[Podcast] Data Science Based Decisions: Mixed-Integer Programming

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[Podcast] Data Science Based Decisions: Mixed-Integer Programming

This latest Data Science Central podcast traces the history of mixed-integer programming (MIP), noting several parallels with machine learning. In the process, we briefly discuss the impact that MIP has had on a number of application domains and why this powerful technology should be a part of every data scientist’s analytics toolkit.

Hosted by:

Rafael Knuth, Contributing Editor – Data Science Central

Meet the Experts

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Dr. Edward Rothberg

Chief Scientist and Chairman of the Board

Dr. Edward Rothberg

Chief Scientist and Chairman of the Board

Dr. Rothberg has served in senior leadership positions in optimization software companies for more than twenty years. Prior to his role as Gurobi CEO, Dr. Rothberg held the Gurobi COO position since co-founding Gurobi in 2008, and prior to that he led the ILOG CPLEX team. Dr. Edward Rothberg has a BS in Mathematical and Computational Science from Stanford University, and an MS and PhD in Computer Science, also from Stanford University. Dr. Rothberg has published numerous papers in the fields of linear algebra, parallel computing, and mathematical programming. He is one of the world's leading experts in sparse Cholesky factorization and computational linear, integer, and quadratic programming. He is particularly well known for his work in parallel sparse matrix factorization, and in heuristics for mixed integer programming.

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[Podcast] Data Science Based Decisions: Mixed-Integer Programming

Hosted by:

Meet the Experts

Dr. Edward Rothberg

Dr. Edward Rothberg

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