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rayedkhan/README.md

rayed khan

Computer engineering, NYU Abu Dhabi. Agentic AI and automation at PitchMatter, in Dubai.

Before that: digital systems at MRF, enterprise security at Dolphin Energy, and deep learning research at the eBRAIN Lab.

Most of the work here is written from the primitives. Gradients, update rules and optimizers put down by hand rather than imported: regression, logistic regression, the SVM, the decision tree, the neural network, the convolution. One at a time, each one to find out what it actually does.

The hardware runs alongside it. An AES key pulled from power traces, a pipelined CPU built twice, a scan chain locked and then picked apart, a tremor detector living inside 2 KB of RAM.

rayed@nyu.edu

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  1. zero-shot-object-navigation zero-shot-object-navigation Public

    Reproducing VLTNet, an LLM-guided zero-shot object navigation method, in AI2-THOR, and finding that its headline metric measures movement rather than task success

    Python 4

  2. anime-face-gan anime-face-gan Public

    DCGAN trained from scratch on 63,000 anime faces in PyTorch, with every epoch's samples saved

    Jupyter Notebook

  3. mips-pipelined-cpu mips-pipelined-cpu Public

    A 32-bit MIPS five-stage pipeline in Verilog, built twice: once resolving data hazards by forwarding, once by stalling

    Verilog

  4. aes-cpa-attack aes-cpa-attack Public

    Correlation power analysis that recovers a full AES-128 key from 50 power traces in 4.3 seconds

    Jupyter Notebook

  5. parkinsonian-tremor-detector parkinsonian-tremor-detector Public

    Rest-tremor detector running entirely on an Adafruit Circuit Playground Classic: 50 Hz sampling, a 128-point FFT, and a live readout of the 3-6 Hz band

    C++