Shree Singhal

Portfolio

Shree Singhal

I am drawn to all things Computer and Software Engineering, especially embedded systems, algorithm design, high-performance computing, and artificial intelligence.

My experience spans closed-loop embedded control systems, hardware-in-the-loop development, HPC development, RISC-V/MIPS processor design, and reinforcement learning.

πŸŽ“ B.S. Computer Engineering and Computer Science, Northeastern University 2026

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Projects

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ReNU wind turbine at competition.

ReNU - Collegiate Wind Turbine Software + Electrical Lead

Led a 6-person team, designing the full power and control stack: 3-phase full-wave rectifier, MPPT load control, and a closed-loop PID pitch control system driven by tachometer feedback. Regulated rotor speed across 5–15 mph wind. Placed 4th of 32 teams at the DOE Collegiate Wind Turbine Competition 2026.

  • Embedded
  • PID Control
  • MPPT
  • Power Electronics
  • Team Lead
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Ambient backscatter prototype board and antenna.

Ambient Backscatter Wireless Communication

PEAK Award-funded independent research on a backscatter system that modulates ambient Wi-Fi signals for passive data transmission. Designed RF filtration, a custom encoding/decoding protocol, and a custom antenna. Mentored by Dr. Stefano Basagni and featured in the NU COE Spotlight.

  • RF
  • Wireless
  • Antenna Design
  • Research
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Drone tracking + laser turret capstone rig.

Drone Tracking In-Air Laser Charging Capstone

Three-layer computer vision pipeline combining GroundingDINO bounding boxes, segmentation masks, and CoTracker3 point tracking to keep focus on an onboard photodiode target. Drives a closed-loop PID turret actuation system that aligns laser placement with live tracking coordinates.

  • Computer Vision
  • GroundingDINO
  • CoTracker3
  • PID
  • Capstone
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Project Mitosis delta 3D printer build.

Project Mitosis β€” 3D-Printed Delta 3D Printer

NURobotics project building a delta 3D printer from scratch. Designed and software-tested a CadLab PCB that interfaces an STM32 with stepper motor drivers, and wrote C++ firmware that synchronizes three steppers to execute parallel kinematic motion.

  • STM32
  • C++ Firmware
  • PCB Design
  • Robotics
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DQN vs tabular Q-learning training curves.

DQN vs Q-Learning Market Making Agent

Designed and trained two RL agents (tabular Q-Learning and Double DQN) to act as market makers in a custom trading environment. Engineered a reward function balancing profit against inventory risk over a 25-action bid/ask offset space. DQN delivered ~4Γ— higher profit (680.71 vs 169.77) at ~20Γ— the compute cost, quantifying the tabular-vs-deep-RL tradeoff.

  • Reinforcement Learning
  • DQN
  • Python
  • Quant
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Literature review on high-performance computing for FFTs.

Literature Review: Progression of the Use of High-Performance Computing for Fast Fourier Transforms from 2005-2025

Surveyed and synthesized 12 research papers spanning 2005–2025 to trace how Fast Fourier Transform implementations have evolved alongside HPC hardware.

  • HPC
  • FFT
  • Literature Review
  • Research
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RISC-V datapath waveform / FUSE filesystem layout.

Embedded Design β€” FUSE FS & RISC-V Datapath

Two systems-level projects: a Unix-style FUSE file system in C++ with block-storage architecture, nested directories, bitmap resource tracking, and file I/O syscall handling; and a full CPU datapath implemented in SystemVerilog/Verilog using both RISC-V and MIPS instruction sets.

  • C++
  • SystemVerilog
  • RISC-V
  • MIPS
  • Operating Systems

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Experience

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Skills

Languages

  • C++
  • Python
  • Java
  • SystemVerilog
  • Verilog
  • MATLAB
  • Kotlin
  • Racket

Embedded & Hardware

  • RISC-V Design
  • STM32CubeIDE
  • Arduino
  • KiCAD
  • Altium
  • LT SPICE
  • Xilinx
  • Quartus Prime Lite

HPC & Systems

  • Slurm
  • CUDA
  • Linux / WSL
  • Git
  • Bootstrapping & Sensitivity Analysis

Methods & ML

  • Reinforcement Learning (DQN / Q-Learning)
  • Computer Vision (GroundingDINO, CoTracker3)
  • PID Control
  • Object-Oriented Design
  • Agile / SCRUM