Alon Efrat — Curriculum Vitae

1 Education

1998 Ph.D. Computer Science, Tel-Aviv University, IsraelThesis: Optimal Geometric Location ProblemsAdvisor: Prof. Micha Sharir

1993 M.Sc. Computer Science, The Technion, IsraelThesis: Maintaining the Smallest Enclosing CircleAdvisors: Prof. Alon Itai and Prof. Reuven Bar-Yehuda

1991 B.Sc. Applied Mathematics, Mathematics Department, The Technion, Israel

2 Professional Experience

2000 - present Assistant Professor, Computer Science Department, University of Arizona2000 - 2001 Visiting Researcher and Consultant, IBM Almaden Research Center1999 - 2000 Post Doctorate Research Fellow (Sponsor: Leonidas Guibas), Stanford University1994 - 1998 Instructor, Computer Science Department, Tel-Aviv University, The Technion, Israel

Areas of Scientific Activities: Geometric Algorithms and their applications. Theoretical Computer Science.

3 Honors and Awards

1996 The Maus prize for distinguished graduate students, Tel-Aviv University, $30001998 The Rothschild Fellowship, Israel (Post Doctorate Fellowship), $24,000.1998 The Minerva Fellowship Israel—Germany (Post Doctorate Fellowship).1998 The Chateaubriand Fellowship, French (Post Doctorate Fellowship).1998 The Pacific Institute for the Mathematical Sciences,

4 Services and Outreach

4.1 Service to the scientific community

• Program Committee member of the IEEE (Institute of Electrical and Electronics Engineers), Inter-national Conference on Communications, Wireless Adhoc and Sensor Networks Symposium (WASICC) 2007.

• Editorial board member of the International Journal of Computational Geometry and Applications(IJCGA) since 2006.

• Program Committee member of the IEEE 3rd Int. conf. on Broadband Communication, Networksand systems (BROADNET) 2006.

• Conference local chair of the 22nd ACM (Association for Computing Machinery) Symposium ofComputational Geometry (SoCG), Sedona, Arizona 2006.

• Together with Subhash Suri from The University of Santa-Barbara and Leo Guibas from StanfordUniversity, we organized the NSF Workshop on Geometric Approaches to Ad Hoc and SensorNetworks, an NSF-funded workshop aiming to bring together leading researchers of Ad Hoc andSensor Networks and those of computational geometry, computational topology, and combinatorialoptimization. June 2006.

• Program Committee member for the 21st. ACM Symposium of Computational Geometry (SoCG)2005.

• Guest editor of the special issue of the International Journal of Computational Geometry and Appli-cations (IJCGA) dedicated to selected papers from the 21st. ACM Symposium of ComputationalGeometry (SoCG) 2005.

• NSF panel review, 2005, (theory panel).

• NSF panel review, 2006 (twice on networking panels, and once for a theory panel).

• Referee reports:

Referee reports for journals: Discrete and Computational Geometry, Combinatorica, Infor-mation Processing Letters, Computational Geometry: Theory and Applications, InternationalJournal of Computational Geometry and Applications, Journal of Algorithms, IEEE (Insti-tute of Electrical and Electronics Engineers) Transactions on Pattern Analysis and MachineIntelligence, IEEE Transactions on Robotics and Automation, IIE Transactions: OperationsEngineering, and many others.

Referee reports for conferences: Annual ACM (Association for Computing Machinery) SIAM(Society of for Industrial and Applied Mathematics) Symposium on Discrete Algorithms, IEEESymposium on Foundations of Computer Science, ACM Annual Symposium on ComputationalGeometry. Workshop on Foundation of Robotics, and many others.

4.2 Service to the Department

• Served on Computing Committee, Graduate Admissions Committee and Graduate Affairs com-mittee.

• Initiated and generated several courses. See more details in my evaluation of teaching and advisingstatement.

4.3 Service to the College

• Grade Appeal Committee.

• Various dissertations and thesis committees.

5 Publications

A. Patents

1. Buddy Tracking — A system for proximity alerts between friends. US 2004/0198398.

B. Papers in Journals

2. *P.K. Agarwal, A. Efrat, M. Sharir and S. Toledo, Computing a segment-center for a planar point set, J.Algorithms, 15 (1993), 314–323.

3. *A. Efrat, M. Sharir and G. Rote, On the union of fat wedges and separating a collection of segments by aline, Computational Geometry: Theory and Applications (CGTA), 3 (1994), 277–288.

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4. *A. Efrat and C. Gotsman, Subpixel image registration using circular fiducials, International J. of Compu-tational Geometry and Applications (IJCGA), 4 (1994), 403–422.

5. *A. Efrat, M. Sharir and A. Ziv, Computing the smallest k-enclosing circle and related problems, Computa-tional Geometry: Theory and Applications (CGTA), 4 (1995), 119–136.

6. *A. Efrat and M. Sharir, A near-linear algorithm for the planar segment center problem, Discrete and Com-putational Geometry (DCG), 16 (1996), 239–257.

7. *A. Efrat and O. Schwarzkopf, Separating and shattering long line segments, Information Processing Letters(IPL), 64 (1998), 309–314.

8. *L.P. Chew, D. Dor, A. Efrat and K. Kedem, Geometric pattern matching in d-dimensional space, Discreteand Computational Geometry (DCG), 21 (1999), 257–274.

9. *A. Efrat and M.J. Katz, On the union of κ-curved objects, Computational Geometry: Theory and Applica-tions (CGTA), 14 (1999), 241–254.

10. *A. Efrat and M. Sharir, On the complexity of the union of fat objects in the plane, Discrete and Computa-tional Geometry (DCG), 23 (2000), 171–189.

11. *P.K. Agarwal, A. Efrat and M. Sharir, Vertical decomposition of shallow levels in 3-dimensional arrangementsand its applications, SIAM J. Computing 29 (2000), 912–953.

12. *A. Efrat, M.J. Katz, F. Nielsen and M. Sharir, Dynamic data structures for fat objects and their applications,Computational Geometry: Theory and Applications (CGTA), 15 (2000), 215–227.

13. *A. Efrat and M.J. Katz, Computing an Euclidean bottleneck matching in higher dimension, InformationProcessing Letters (IPL), 4 (2000), 169–174.

14. *A. Efrat, M. J. Katz and A. Itai, Geometry helps in bottleneck matching and related problems, Algorithmica1 (2001), 1–28.

15. A. Amir, A. Efrat, P. Indyk and H. Samet, Efficient algorithms and regular data structures for dilation,location and proximity problems, Algorithmica, 30 (2001), 166–187.

16. *B. Aronov, A. Efrat, D. Halperin and M. Sharir, A subquadratic bound on the number of regular verticesof the union of Jordan regions, Discrete and Computational Geometry (DCG), 25 (2001), 203–220.

17. *T. Chan and A. Efrat, Fly cheaply: on the minimum fuel-consumption problem, J. Algorithms, 41 (2001),330–337.

18. N. Davis, K. Cheverst, K. Mitchell and A. Efrat, Using and determining location in a context-sensitive tourguide: the guide experience, IEEE computers, 34 (2001), 35–41.

