Selected Publications

The image segmentation problem is to delineate, or segment, a salient feature in an image. As such, this is a bipartition problem with the goal of separating the foreground from the background. An NP-hard optimization problem, the Normalized Cut problem, is often used as a model for image segmentation. The common approach for solving the normalized cut problem is the spectral method which generates heuristic solutions based upon finding the Fiedler eigenvector. Recently, Hochbaum (IEEE Trans Pattern Anal Mach Intell 32(5):889–898, 2010) presented a new relaxation of the normalized cut problem, called normalized cut ‘ problem, which is solvable in polynomial time by a combinatorial algorithm. We compare this new algorithm with the spectral method and present experimental evidence that the combinatorial algorithm provides solutions which better approximate the optimal normalized cut solution. In addition, the subjective visual quality of the segmentations provided by the combinatorial algorithm greatly improves upon those provided by the spectral method. Our study establishes an interesting observation about the normalized cut criterion that the segmentation which provides the subjectively best visual bipartition rarely corresponds to the segmentation which minimizes the objective function value of the normalized cut problem. We conclude that modeling the image segmentation problem as normalized cut criterion might not be appropriate. Instead, normalized cut ’ not only provides better visual segmentations but is also solvable in polynomial time. Therefore, normalized cut ‘ should be the preferred segmentation criterion for both complexity and good segmentation quality reasons.
EURO J. Comput. Optim., 2013

Recent Publications

. Evaluating performance of image segmentation criteria and techniques. EURO J. Comput. Optim., 2013.

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While at UC Berkeley, I have served as a Graduate Student Instructor for a number of undergraduate (100 series) and graduate (200 series) courses:

  • IEOR 150 Production Systems Analysis, Prof Yano, Fall 2020
  • IEOR 153 Logistics Network Design and Supply Chain Management, Instructor Olfat, Spring 2020
  • IEOR 150 Production Systems Analysis, Prof Yano, Fall 2019
  • IEOR 153 Logistics Network Design and Supply Chain Management, Prof Ozsen, Spring 2019
  • IEOR 150 Production Systems Analysis, Prof Yano, Fall 2018
  • IEOR 240 Optimization Analytics, Prof Adler, Fall 2017
  • UBGA 141 Introduction to Operations Management, Prof Yano, Spring 2014
  • IEOR 160 Operations Research I, Prof Hochbaum, Fall 2013

In Spring 2014, I was awarded the IEOR department’s Oustanding GSI award.