, MongoDB, Redis
  • Embedded Devices: ARM, Raspberry Pi, Arduino, Mbed OS, Node-RED, MQTT
  • Full Stack Web Development: React, Angular, GraphQL, Apollo Client, ASP.NET Core, Jest, NUnit
  • Mobile Application Development: React Native, Android
  • Programming Languages: Python, TypeScript, F#, C, C++, C#, Java
  • Reverse Engineering and Malware Analysis: Ghidra, IDA Pro, Cuckoo Sandbox, Yara, Capstone, Frida, WinDbg, x64dbg
  • Systems Programming: Windows Device Drivers, Intel VT-x and VT-d, x86 and x86-64 assembly
  • Miscellaneous: Windows, Linux, Debugging (VS and GDB), Git, PowerShell, Bash


Graduate Teaching Assistant

Aug 17 - Present
Georgia Institute of Technology

Assisted Dr. Merrick Furst in teaching the Automata and Complexity course to 125 students. Wrote and graded homework assignments. Held weekly office hours to help students.

Software Development and Machine Learning Engineer

Nov 2019 - Present
Inovar Health, LLC

Worked on a mobile application that matches users with similar activity interests within an organization. Worked on matchmaking algorithm that used data science and machine learning to optimize matches made. Customers include Georgia State University, Mercer University, and Georgia Power.

Student Research Assistant

May 2019 - Present
High Performance Architecture Lab at Georgia Institute of Technology

Under Dr. Hyesoon Kim’s supervision, worked on the following research projects:

  • Creating a lossy delta-based compression scheme for neural network weight compression which reduces memory usage by up to 85% with negligance accuracy decrease and decompression performance overhead.
  • Optimizing execution of visual SLAM on the Raspberry Pi to achieve a 5× speedup in total processing time.
  • Intelligent context-aware scheduling algorithm for dynamically allocating CPU resources, achieving a 42% speedup compared to the Linux scheduler.
  • Exploiting sparsity of the SLAM algorithm to create an efficient, low-power SLAM implementation on the FPGA, which consumes 2.5× less power and is 7.4× faster than the state-of-the-art.
  • Creating a secure and verified location-aware communication mechanism for autonomous vehicles.

Student Research Assistant

Jan 2020 - Aug 2020
Cyber Forensics Innovation (CyFI) Lab at Georgia Institute of Technology

Under Dr. Maria Konte’s supervision, developed social media data collection apparatuses for classification of malicious online behavior. Analyzed this data to create a pre-emptive cyber attack detection framework based on live social media data.

Under Dr. Brendan Saltaformaggio’s supervision, maintained and developed a concolic analysis tool that uses heuristics to induce (through symoblic execution) and detect malware behavior. Additionally, analyzed popular malware samples (e.g., WannaCry and Stuxnet) to create heuristics for detecting common malware behavior.

Software Engineering Intern

May 2017 - May 2018
Ciena Corporation

Developed software to interface with network devices (e.g., Cisco Meraki) for orchestration, which was deployed to over 150 customers worldwide, with 15 being tier 1 service providers. Maintained CI/CD pipeline for application build process.


ProSay - ProSay is a legal service delivery platform that has been accepted into Georgia Tech's CREATE-X program. Get your legal questions answered for as low as $10!
Operation Dynamo - A platform for crowdsourced 3D printing of parts COVID 19.


  • Quantifying the Design-Space Tradeoffs in Autonomous Drones (2020)
  • Ramyad Hadidi, Bahar Asgari, Sam Jijina, Adriana Amyette, Nima Shoghi, Hyesoon Kim
    Formalizes fundamental drone subsystems and finds how computations can impact this design space. Presents a design-space exploration of autonomous drone systems. Releases a fully customizable open-source drone hardware and software stack.
  • Neural Network Weight Compression with NNW-BDI (2020)
  • Nima Shoghi, Andrei Bersatti, Hyesoon Kim
    Proposes a compression mechanism for neural network weights that uses techniques such as quantization, downscaling, randomized base selection, and base-delta-configuration adjustment. Reduces memory usage by up to 85% without any inference accuracy reduction. Accepted into MEMSYS 2020.
  • PISCES: Power-Aware Implementation of SLAM by Customizing Efficient Sparse Algebra (2020)
  • Bahar Asgari, Ramyad Hadidi, Nima Shoghi
    Implements a power-efficient SLAM algorithm on the FPGA by exploiting the sparsity of SLAM algorithms. Consumes 2.5× less power and is 7.4× faster than the state-of-the-art. Accepted into Design Automation Conference 2020.
  • Understanding the Software and Hardware Stacks of a General-Purpose Cognitive Drone (2020)
  • Sam Jijina, Adriana Amyette, Nima Shoghi, Ramyad Hadidi
    Analyzes the ArduCopter's performance under different workloads. Studies area-specific applications' effect on the flight stack. Accepted into ISPASS 2020.
  • SLAM Performance on Embedded Robots (2019)
  • Nima Shoghi, Ramyad Hadidi, Hyesoon Kim
    Measured and optimized the performance of running stereo camera SLAM on the Raspberry Pi. Concludes that our optimizations can speed up the algorithm’s runtime by about 5× with minor impact on accuracy. Accepted into and awarded 3rd place at the 2019 ACM Student Research Competition at ESWEEK.