19. A. Efrat, F. Hoffmann, K. Kriegel, C. Schultz and C. Wenk, Geometric algorithms for the analysis of 2D-Electrophoresis gels, Journal of Computational Biology (JCB), (special issue dedicated to papers from RE-COMB 2001), 9 (2002), 299–316.

20. A. Efrat, L. J. Guibas, S. Har-Peled, J. S. B. Mitchell and T.M. Murali, New similarity measures between poly-lines with applications to morphing and polygon sweeping, Discrete and Computational Geometry (DCG),28 (2002), 535–569.

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21. A. Efrat, F. Hoffmann, K. Kriegel, C. Knauer, G. Rote and C. Wenk, Covering shapes by ellipses Algorithmica(special issue on shape algorithms), 38 (2003), 145–160.

22. A. Amir and S. Srinivasan, Search the audio, browse the video — a generic paradigm for video collections,EURASIP Journal on Applied Signal Processing, 2 (2003), 209–222.

23. H. Alt, A. Efrat, G. Rote and C. Wenk, Matching planar maps, J. Algorithms 49 (2003), 262–283.

24. A. Efrat, The Complexity of the union of (α, β)-covered objects, SIAM J. Computing, 34 (2005), 755–787.

25. A. Efrat, P. Indyk and S. Venkatasubramanian, Pattern matching for sets of segments, Algorithmica, 40(2004), 147-160.

26. A. Efrat, S. Kobourov and A. Lubiw, Computing homotopic shortest paths efficiently, Computational Geom-etry: Theory and Applications (CGTA), to appear.

27. B. Aronov, A. Efrat, V. Koltun and M. Sharir, On the union of κ-round objects in three and four dimensions,Discrete and Computational Geometry (DCG), (special issue dedicated to selected papers of Proc. 20thAnnual Symposium on Computational Geometry (SoCG)), to appear.

28. P. Brass, E. Cenek, C. A. Duncan, A. Efrat, C. Erten, D. Ismailescu, S. G. Kobourov, A. Lubiw and J. S.B. Mitchell, On simultaneous, planar graph embeddings, Computational Geometry: Theory and Applications(CGTA), to appear.

29. A. Efrat, L.J. Guibas and O.A. Hall-Holt and L. Zhang, On incremental rendering of silhouette maps of apolyhedral scene, Computational Geometry: Theory and Applications (CGTA), to appear.

30. O. Cheong, A. Efrat and S. Har-Peled, On finding a guard that sees most and a shop that sells most, Discreteand Computational Geometry (DCG), to appear.

31. A. Efrat, C. Erten and S. Kobourov, Fixed-location circular arc drawings, Journal of Graph Algorithms andApplications, to appear.

32. A. Efrat and S. Har-Peled, Locating guards in art galleries, Information Processing Letters (IPL), to appear.

33. C. A. Duncan, A. Efrat, S. G. Kobourov and C. Wenk, Drawing with fat edges, International Journal ofFoundations of Computer Science (IJFCS), (special issue dedicated to selected papers from Graph Drawing),to appear.

34. A. Amir, A. Efrat, J. Myllymaki, L. Palaniappan and K. Wampler, Buddy tracking— efficient proximitydetection among mobile friends, Pervasive and Mobile Computing, to appear after revision.

35. A. Efrat, Q. Fan and S. Venkatasubramanian, Curve matching, time warping, and light fields, new algorithmsfor computing similarity between curves, J. Mathematic Imaging and Vision, to appear.

36. Y.T. Hou, Y. Shi, J. Pan, A. Efrat, and S.F. Midkiff, Maximizing lifetime of wireless sensor networks throughoptimal single-session flow routing, IEEE Transactions on Mobile Computing, to appear.

37. R. Kraft, M. Escobar, M. Narro, J. Kurtis, A. Efrat, K. Barnard, and L. Restifo, Phenotypes of drosophilabrain neurons in primary culture reveal a role for fascin in neurite shape and trajectory, the Journal ofNeuroscience, to appear.

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38. E. Ezra, M. Sharir and A. Efrat, On the ICP Algorithm, Computational Geometry: Theory and Applications(CGTA), to appear.

C. Papers in Proceedings of Competitive Peer-Reviewed Conferences

39. *A. Efrat, M. Sharir and A. Ziv, Computing the smallest k-enclosing circle and related problems, Proc. 3rdWorkshop Algorithms Data Structures (WADS) 1993, LNCS (Lecture Notes in Computer Science) vol. 709,325–336.

40. *A. Efrat and C. Gotsman, Subpixel image registration using circular fiducials, Proc. of the 2nd IsraeliSymposium on Theory of Computing and Systems 1994, 127–136.

41. *A. Efrat and M. Sharir, Near-linear algorithm for the planar segment center problem, Proc. 5th AnnualACM-SIAM Symposium on Discrete Algorithms (SODA) 1994, 87–97.

42. *P.K. Agarwal, A. Efrat and M. Sharir, Vertical decomposition of shallow levels in 3-dimensional arrangementsand its applications, Proc. 11th Annual Symposium on Computational Geometry (SoCG), 1995, 39–50.

43. *L.P. Chew, D. Dor, A. Efrat and K. Kedem, Geometric pattern matching in d-dimensional space, 3rdEuropean Symposium on Algorithms (ESA), LNCS vol. 979, 1995, 264–279.

44. *A. Efrat and A. Itai, Improvements on bottleneck matching and related problems, using geometry, Proc.12th Annual Symposium on Computational Geometry (SoCG), 1996, 301–310.

45. *A. Efrat and M.J. Katz, Computing most-uniform and minimum deviation matchings in geometric settings,Proc. 7th Annual International Symposium on Algorithms and Computational (ISAAC), 1996, 115–125.

46. *A. Efrat and O. Schwarzkopf, Separating and shattering long line segments, Proc. 7th Annual InternationalSymposium on Algorithms and Computational (ISAAC), 1996, 36–44.

47. *A. Efrat and M. Sharir, On the complexity of the union of fat objects in the plane, Proc. 13th AnnualSymposium on Computational Geometry (SoCG), 1997, 104–112.

48. *A. Efrat, M.J. Katz, F. Nielsen and M. Sharir, Dynamic data structures for fat objects and their applications.Proc. 5th Workshop on Algorithms and Data Structures (WADS), 1997, LNCS vol. 1272, 297–396.

49. *A. Efrat and M.J. Katz, On the Union of α-curved Objects, Proc. 14th Annual Symposium on ComputationalGeometry (SoCG), 1998, 206–213.

50. *A. Efrat and S. Har-Peled, Fly Cheaply: on the Minimum fuel-consumption problem, Proc. 14th AnnualSymposium on Computational Geometry (SoCG), 1998, 143–145.

51. *B. Aronov, A. Efrat, D. Halperin and M. Sharir, A subquadratic bound on the number of regular verticesof the union of Jordan regions. Proc. 6th Scand. Workshop on Algorithms Theory (SWAT), 1998, 322–334.

52. A. Efrat, The complexity of the union of (α, β)-covered objects, Proc. 15th Annual Symposium on Computa-tional Geometry (SoCG), 1999, 134–142.

53. A. Amir, A. Efrat, P. Indyk and H. Samet, Efficient algorithms and regular data structures for dilation,location and proximity problems, Proc. 40 Annual IEEE Symposium on Foundations of Computer Science(FOCS), 1999, 160–170.

