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
- FPGA programming using high level synthesis tools (e.g., Vitis HLS) and hardware description languages (e.g., Verilog and VHDL)
- 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
Experiences
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.
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.
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 negligible 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.
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.
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.