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Alif Jakir

Omnidisciplinary inventor

philosopher–scientist–futurist

Hi, I’m Alif—welcome to my site. My work sits at the intersection of artificial and organic intelligence: how minds arise in neural and machine substrates, and how we can design systems that amplify critical thinking and partnership with humans rather than substituting for them. The full layout, contact links, and research sections load with the interactive site.

Loading a bunch of web magic

Still faster than explaining this site out loud.

Interactive tutorial

Chapter-by-chapter study scaffold with runnable Python, multiple learning tracks, and OCW alignment

This tutorial follows Mathematics of Big Data (Kepner & Jananthan, MIT Press 2018) chapter by chapter. Each module includes learning objectives, key concepts, exercises from the book, and runnable Python cells that translate D4M/MATLAB examples into numpy + scipy.sparse + networkx so you can experiment directly in your browser via Pyodide. Lecture videos and slides from MIT OCW RES.LL-005 are cross-referenced where they align.

Acknowledgment: this interactive tutorial was built with substantial help from Anthropic’s Claude, which translated the book’s dense formal mathematics into runnable Python examples and structured each chapter into an approachable learning progression. Alif is not (yet) an expert on abstract algebra and semiring theory — any remaining errors are his own.

Pick a track below to see a curated chapter sequence, or browse organized by part.

  • Practitioner fast-path

    7 chapters — build something that runs

    Skip the proofs. Chapters 1-4 and 6-7 give you associative-array fluency, D4M schema design, graph algorithms via matrix multiply, and enough PageRank/SVD/DNN literacy to read GraphBLAS code. Add the D4M install (Session 0) and a GraphChallenge capstone and you can ship.

  • Full book journey

    All 16 chapters in order

    The complete arc from spreadsheet intuition through rigorous semiring algebra to eigenvalues on associative arrays. Designed for a semester-length reading group or a focused 4-week sprint. Every chapter, every exercise, every proof.

  • Math foundations deep-dive

    10 chapters — the algebra track

    For readers who want the abstract algebra: semirings, lattices, structural properties, semimodules, linearity, and eigenanalysis over semirings. Starts with Ch 1 for context, then Parts II and III minus the applications-heavy chapters.

  • Graphs & machine learning

    7 chapters — from edges to PageRank

    The graph-centric path: how to represent, construct, traverse, and analyze graphs using matrix operations — then bridge to centrality, SVD, PageRank, and DNNs. Includes the rigorous graph-construction chapter from Part II and eigenanalysis from Part III.

Before you start

  1. Get the book: MIT Press or Google Books preview
  2. Optionally install D4M (MATLAB/Octave/Julia) for the original code — all examples here also run as Python in the browser
  3. Bookmark OCW RES.LL-005 for the Fall 2012 lecture videos and IAP 2020 supplements

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