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54. H. H. Gonzalez-Banos, E. Mao, J.C. Latombe, T. M. Murali and A. Efrat, Planning robot motion strategiesfor efficient model construction, Proc. 9th International Symposium of Robotics Research, 2000, 345-352.

55. A. Efrat, L.J. Guibas, O.A. Hall-Holt and L. Zhang, On incremental rendering of silhouette maps of apolyhedral scene, Proc. 11th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 2000, 910–917.

56. A. Efrat, L.J. Guibas, S. Har-Peled, D.C. Lin, J.S.B. Mitchell, T.M. Murali, Sweeping simple polygons with achain of guards, Proc. 11th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 2000. 297–936.

57. A. Efrat, P. Indyk and S. Venkatasubramanian, Pattern matching for sets of segments, Proc. 12th AnnualACM-SIAM Symposium on Discrete Algorithms (SODA), 2001, 295–304.

58. A. Efrat, S. Har-Peled, L. Guibas and T.M. Murali, Morphing between curves, Proc. 12th Annual ACM-SIAMSymposium on Discrete Algorithms (SODA), 2001, 680–689.

59. A. Efrat, F. Hoffmann, K. Kriegel, C. Schultz and C. Wenk, Geometric algorithms for the analysis of 2D-Electrophoresis gels, Proc. 5th Annual International Conference on Computational Molecular Biology (RE-COMB), 2001, 114-123.

60. C. A. Duncan, A. Efrat, S. G. Kobourov and C. Wenk, Drawing with Fat Edges, Proc. 9th Graph Drawing(GD) 2001, LNCS vol. 2265, 162–177.

61. A. Amir, A. Efrat and S. Srinivasan, Developments in phonetic word retrieval, ACM Tenth InternationalConference on Information and Knowledge Management (CIKM) 2001 , 580–582.

62. A. Efrat, F. Hoffmann, K. Kriegel, C. Knauer, G. Rote and C. Wenk, Covering shapes by ellipses Proc. 13thAnnual ACM-SIAM Symposium on Discrete Algorithms (SODA), 2002, 453–454.

63. A. Efrat and S. Har-Peled, Locating guards in art galleries, 2nd International Conference on TheoreticalComputer Science (IFIP), 2002, 181–192.

64. A. Efrat, S. Kobourov and A. Lubiw, Computing homotopic shortest paths efficiently, European Symposiumon Algorithms (ESA), 2002, 411–423.

65. H. Alt, A. Efrat, G. Rote and C. Wenk, Matching planar maps, Proc. 14th Annual ACM-SIAM Symposiumon Discrete Algorithms (SODA), 2003, 589–592.

66. A. Efrat, H. H. Gonzalez-Banos, S. Kobourov and L. Palaniappan, Optimal motion strategies to track andcapture a predictable target, IEEE International Conference on Robotics and Automation, 2003.

67. M. Dror, A. Efrat, A. Lubiw and J. S. B. Mitchell, Touring a sequence of polygons, ACM Symposium onTheory of Computing (STOC) 2003, 473–482.

68. P. Brass, E. Cenek, C. A. Duncan, A. Efrat, C. Erten, D. Ismailescu, S. G. Kobourov, A. Lubiw and J. S. B.Mitchell, On simultaneous, planar graph embedding, Workshop on Data Structures (WADS) 2003, LNCSvol. 2748, 243–255.

69. A. Efrat, C. Erten and S. Kobourov, Fixed-location circular-arc drawing of planar graphs, Proc. 11th GraphDrawing (GD), 2003, LNCS vol. 2912, 147–158..

70. O. Cheong, A. Efrat and S. Har-Peled, On finding a guard that sees most and a shop that sells most, Proc.15th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 2004, 1098–1107.

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71. A. Amir, A. Efrat, J. Myllymaki, L. Palaniappan and K. Wampler, Buddy tracking — efficient proximitydetection among mobile friends, 23rd Conference of the IEEE Communications Society (INFOCOM) 2004.

72. J. Arango, M. Degermark, A. Efrat and S. Pink, An efficient flooding algorithm for mobile Ad-hoc networks,Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt), 2004.

73. B. Aronov, A. Efrat, V. Koltun and M. Sharir, On the union of κ-round objects in three and four dimensions,Proc. 20th Annual Symposium on Computational Geometry (SoCG), 2004, 383–390.

74. Q. Fan, A. Efrat, V. Koltun, S. Krishnan and S. Venkatasubramanian, Hardware-assisted natural neighborinterpolation, Workshop on Algorithm Engineering and Experiments (ALENEX) 2005.

75. A. Efrat, S. Har-Peled and J. Mitchell, Approximation algorithms for two optimal location problems in sensornetworks, IEEE 2nd Int. conf. on Broadband Communication, Networks and systems (BROADNET), 2005.

76. A. Iyer, A. Efrat, C. Erten, D. Forrester and S. Kobourov, Force-directed approaches to sensor localization,Workshop on Algorithm Engineering and Experiments (ALENEX) 2006.

77. E. Ezra, M. Sharir and A. Efrat, On the ICP Algorithm, Proceedings 22nd Annual Symposium on Computa-tional Geometry 2006, 95–104.

78. J. Arango, A. Efrat, S. Ramasubramanian and M. Krunz, Retransmission and back-off strategies for broad-casting in multi-hop wireless networks, IEEE 3rd Int. conf. on Broadband Communication, Networks andsystems (BROADNET) 2006.

79. Y. Shi, Y. T. Hou, and A. Efrat, Algorithm Design for Base Station Placement Problems in Sensor Networks,3rd Int. Conf. on Quality of Service in Heterogeneous Wired/Wireless Networks 2006, to appear.

80. J. Arango, A. Efrat, M. Krunz and S. Ramasubramanian, Onroad vehicular broadcast, 15th InternationalConference on Computer Communications and Networks (ICCCN) 2006, to appear.

81. A. Efrat, R. Balasubramanian and S. Ramasubramanian, Coverage time optimization in sensor networks, 3rdIEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS’06) 2006, to appear.

82. Q. Fan, A. Amir, K, Barnard, A. Efrat, and L. Ming, Matching slides to presentation videos, ACM Interna-tional Workshop on Multimedia Information Retrieval (SIGMM), 2006, to appear.

83. A. L. Buchsbaum, A. Efrat S. Jain and S. Venkatasubramanian, Restricted strip covering and the sensor coverproblem, Proc. 18th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 2007, to appear.

D. Papers in Non-Competitive Venues

84. *R. Bar-Yehuda, A. Efrat and A. Itai, A simple algorithm for maintaining the center of a planar point-set,Proc. 5th Canad. Conf. Comput. Geom. (CCCG), 1993, 252–257.

85. *A. Efrat, M. Lindenbaum and M. Sharir, Finding maximally consistent sets of halfspaces, Proc. 5th Canad.Conf. Comput. Geom. (CCCG), 1993, 432–436.

86. *A. Efrat, M. Sharir and G. Rote, On the union of fat wedges and separating a collection of segments by aline, Proc. 5th Canad. Conf. Comput. Geom. (CCCG), 1993, 115–120.

