Ali Kemal Sinop
Contact Information:
Princeton University,
Department of Computer Science,
Office 103A,
35 Olden St,
Princeton, NJ 08540.
Email: a...@cs.cmu.edu
I am currently a postdoctoral researcher in theoretical computer science at
Center for Computational Intractibility, Princeton University
and
Institute for Advanced Study.
I received my PhD from
Carnegie Mellon University, Computer Science Department where I was
fortunate to be
advised by Prof. Venkatesan Guruswami.
My research interests are in theoretical computer science. More specifically I am interested in:
- hardness of approximation,
- approximation algorithms,
- spectral graph theory and linear algebra.
Prior to coming to CMU, I worked at Siemens Corporate Research
for two years under supervision of Dr. Leo Grady
doing research on graph based algorithms for image segmentation.
I received my M.S. degree from University of Pennsylvania in Philadelphia, Pennsylvania
in Computer and Information Science and my B.S. from
Bilkent University, Ankara, Turkey.
Last but not least, my hometown is Meram, Dere in city of Konya, Turkey .
Educational Background
Teaching
I was a teaching assistant for the following courses:
Recent Publications (2009-present)
-
Approximating Non-Uniform Sparsest Cut via
Generalized Spectra.
-
Faster SDP Hierarchy Solvers for Local Rounding Algorithms.
-
Constant Factor Lasserre Gaps for Graph Partitioning Problems.
-
Optimal Column-Based Low-Rank Matrix Reconstruction.
-
Lasserre Hierarchy, Higher Eigenvalues, and
Approximation Schemes for Quadratic Integer
Programming with PSD Objectives.
-
The complexity of finding independent sets in bounded degree (hyper)graphs of low chromatic number.
-
Improved inapproximability results for maximum k-colorable subgraph.
-
Combinatorial Preconditioners and Multilevel Solvers for Problems in Computer Vision and Image Processing.
Not So Recent Publications
-
Fast Approximate Random Walker Segmentation Using Eigenvector Precomputation.
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A Seeded Image Segmentation Framework Unifying Graph Cuts and Random Walker Which Yields A New Algorithm.
-
Uninitialized, Globally Optimal, Graph-Based Rectilinear Shape Segmentation - The Opposing Metrics Method.
-
Accurate Banded Graph Cut Segmentation of Thin Structures Using Laplacian Pyramids.
Some Friends on the Web
Anshul Gandhi, Ravishankar Krishnaswamy,
Bodicherla Aditya Prakash, Dafna Shahaf,
Yuan Zhou