
Joseph S.B. Mitchell
Stony Brook University · Psychology
Active 1932–2026
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About
Joseph S.B. Mitchell is a SUNY Distinguished Professor in the Department of Applied Mathematics and Statistics at the State University of New York at Stony Brook. His research interests encompass computational geometry, algorithms and data structures, optimization, operations research, graphics and visualization, as well as computer-aided (geometric) design and manufacturing. He is associated with the AI Innovation Institute at Stony Brook, contributing to advancements in these fields through his academic and research activities.
Research topics
- Computer Science
- Artificial Intelligence
- Discrete mathematics
- Mathematics
- Mathematical analysis
- Computer vision
- Algorithm
- Programming language
- Combinatorics
- Geometry
Selected publications
Approximating Maximum Independent Set for Rectangles in the Plane
2022 · 17 citations
1st authorCorrespondingWe give a polynomial-time constant-factor approximation algorithm for maximum independent set for (axis-aligned) rectangles in the plane. Using a polynomial-time algorithm, the best approximation factor previously known is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(\log\log n)$</tex> . The results are based on a new form of recursive partitioning in the plane, in which faces that are constant-complexity and orthogonally convex are recursivel…
Computing Coordinated Motion Plans for Robot Swarms: The CG:SHOP Challenge 2021
ACM Journal of Experimental Algorithmics · 2022 · 8 citations
Senior authorCorrespondingWe give an overview of the 2021 Computational Geometry Challenge, which targeted the problem of optimally coordinating a set of robots by computing a family of collision-free trajectories for a set S of n pixel-shaped objects from a given start configuration to a desired target configuration.
Shortcut hulls: Vertex-restricted outer simplifications of polygons
Computational Geometry · 2023-01-19 · 2 citations
articleProvable Methods for Searching with an Imperfect Sensor
2025-05-19 · 1 citations
articleSenior authorAssume that a target is known to be present at an unknown point among a finite set of locations in the plane. We search for it using a mobile robot that has imperfect sensing capabilities. It takes time for the robot to move between locations and search a location; we have a total time budget within which to conduct the search. We study the problem of computing a search path/strategy for the robot that maximizes the probability of detection of the target. Considering non-uniform travel times bet…
Voluntary mobility clustering for epidemic control
2025-11-03
articleOpen accessIn case of a future pandemic, the mobility dynamics of a city can be controlled by intervening in the mobility patterns of people. Instead of hard quarantine policies, incentives can be designed that are compatible with people's preferences. At first, we distinguish mobility from the different types of locations for which distance matters. We match these types of locations in a way that maximizes the natural preference of people to visit the locations. We investigate different approaches for mat…
Recent grants
Algorithmic Studies in Applied Geometry
NSF · $200k · 2007–2010
AF: Small: Approximation Algorithms for Geometric Network Optimization
NSF · $451k · 2015–2019
NSF · $15k · 2015–2016
Frequent coauthors
- 219 shared
Esther M. Arkin
Hangzhou Dianzi University
- 85 shared
Valentin Polishchuk
- 83 shared
Sándor P. Fekete
Technische Universität Braunschweig
- 53 shared
Steven Skiena
- 43 shared
Michael A. Bender
Stony Brook University
- 35 shared
Alon Efrat
Alexandru Ioan Cuza University
- 33 shared
Martin Held
- 33 shared
Erik D. Demaine
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