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87. A. Efrat, S. G. Kobourov, M. Stepp, and C. Wenk, Growing Fat Graphs, video contribution, Proc. 18thAnnual Symposium on Computational Geometry (SoCG), 2002, 277–278.

88. C. Wenk, H. Alt, A. Efrat, L. Palaniappan and G. Rote, Finding a curve in a map, video contribution, Proc.19th Annual Symposium on Computational Geometry (SoCG), 2003, 384–385.

89. A. Efrat, H. H. Gonzalez-Banos, S. Kobourov and L. Palaniappan, Optimal motion strategies to track andcapture a predictable target, Fall Workshop on Computational Geometry, 2003.

90. C. Wenk, H. Alt, A. Efrat, L. Palaniappan and G. Rote, Finding a curve in a map, video contribution, FallWorkshop on Computational Geometry, 2003.

91. K. Barnard, A. Connolly, L. Denneau, A. Efrat, J. N. Heasley, R. Jedicke, J. M. Kubica, B. Moon, A.Moore, S. Morris, P. Rao, The LSST moving object pipeline, Observatory operations: strategies, processes,and systems, proc. of SPIE Vol. #6270, 2006, to appear.

92. J. Kubica, T. Axelrod, K. Barnard, A. Connolly, L. Denneau, A. Efrat, J. Heasley, R. Jedicke, B. Moon, A.Moore, S. Morris, P. Rao, ”Efficiently Tracking Moving Sources in the LSST”, 207th meeting of the AmericanAstronomical Association.

E. Manuscripts and Technical Reports

93. N. Cohen, A. Efrat and F. Bruckstein, On ants crickets and frogs in circular pursuit. Report. CIS-9309.Faculty of Computer Science, Technion - IIT, 1994.

94. Y. Shi, Y.T. Hou, and A. Efrat, ”Design of low-complexity approximation algorithms for base station place-ment problems in sensor networks,” Technical Report, The Bradley Dept. of ECE, Virginia Tech, 2005.

F. In Preparation

95. M. L. Narro, F. Yang, R. Kraft, C. Wenk, A. Efrat, and L. L. Restifo, NeuronMetrics: Software for rapidsemi-automated processing of cultured-neuron images, submitted to Brain Research.

6 Presentation

6.1 Invited Presentations in the last 5 years

• Dagstuhl Workshop on Computational Geometry (2001). Dagstuhl is a center in Germany that arranges week-long workshops on different aspects of Computer Science. Attendance is by invitation only, and consideredrather prestigious.

• Invited talk at Honda Research Labs (2002). Subject of the talk: Art Gallery Problems.

• Dagstuhl Workshop on Computational Geometry (2003). Subject of talk: Touring a Sequence of Polygons.

• Invited talk at Tel-Aviv University (2004). Subject of talk: Touring a Sequence of Polygons.

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• Invited talk at Ben-Gurion University (2004). Subject of talk: Touring a Sequence of Polygons.

• Invited talk at Texas A&M University (2004). Subject of talk: Touring a Sequence of Polygons.

• Invited talk in the Electrical and Computer Department at the University of Arizona (2004). Subject of talk:Touring a Sequence of Polygons.

• Invited visit and talk in Caltech (2005). Subject of talk: Sensors locations problems.

• Invited visit and talk in Arizona State University (2005). Subject of talk: Sensors locations problems.

• Dagstuhl Workshop on Computational Geometry (2005). Subject of talk: On Sensor Location Problems.

• Invited visit and talk in Universitat Politecnica de Catalunya, Barcelona. (2006). Subject of talk: On SensorLocation Problems.

• Invited visit and a talk in Stony Brook University (2006). Subject of talk: On Sensor Location Problems.

• Santa Barbara NSF Workshop on Geometric Approaches to Ad Hoc and Sensor Networks (2006). Subject oftalk: On sensor scheduling problems.

• Dagstuhl Workshop on Robot Motion Planning (2006). Subject: TBA.

• Dagstuhl Workshop on Computational Geometry (2007). Subject: TBA.

6.2 Submitted Presentations in the last 5 years

• A. Efrat, S. Har-Peled, L. Guibas and T.M. Murali, Morphing between Curves. Proc. 12th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 2001.

• A. Efrat and S. Har-Peled, Locating guards in art galleries, 2nd IFIP International Conference on TheoreticalComputer Science 2002.

• A. Efrat, H. H. Gonzalez-Banos, S. Kobourov and L. Palaniappan, Optimal motion strategies to track andcapture a predictable target, Fall Workshop on Computational Geometry, 2003.

• Q. Fan, A. Efrat, V. Koltun, S. Krishnan and S. Venkatasubramanian, hardware-assisted natural neighborinterpolation, Workshop on Algorithm Engineering and Experiments (ALENEX) 2005.

• A. Amir, A. Efrat, J. Myllymaki, L. Palaniappan and K. Wampler, Buddy tracking — efficient proximitydetection among mobile friends 23rd Conference of the IEEE Communications Society (INFOCOM) 2004.

• A. Efrat, S. Har-Peled and J. Mitchell, Approximation algorithms for two optimal location problems in sensornetworks, IEEE 2nd Int. conf. on Broadband Communication, Networks and systems. (BROADNET) 2005.

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7 Grants

2005 PI: ACIST: Video Browsing with application for KUAT programs. With Kobus Barnard.$95,000. Effort: %50.

2005 PI NSF REU Supplement: ITR/Collaborative Research: Intelligent Topology Control and EnergyProvisioning for Wireless Video Sensor Networks. $6,000

2005 PI: NSF: Databases for moving objects for the Large Synoptic Survey Telescope (LSST). WithKobus Barnard and Bongki Moon. $205,000. Effort: %33.

2004 NSF PI: REU Supplement: ITR/Collaborative Research: Intelligent Topology Control and EnergyProvisioning for Wireless Video Sensor Networks. $6,000

2004 PI: NSF CAREER Grant 0348000. Pattern Matching, Realistic Input Models and Sensor Placement.Useful Algorithms in Computational Geometry. Duration 2004–2007. $403,000. Effort: %100.

2003 PI: NSF Grant 0312443 — ITR/Collaborative Research: Intelligent Topology Control and EnergyProvisioning for Wireless Video Sensor Networks. With Steve Pink (Co-PI)Duration is 2002-2005. $122,126. Effort: %85.

2002 CO-PI: NSF Grant 0222920 — Visualization of Giga-Graphs and Graph Processes. With StephenKobourov (PI) and James Abelo. Duration is 2002-2005. $240,000. Effort: %20.

2002 PI: Honda Research Laboratory (a gift with Joe Mitchell (co-PI)), $55,000. Effort: %50.

8 Signed Statement

This is a true and accurate statement of my activities and accomplishments. I understand thatmisrepresentation in securing promotion and tenure may lead to dismissal or suspension under ABORPolicy 6-201 J.1.b.

