projects
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u/adnanqidwai/projects.md

projects

A few projects I have worked on, grouped roughly by area.

AI Systems, RL and Agents

  • PatchGym

    Verifier-backed coding-agent environment that generates seeded Python repair tasks, runs agents in copied workspaces, and grades patches with public/hidden tests, API checks, and edit budgets.

  • RLM + GEPA for Retrieval

    Retrieval-policy harness where a DSPy RLM searches local Markdown documents, cites evidence, and is scored from tool traces. GEPA refines the written policy from answer, citation, and budget failures.

  • TinyRLVR

    Small verifiable reasoning lab for Countdown/24-game tasks, exact arithmetic checking, expression-tree policies, and a Q-learning controller for budgeted test-time compute.

  • LLM Compiler

    LangGraph tool-use agent that decomposes a query into dependent tool calls, runs independent calls in parallel, and joins the results before answering.

  • Voice RAG Tutor

    Voice-enabled learning tool that combines retrieval, agent routing, and text-to-speech for learning about music.

Compilers and systems

  • Nanopass Compiler for Racket

    Racket-to-x86 compiler organized as small lowering passes across staged IRs, with vectors/tuples, heap allocation, garbage collection, and register allocation.

  • Parallel t-Spanner Construction

    C++ graph-algorithms project for constructing sparse t-spanners and comparing sequential versus parallel construction behavior.

  • Eggshell

    Unix-style shell implementation with command execution and systems-programming primitives.

Core NLP Tasks

  • Neural POS Taggers

    Part-of-speech taggers built with feed-forward and recurrent neural models for sequence-labeling experiments.

  • NLP Course Exercises

    Assignments from Stanford 224n covering word vectors, neural classifiers, and sequence models.

Classical ML

  • K-Means from Scratch

    K-means clustering from scratch on a football dataset, including centroid updates and cluster assignments.

  • Multiclass SVMs

    Multiclass SVMs comparing one-vs-rest and one-vs-one classification strategies.

  • Random Forest from Scratch

    Random forest classifier from scratch over penguin data, covering bootstrapping, feature splits, and tree-vote aggregation.

  • Clustering Algorithm Comparison

    Comparison of clustering algorithms across compact, skewed, subclustered, and well-separated datasets.

Data mining and analytics