Algorithms For Big Data Compsci 229r Lecture 23 Algorithms For Big Data Compsci 229r Lecture 23

Algorithms For Big Data Compsci 229r Lecture 23 Algorithms For Big Data Compsci 229r Lecture 23 Net Worth & Biography

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External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting. Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris' Amnesic dynamic programming (approximate distance to monotonicity). Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing. Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression. ℓ1/ℓ1 recovery, RIP1, unbalanced expanders, Sequential Sparse Matching Pursuit.

ORS theorem (distributional JL implies Gordon's theorem), sparse JL. Communication complexity (indexing, gap hamming) + application to median and F0 lower bounds. Krahmer-Ward proof, Iterative Hard Thresholding. Randomized and approximate F0 lower bounds, disjointness, Fp lower bound, dimensionality reduction (JL lemma). MapReduce: TeraSort, minimum spanning tree, triangle counting. Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ...

Estimated Worth: $48M - $70M

Salary & Income Sources

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Khintchine, decoupling, Hanson-Wright, proof of distributional JL lemma. Necessity of randomized/approximate guarantees, linear sketching, AMS sketch, p-stable sketch for p less than 2. Approximate matrix multiplication with Frobenius error via sampling / JL, matrix median trick, subspace embeddings. CountSketch, ℓ0 sampling, graph sketching.

Career Highlights & Achievements

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Algorithms for Big Data (COMPSCI 229r), Lecture 11 Net Worth
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Assets, Properties & Investments

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Last Updated: May 15, 2026

Net Worth Outlook & Future Earnings

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Disclaimer: Disclaimer: Net Worth estimates are based on publicly available data, media reports, and financial analysis. Actual numbers may vary.