Alon Efrat

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9 Statement of Accomplishments and Objectives on Research, Teach-ing and Service/Outreach

9.1 Accomplishments and Objectives on Research and Service

Basic Research Philosophy:

When I applied for a faculty position at the Department of Computer Science at the University of Arizona, Iwrote in my research statement:

In my research, I have studied problems that arise in computational geometry and related areas, es-pecially those motivated by applications in Geographic Information Science (GIS), computer graphics,robotics, and pattern matching. There has been extensive research in computational geometry on var-ious fundamental problems in these and similar domains. Algorithms developed for such problems bypractitioners in these areas tend to be efficient mainly for their typical inputs and they might be in-efficient for more complicated inputs. On the other hand, techniques used in computational geometryhave proven to be valid and theoretically efficient even in worst-cases. Unfortunately, they are oftentoo difficult to implement or slow in practice. These drawbacks have limited the widespread use ofcomputational geometry techniques in fulfilling the practical needs of computer graphics, robotics, andGIS applications.

In my research, I focus on bridging this gap between practice and theory by developing algorithms whosecorrectness and bounds are proven, yet are simple to implement and perform efficiently on real-worldinputs.

In my research so far I have strived to address the issues I mentioned in my statement above. I have beenadvancing theory and conducting research projects that have had practical significance. Below, I detail a list ofthe areas where I have contributed and outline my future research plans.

My experience in the area of geometric algorithms allows me to apply my research interests in diverse fields.I provide a unique viewpoint on various problems which (as my publication record shows) can lead to originaland significant research contributions. I am able to develop theoretical ideas, that are motivated by real-worldapplications from deep insight into geometric and combinatorial structures.

I want to add that this line of research is supported by the NSF CAREER award in the amount of $405,000.Other grants are listed below.

Research Disciplines — Achievement and Future Directions

Realistic Input Models In many cases, input taken from realistic scenarios exhibits many combinatorial prop-erties that worst-case scenarios do not demonstrate. There has been an extensive study in the computationalgeometry community to understand and use these properties. The development of algorithms for realisticinput models is done by either tailoring known computational techniques, or by inventing new ones, that areeasy to implement and run fast on these classes of inputs. In many situations, the worst-case scenario foran algorithm is exhibited only for input objects that are long and “skinny”. These objects seldom appear inrealistic scenarios. This phenomenon has given rise to the notion of fatness. A convex object C is called fatif the radius of the smallest ball enclosing C is not much larger than that of the largest ball enclosed insideC. For non-convex objects the definition is more involved.

I have extensively studied problems where fat objects are involved. Since my doctoral research work I dealtwith objects in two-dimensions. In a series of research papers with other colleagues ([3, 9, 20, 12, 16, 47, 48])1

we developed efficient algorithms for different variants of fatness, and also showed that known algorithms canbe executed efficiently for fat objects. The paper [24] was published in the SIAM Journal of Computing. Inthree-dimensions and four-dimensions we were able to give a bound on the complexity of the union of shapes

1The citation number refers to publications listed in my CV.

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with bounds on their curvature [27, 73], and presented efficient algorithms for computing the free space of amotion of a robot in the presence of these type of obstacles.

I believe that this area of investigation has a wide spectrum of possible research directions and one of mygoals is to continue this line of work.

Sensor Networks I am interested in Ad-Hoc and Wireless Sensor networks research. The behavior of thesesystems is affected to a great extent by the geometric environment in which they operate. Accordingly, manyintriguing optimizations of geographic questions arise. During the past couple of years I have enjoyed workingin this area which I see complementing my research activities as a computational geometer. With other co-PIs I won an NSF grant for studying basestation location problems (see description below), and a grantfrom Honda Research Lab for studying sensors locations. I have been collaborating with networking people,am co-supervising Jesus Arango, a networking doctoral student, I published and participated in programcommittees of networking venues, and have been invited to be on NSF panels for networking. Together withSubhash Suri and Leo Guibas, we organized the NSF Workshop on Geometric Approaches to Ad Hoc andSensor Networks, in Santa Barbara (June 06), an NSF-funded workshop aiming to bring together leadingresearchers of ad Hoc and Sensor Networks and those of computational geometry, computational topology,and combinatorial optimization.

I proposed, along with Arnon Amir and others, a simple data structure and an algorithm that can beimplemented on a mobile PDA that is aware of its location. The algorithm alert the user once one of the user’sfriends appears in the user’s vicinity (for example, when one of the friends visits the user’s neighborhood).The algorithm minimizes the number of messages exchanged between users while they are moving, andneed to update each other about their new locations. This work appeared in the prestigious conferenceINFOCOM ([71]). An improved version of this paper ([34]) was accepted to the Journal of Pervasive andMobile Computing. This work is also the basis of a patent that Arnon and I hold [1].

Many intriguing questions deal with optimal location of a set sensors, in order to meet some optimal criteria.A classic example is the Art Gallery problem where one seeks to place the smallest number of cameras insidea building or terrain, so that each point of the building/terrain is seen by at least one camera. While findingthe smallest number of cameras is in general NP-Hard, we presented an efficient approximation algorithmfor this problem, ([32]), and of several other variants of this problem where one tries to guarantee that everypoint is seen from several directions. We were also able to show how to find the location of a guard, or a setof no more than a predetermined number of guards, that see the maximum area of the building or terrain([30, 70]).

With my colleagues, I have studied forwarding protocols for routing messages between nodes without knowl-edge of neighborhood relationship ([72, 78, 80]). We investigated scheduling problems for a set of sensorsthat collaboratively provide surveillance of an area ([81, 83]), and found the algorithm for optimal basestationlocation for maximizing the lifespan of a network of battery-operated multi-hops set of sensors ([36, 79, 94]).

Commonly a large number of inexpensive sensors are sprinkled in an environment. These sensors do nothave any means to find their location, such as GPS. However, they can find which other sensors are in theirvicinity, and estimate, with some level of accuracy, the distance to them. We assume the existence of a centralbasestation that can collect this information from the sensors. Together with Stephen Kobourov (from theUniversity of Arizona) and others, we designed an algorithm (influenced by graph-drawing techniques), thatdetermines the position of each sensor based on these neighboring relations ([76]). Due to its importance andpotential application, research on variants of this problem is currently receiving significant attention. We aredeveloping a distributed version of this which we believe will have a significant impact on this area.

Biological Applications I have had very productive collaborations with biologists. I have made contribu-tions to the system “CAROL” designed at the Free University in Berlin, for analysis of electrophoresis gels[19, 21, 59, 62]. Furthermore, my recent collaboration with Linda Restifo from the Neurobiology and Neu-rology department at the University of Arizona focused on developing automatic analysis of neurons. Thiscollaboration has yielded two papers. The first ([37]) was accepted to the Journal of Neuroscience. Thesecond paper ([95]) was recently submitted to the Journal of Brain Research.

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The University of Arizona is the home of many biologists. There are many aspects of geometric algorithmsin the research conducted by many of them. I plan to continue fruitful collaborations with many of them.

Medical Applications This area is probably the most important for me in all my research, and emotionallythis is where I really hope to make a meaningful contribution. There are several projects on which I amcurrently working, in collaborations with Prof. Russell Hamilton and others from the Radiation Oncologycenter and the University Medical Center of the University of Arizona. All these projects are aiming toimprove the efficiency of radiation treatment of cancer patients. One of the projects estimates deformationsof the prostate during the radiation treatment period for prostate cancer. The estimation is based on changesin the locations of seeds implanted into the prostate, whose 3D positions in the body is monitored daily usingan X-ray device. Another project aims to improve positioning of breast cancer patients, based on positionof their skin surface, whose 3D description is acquired using optical means. Better positioning means that alarger fraction of the radiation reaches the tumor, rather than vital healthy tissues.

