Over the last century, Operations Research (O.R.) has transformed numerous public sector domains such as healthcare and defense. This talk dives into how O.R. models such as integer programming can benefit democratic processes, particularly in redrawing political district boundaries. In the U.S., state legislative and congressional district boundaries are redrawn every ten years to account for population shifts. The precise way these lines are drawn affects who will likely get elected and affects electoral representation. This talk presents optimization models and algorithms that incorporate fairness into this map-drawing process, motivated by well-established political science research. Beyond these models, this talk features a case study in Arizona that explores practical considerations. These considerations include trade-offs between redistricting criteria such as compactness and competitiveness, achieving near-equal population balance, and computational intractability when solving large input sizes. These models and methods serve as starting points for achieving fairness and transparency in future redistricting cycles where computers are expected to play a greater role than ever.

Rahul is an Operations Research and Data Science professional. He has a Ph.D. in Operations Research from the University of Illinois at Urbana-Champaign, an M.S. in Operations Research from SUNY Buffalo and a B.S. in Engineering Physics from the Indian Institute of Technology Madras. His Ph.D. thesis at UIUC explored political redistricting, a complex national problem with electoral consequences, through the lens of large-scale optimization modeling and heuristic design. He has prioritized practical implementation and mathematical rigor in his optimization work, resulting in publications such as Operations Research, INFORMS Journal on Optimization, Journal of Combinatorial Optimization, and INFORMS Journal on Applied Analytics. Presently, he is a Senior Data Scientist at Walmart Centroid, optimizing Walmart's large-scale supply chain and transportation strategy.