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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.

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Mathematics of Big Data: Spreadsheets, Databases, Matrices, and Graphs

Mathematical supercomputing with D4M: what I took away from IAP 2023 with Jeremy Kepner and Hayden Jananthan

  • Interactive tutorial (16 chapters, 4 tracks, Python cells)

    Chapter-by-chapter study scaffold with runnable Python code (Pyodide), multiple learning tracks (practitioner, full book, math foundations, graph & ML), OCW alignment, and exercises. Start here.

  • Original study scaffold

    The informal IAP-style reading rhythm: D4M install, OCW RES.LL-005, GraphBLAS bridge, and a small GraphChallenge-style capstone.

  • Why associative arrays / D4M

    One mathematical object behind spreadsheets, stores, matrices, and graphs, and why that unification changes how you engineer at scale.

  • Kepner & Jananthan

    Lincoln Lab supercomputing, GraphBLAS, Julia, logic, and the story of the MIT Press book, with primary links.

  • Supercomputing context

    LLSC scale, TOP500 history, sorting benchmarks, sparse DNN challenge: the public record on why bench culture and math stack together.

  • Resource compendium

    MIT Press artifacts, full OCW mirror, software, GraphChallenge, facilities, and adjacent programs, in one systematic link list.

In January IAP 2023 I studied Mathematics of Big Data and Machine Learning with Jeremy Kepner and Hayden Jananthan. They gave me a complimentary copy of Mathematics of Big Data (MIT Press, 2018; foreword by Charles E. Leiserson), and we worked through the book in step with class, roughly a chapter per meeting, from spreadsheet-shaped intuition to associative arrays, sparse structure, graphs, and the linear-algebraic language that large-scale systems share. MIT Mathematics' IAP 2023 listing includes their session in the 18.095 Mathematics Lecture Series (January 11, 2023). For me the shift was methodological: moving from “yet another toolchain” to the foundations that survive when data is distributed, sparse, and heterogeneous. It is the same through-line you see in national-lab supercomputing and in projects like D4M.