Astronomical Applications Together with Kobus Barnad and Bongki Moon, from the Computer ScienceDepartment at the University of Arizona, we joined the Large Synoptic Survey Telescope (LSST) project.Our rule centers in identifying and indexing the moving objects, especially in the solar system. We studymainly (i) efficient data-structures for efficiently answering queries on these objects (for example, which onesare expected to pass a given query region of the sky in a certain time interval), and (ii) matching orbits ofknown objects to observed objects. These matchings are max-likelihood estimated.

We are currently funded by the NSF in the amount of $200,000, for the years 2005 and 2006. Our researchso far has lead to two publications ([91, 92]) describing the preliminary procedures of the system we aredeveloping.

Applications to Computer Vision I have been interested in problems of geometric pattern matching for along time. These problems have many immediate applications. For example, from among multiple shapesstored in a database, each represented as a point set, we would like to find the one most similar to a queryshape. In an early stage of my PhD I had presented algorithms for matching shapes in higher dimensionswhen the Hausdorff distance is used as the similarity measure [8, 43]. I was also intrigued to find alternativeways to measure similarity between point sets, since despite its usefulness and its common use, the Hausdorffdistance suffers in certain cases, from many drawbacks. We proposed the bottleneck 1-1 matching [4, 13, 44]as an alternative, and proposed efficient algorithms for computing this measure. I was also intrigued tofind algorithms for finding matchings of sets of segments, especially where these segments belong to certainorientations, e.g., only horizontal or only vertical. This scenario is motivated by constructing models ofenvironments using mobile robots, where a sequence of different “pictures” or laster scans of the environmentneed to be aligned and merged, and the majority of the segments describing these pictures are horizontal orvertical. The alignment of a picture with respect to another one requires an efficient algorithm for findinga transformation that minimizes the similarity measure between the pictures. The papers [25, 57] describeseveral techniques obtained for these problems.

As opposed to matching points sets, matching curves calls for better insights. Of course, one can view acurve as a collection of many points, but this obscures the continuity of moving along the curve. A classicalexample is matching hand-written signature where the algorithm needs to decide if a given signature matchesthe signatures of the same person stored in a database. A common method to handle similarly betweencurves is the Dynamic Time Warping (DTW) that was first proposed in the 60s as a measure of speechsignal similarity. This method is efficient and easy to implement, but the level of similarity between thecurves depends heavily on the locations of sample of points along the curves, and moreover, this disadvantagecannot be overcome by over-sampling the curves. We recently proposed ([35]) a method to measure thesimilarity between all points of the curves (not only the sample points). We call this method the ContinuousDTW, and presented algorithms for exactly and approximately computing this similarity.

A related method of measuring similarity between two curves is the well-studied Frechet distance, whichassumes that a man walks along one of the curves, a dog walks on the other curve, the man holds the dog bya leash, and the distance between the curves is defined as the shortest possible leash needed, over all possible

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monotone motions of the man and the dog along their curves. We used this method ([23, 65, 88]) to find apath in a map which is the most similar to a path taken by a person moving along a road, who periodicallymeasures his location, but his measurements are noisy and inaccurate. The short video [88], describing thisalgorithm, is accessible from my webpage.

A current research area is the study of the Iterative Closest Pair (ICP). This algorithm is very popularin computer vision and computer graphics applications. The algorithm finds a transformation (commonlytranslation and rotation) of one shape so as to fit another shape. This is needed for example when one uses alaser scanner to scan a 3D geometric object, and needs to align different scans to obtain a description of thewhole object. This algorithm is usually extremely fast in practice, but its behavior is not fully understood,and has not been analyzed. In [38, 77] we showed upper bounds on its running time for any input of n points,and described scenarios (by presenting specific examples of sets of points) at which the running time might belarge. This work is well justified, due to the popularity of the ICP algorithm. Many questions are still openin this area. We hope to further improve the analysis. More importantly, we believe that this analysis, andthe understanding of the algorithm would enable us to propose improvements to the ICP algorithm itself.

Other Research Areas I contributed to publications in other areas such as Graph Drawing [31, 33, 60, 69, 87],Geographic Information Systems (GIS) [5, 53], Robots motion planning [20, 54, 66, 56, 67], Computer Graphics[55, 55], Multimedia and video browsing [22, 61, 82], and other.

9.2 Accomplishments and Objectives in Teaching

Teaching Philosophy I have taught many different courses since 1994, and have developed a fair number ofnew courses. My teaching style is open and friendly. I like to stimulate the curiosity of my students and theirdesire to address challenging questions. In this way, I am confident that students are motivated and willing to putan effort into the course work.

I enjoy teaching introductory courses in computer science since they enable students to develop the foundationof a methodical yet creative way of coping with problems. I find teaching introductory courses challenging andexciting. For similar reasons, I also enjoy teaching courses on algorithms, such as introduction to algorithms,computational geometry and data structures. I have taught both introductory and advanced courses in these areas,and I consider myself to be very experienced in this regard.

When a new scientific area develops, I am interested in translating it into a graduate course. This enablesstudents to be current in their knowledge of cutting edge research and gives me the opportunity to deepen myunderstanding of a new research area. This has led to collaborations with students who become intrigued with thetopics taught. I list some examples of such courses.

Overall Picture of Teaching History Before Arriving to the University of Arizona During this timeI taught numerous and diverse courses at various Universities and Institutes in Israel. I was considered to be a goodteacher as indicated by my teaching evaluations that rated me between 4.2 to 4.8 (where 5 is the best possible).

During my senior PhD years, I was a teaching assistant and course instructor at Tel-Aviv University. I taughtdifferent introductory courses. I also developed and offered a seminar in randomized algorithms in computationalgeometry, and a seminar on algorithms for Geographic Information Science (GIS).

At the same period, due to the demand from the high-tech industry and the internet boom, several institutesoffered a certificate program degree in software engineering, and I was hired by several of them (the Technion,Tel-Aviv college, The Open University, IBM Israel) to teach theory courses— mainly on Data Structures andAlgorithms. I was also hired by the Open University to develop and prepare distance learning courses. Thesecourses included “advanced algorithms”, “file systems” and “computational geometry”.

Overall Picture of Teaching History at the University of Arizona Success teaching students at theUniversity of Arizona required an adjustment in my teaching techniques, and took me some effort to carry out.The teaching evaluations I received on most of the courses I taught during my first years at the university wereon the low side. There were some external factors that contributed to this — some of these courses I taught for

the first time, and their content, though related to my research, was not exactly in my domain of expertise. Thiswas the case for the course “introduction to computer graphics” and “advance computer graphics”. Regarding thelatter, it was a course that I developed and thus was more challenging. Furthermore, some of these courses, such as“foundations of computing” were required courses and notoriously unpopular among students, who in many casesdid not see them as contributing to their professional careers, and were thus not invested in the course.

Teaching is a part of my work I particularly enjoy and in the past had taken pride in doing it well. I took thestudents feedback very seriously and invited colleagues and senior facultys input in my teaching. The feedbacks Ireceived from the visits in my classes were that my lectures were coherent and well organized. However, it took mesome time to fully find a common language with the students.

