Experience

My Top Skills
Always nice to hone existing skills and develop new ones!
- Languages: Python, C/C++, Java, VHDL, Verilog, SystemVerilog, BASH, HTML
- Libraries/Frameworks: LangChain, LangGraph, PyTorch, Tensorflow, NumPy, Pandas, Matplotlib, Jupyter, SQLAlchemy
- Developer Tools: GCP, Docker, Kubernetes, Postman, Swagger, MySQL, MongoDB, Grafana, Redis, Git, GitHub
- Hardware Design Tools: Altium Designer, Xilinx, Intel Quartus Prime, ModelSim, MATLAB, Simulink, LTSpice
- Hardware Skills: Embedded Systems, Circuit Design, PCB Design, FPGA Design, Digital Logic, Microcontrollers
(Arduino, ESP32, STM32), Motor Control (O-Drive), Serial Communication (UART, SPI, I2C, CAN, USB), Soldering
Work Experience
System Manager
- Lead a 10-member team managing frame and power systems, improving task completion efficiency by 30%.
- Assigned and tracked 15+ tasks per month, ensuring balanced workloads and timely project delivery.
Electrical Engineering Member
- Developed STM32 firmware in C for CAN-based communication, reducing signal delay from 10ms to 3ms.
- Engineered a custom remote kill switch using ESP32 microcontrollers in C++, reducing emergency response time from 5 seconds to under 1 second.
- Designed custom PCBs for the RX kill switch and TX controller using Altium Designer, ensuring consistent voltage cutoff under high-power loads.
- Assembled 20+ high-precision PCBs, achieving a 95% first-pass success rate in functional testing.
- Designed custom Python scripts on an NVIDIA Jetson GPU to coordinate real-time communication with 20+ hardware components, enhancing system synchronization and responsiveness.
- Created custom Python, C, and C++ debugging libraries to assist 50+ robotics members in software-hardware integration, reducing troubleshooting time by 40% through automated error detection.
Machine Learning Member
- Built and mathematically derived logistic and softmax regression models for MNIST digit classification using a dataset of 60,000+ training images, achieving a 98%+ accuracy on test data.
- Developed and fine-tuned neural networks and convolutional neural networks in PyTorch and Tensorflow, enhancing performance through dropout and batch normalization techniques.
- Identified key performance bottlenecks using Matplotlib visualizations, improving model convergence rates.
Embedded Systems Intern
- Developed Python firmware for the MACH-1 Medical Tester and Arthro-Probe, reducing signal processing time from 100ms to 30ms, enabling faster orthopedic diagnostics.
- Wrote and optimized custom VHDL code for Spartan FPGAs in Xilinx, enhancing real-time data acquisition reliability, reducing processing errors, and increasing acquisition speed by 15%.
- Programmed FPGA interfaces in VHDL for ADS1298 ADCs and LSM9DS1 IMU, creating custom SPI, I2C, and UART modules that allowed data transmission for real-time electrical signals and motion tracking.
- Designed and optimized real-time data acquisition pipelines to process high-frequency sensor inputs, improving system responsiveness and diagnostic accuracy by 67%.
- Verified 20+ medical PCBs, identifying and resolving critical signal integrity issues before deployment.
Teacher Assistant
[Fall 2024]:
• Graded Professor Baharak Fatholahzadeh’s Waves, Light, and Modern Physics College class.
• Topics include: Oscillatory Motion, Wave motion, Sound Waves, Superposition and standing waves, Ray Optics, Image Formation, Interference of waves, Diffraction, Relativity, Quantum Physics, Nuclear Decay, The Optics of the Compound Microscope
[Winter 2024] :
• Graded Professor Baharak Fatholahzadeh’s Electricity and Magnetism College class.
• Topics included: Electric Fields, Electric Potential, Capacitance and dielectrics, Direct current circuits, Magnetic Fields and Sources, Faraday’s Law and Inductance, Continuous Distribution of Charge