In the last year and a half I worked on various aspects of my teaching. I attended an accent reduction clinicthat the Department of Speech, Language, and Hearing Sciences at the University of Arizona offers. I also soughtmore specific feedback from students throughout the semester — I invited a focus group from the university officeof instructional research and evaluation to conduct a discussion with the students and get direct feedback on theirview of the course, its contents and my teaching, as well as parts that they would like to see be addressed more inclass. As I had hoped, my teaching evaluations improved significantly. In the “Introduction to Algorithms” coursewhich I taught during Spring 06, the mean grade of teaching effectiveness was 3.74 (out of a maximum possible of5), raised from a score of 3.27 for the same course which I taught the previous year. I saw similar improvements inother teaching categories of the teaching evaluation as well. The anonymous text comments and suggestions fromthe students clearly indicated that the majority of the students felt that the course was progressing well.

I feel confident that my efforts and the constructive feedback I received from colleagues and students alike haveled to improvements in my teaching effectiveness. I have been able to determine how to best reach and engagestudents in learning. Accordingly, I have seen significant improvements in my teaching of the elective courses inthe last 3 semesters. In “geometric algorithm” in Fall 05, the mean rating for “instructor’s teaching effectiveness”was 4.45. In “optimization problems in sensors and ad-hoc networks” which I taught with Prof. Ramasubramanianfrom ECE in the Fall 05, the mean “instructor’s teaching effectiveness” rating was 4.10.

I have been pleased with my progress so far and continue to look for new ways to improve my teaching.

9.3 Accomplishments and Objectives in Service/Outreach

The Computer Science department at the University of Arizona is a mid to small size department, with only 17full time research faculty. As such, the responsibility that each of us should carry is large. I am excited to take alarger role in helping the department and University accomplish their goals. Regarding my service to the scientificcommunity, I have contributed significantly by serving on various committees and editorial boards. I feel confidentthat I can continue on this path.

Regarding outreach, I am planning and hoping to continue my collaborations with researchers from otherdisciplines. These collaborations, in particular my ongoing collaboration with the Radiation Oncology departmentat UMC, has direct applications to human life as well as being a scientific challenging one. I am hoping to continuedeveloping these collaborations, as well as others in the future.

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A. Patents:

References

[1] A. Efrat and A. Amir, Buddy Tracking — A system for proximity alerts between friends. A patent in fillingstage. US. 2004/0198398.

B. Papers in Journals

[2] P. K. Agarwal, A. Efrat, M. Sharir and S. Toledo, Computing a segment-center for a planar point set, J.Algorithms 15 (1993), 314–323.

[3] A. Efrat, M. Sharir and G. Rote, On the union of fat wedges and separating a collection of segments by aline, Computational Geometry: Theory and Applications (CGTA) 3 (1994), 277–288.

[4] A. Efrat and C. Gotsman, Subpixel image registration using circular fiducials, International J. of Compu-tational Geometry and Applications (IJCGA) 4 (1994), 403–422.

[5] A. Efrat, M. Sharir and A. Ziv, Computing the Smallest k-Enclosing Circle and Related Problems. Compu-tational Geometry: Theory and Applications (CGTA) 4 (1995), 119–136.

[6] *A. Efrat and M. Sharir, A Near-Linear Algorithm for the Planar Segment Center Problem, Discrete andComputational Geometry (DCG) 16 (1996), 239–257.

[7] *A. Efrat and O. Schwarzkopf, Separating and Shattering Long Line Segments. Information ProcessingLetters (IPL) 64 (1998), 309–314.

[8] *L.P. Chew, D. Dor, A. Efrat and K. Kedem, Geometric Pattern Matching in d-dimensional Space, Discreteand Computational Geometry (DCG) 21 (1999) 257–274.

[9] *A. Efrat and M.J. Katz, On the union of κ-curved Objects, Computational Geometry: Theory and Appli-cations (CGTA) 14 (1999), 241–254.

[10] *A. Efrat and M. Sharir, On the Complexity of the Union of Fat Objects in the Plane, Discrete andComputational Geometry (DCG) 23 (2000), 171–189.

[11] *P.K. Agarwal, A. Efrat and M. Sharir, Vertical Decomposition of Shallow Levels in 3-dimensional Arrange-ments and its Applications, SIAM J. Computing 29 (2000), 912–953.

[12] *A. Efrat, M.J. Katz, F. Nielsen and M. Sharir, Dynamic Data Structures for Fat Objects and TheirApplications. Computational Geometry: Theory and Applications (CGTA) 15 (2000), 215-227.

[13] *A. Efrat and M.J. Katz, Computing an Euclidean Bottleneck Matching in Higher Dimension. InformationProcessing Letters (IPL), 4 (2000), 169–174.

[14] *A. Efrat, M.J. Katz and A. Itai, Geometry Helps in Bottleneck Matching and Related Problems, Algorith-mica 1 (2001) 1–28.

[15] A. Amir, A. Efrat, P. Indyk and H. Samet, Efficient Algorithms and Regular Data Structures for Dilation,Location and Proximity Problems. Algorithmica (2001) 166–187.

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[16] *B. Aronov, A. Efrat, D. Halperin and M. Sharir, A subquadratic Bound on the Number of Regular Verticesof the Union of Jordan Regions. Discrete and Computational Geometry (DCG) 25 (2001), 203–220.

[17] *T. Chan and A. Efrat Fly Cheaply: On the Minimum Fuel-Consumption Problem. J. Algorithms 41 (2001),330–337.

[18] N. Davis, K. Cheverst, K. Mitchell and A. Efrat, Using and Determining Location in a Context-SensitiveTour Guide: The Guide Experience. IEEE computers 34 (2001), 35–41.

[19] A. Efrat, F. Hoffmann, K. Kriegel, C. Schultz and C. Wenk, Geometric Algorithms for the Analysis of2D-Electrophoresis Gels. Journal of Computational Biology (JCB), (special issue dedicated to RECOMB2001), 9 (2002), 299–316.

[20] A. Efrat, L. J. Guibas, S. Har-Peled, J. S. B. Mitchell and T.M. Murali, New Similarity Measures BetweenPolylines with Applications to Morphing and Polygon Sweeping, Discrete and Computational Geometry(DCG) (2002), 535–569.

[21] A. Efrat, F. Hoffmann, K. Kriegel, C. Knauer, G. Rote and C. Wenk, Covering Shapes by Ellipses Algorith-mica (special issue on shape algorithms) (2003), 145–160.

[22] A. Amir and S. Srinivasan, Search the Audio, Browse the Video — a Generic Paradigm for Video Collections,EURASIP Journal on Applied Signal Processing, 2(2003), 209–222.

[23] H. Alt, A. Efrat, G. Rote and C. Wenk, Matching Planar Maps, J. Alg. 49 (2003), 262–283.

[24] A. Efrat, The Complexity of the Union of (α, β)-Covered Objects. SIAM J. Computing, 34 (2005), 755–787.

[25] A. Efrat, P. Indyk and S. Venkatasubramanian. Pattern Matching for Sets of Segments. Algorithmica,40(2004), 147-160.

[26] A. Efrat, S. Kobourov and A. Lubiw, Computing homotopic shortest paths efficiently, Computational Ge-ometry: Theory and Applications (CGTA), to appear.

[27] B. Aronov, A. Efrat, V. Koltun and M. Sharir, On the Union of κ-Round Objects in Three and FourDimensions, Discrete and Computational Geometry (DCG) (special issue dedicated to Proc. 20th AnnualSymposium on Computational Geometry (SoCG)). To appear.

[28] P. Brass, E. Cenek, C. A. Duncan, A. Efrat, C. Erten, D. Ismailescu, S. G. Kobourov, A. Lubiw and J. S. B.Mitchell, On Simultaneous, Planar Graph Embeddings, Computational Geometry: Theory and Applications(CGTA), to appear.

[29] A. Efrat, L.J. Guibas and O.A. Hall-Holt and L. Zhang, On Incremental Rendering of Silhouette Maps ofa Polyhedral Scene. Computational Geometry: Theory and Applications (CGTA) to appear.

[30] O. Cheong, A. Efrat and S. Har-Peled, On Finding a Guard that Sees Most and a Shop that Sells Most,Discrete and Computational Geometry (DCG) to appear.

[31] A. Efrat, C. Erten and S. Kobourov, Fixed-Location Circular Arc Drawings Journal of Graph Algorithmsand Applications. To Appear.

[32] A. Efrat and S. Har-Peled, Locating Guards in Art Galleries, Information Processing Letters (IPL), toappear.

[33] C. A. Duncan, A. Efrat, S. G. Kobourov and C. Wenk, Drawing with Fat Edges, International Journal ofFoundations of Computer Science (IJFCS) Special Issue of on Graph Drawing. To appear.

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[34] A. Amir, A. Efrat, J. Myllymaki, L. Palaniappan and K. Wampler, Buddy Tracking — Efficient ProximityDetection Among Mobile Friends, Pervasive and Mobile Computing, to appear after revision.

[35] A. Efrat, Q. Fan and S. Venkatasubramanian, Curve Matching, Time Warping, and Light Fields, NewAlgorithms for Computing Similarity between Curves, J. Mathematic Imaging and Vision, to appear.

[36] Y. T. Hou, Y. Shi, J. Pan, A. Efrat, and S. F. Midkiff, Maximizing Lifetime of Wireless Sensor NetworksThrough Optimal Single-Session Flow Routing,” in IEEE Transactions on Mobile Computing, to appear.

[37] R. Kraft, M. Escobar, M. Narro, J. Kurtis, A. Efrat, K. Barnard, and L. Restifo, Phenotypes of DrosophilaBrain Neurons in Primary Culture Reveal a Role for Fascin in Neurite Shape and Trajectory The Journalof Neuroscience, to appear after revision.

[38] E. Ezra, M. Sharir and A. Efrat, On the ICP Algorithm, Computational Geometry: Theory and Applications(CGTA), to appear.

C. Papers in Proceedings of Peer-Reviewed Conferences

[39] *A. Efrat, M. Sharir and A. Ziv, Computing the Smallest k-Enclosing Circle and Related Problems. Proc.Third Workshop Algorithms Data Struct. (WADS) LNCS 1993, vol. 709, 325–336.

[40] A. Efrat and C. Gotsman, Subpixel Image Registration using Circular Fiducials, Proc. of the second IsraeliSymposium on Theory of Computing and Systems 1994, 127–136.

[41] A. Efrat and M. Sharir, Near-Linear Algorithm for the Planar Segment Center Problem, Proc. 5th AnnualACM-SIAM Symposium on Discrete Algorithms (SODA) 1994, 87–97.

[42] P.K. Agarwal, A. Efrat and M. Sharir, Vertical Decomposition of Shallow Levels in 3-Dimensional Arrange-ments and its Applications, Proc. 11th Annual Symposium on Computational Geometry (SoCG), 1995,39–50.

[43] *L.P. Chew, D. Dor, A. Efrat and K. Kedem, Geometric Pattern Matching in d-dimensional Space, 3rdEuropean Symposium on Algorithms (ESA), LNCS vol. 979, 1995, 264–279.

[44] *A. Efrat and A. Itai, Improvements on Bottleneck Matching and Related Problems, Using Geometry. Proc.12th Annual Symposium on Computational Geometry (SoCG), 1996, 301–310.

[45] *A. Efrat and M.J. Katz, Computing Most-Uniform and Minimum Deviation Matchings in Geometric Set-tings, Proc. 7th Annual International Symposium on Algorithms and Computational (ISAAC), 1996, 115–125.

[46] *A. Efrat and O. Schwarzkopf, Separating and Shattering Long Line Segments, Proc. 7th Annual Interna-tional Symposium on Algorithms and Computational (ISAAC), 1996, 36–44.

[47] *A. Efrat and M. Sharir, On the Complexity of the Union of Fat Objects in the Plane, Proc. 13th AnnualSymposium on Computational Geometry (SoCG), 1997, 104–112.

[48] *A. Efrat, M.J. Katz, F. Nielsen and M. Sharir, Dynamic Data Structures for Fat Objects and TheirApplications. Proc. 5th Workshop on Algorithms and Data Structures (WADS), 1997, 297–396.

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[49] *A. Efrat and M.J. Katz, on the Union of α-Curved Objects, Proc. 14th Annual Symposium on Computa-tional Geometry (SoCG), 1998, 206–213.

[50] *A. Efrat and S. Har-Peled, Fly Cheaply: On the Minimum Fuel-Consumption Problem, Proc. 14th AnnualSymposium on Computational Geometry (SoCG), 1998, 143–145.

[51] *B. Aronov, A. Efrat, D. Halperin and M. Sharir, a Subquadratic Bound on the Number of Regular Verticesof the Union of Jordan Regions. Proc. 6th Scand. Workshop on Algorithms Theory (SWAT), 1998, 322–334.

[52] A. Efrat, The Complexity of the Union of (α, β)-Covered Objects, Proc. 15th Annual Symposium on Com-putational Geometry (SoCG), 1999, 134–142.

[53] A. Amir, A. Efrat, P. Indyk and H. Samet, Efficient Algorithms and Regular Data Structures for Dilation,Location and Proximity Problems, Proc. 40 Annual IEEE Symposium on Foundations of Computer Science(FOCS), 1999, 160–170.

[54] H. H. Gonzalez-Banos, E. Mao, J.C. Latombe, T. M. Murali and A. Efrat,. Planning Robot Motion Strategiesfor Efficient Model Construction, Proc. 9th International Symposium of Robotics Research, 2000, 345-352.

[55] A. Efrat, L.J. Guibas, O.A. Hall-Holt and L. Zhang, On Incremental Rendering of Silhouette Maps ofa Polyhedral Scene. Proc. 11th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 2000,910–917.

[56] A. Efrat, L.J. Guibas, S. Har-Peled, D.C. Lin, J.S.B. Mitchell, T.M. Murali. Sweeping Simple Polygons witha Chain of Guards. Proc. 11th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 2000.297–936.

[57] A. Efrat, P. Indyk and S. Venkatasubramanian, Pattern Matching for Sets of Segments, Proc. 12th AnnualACM-SIAM Symposium on Discrete Algorithms (SODA), 2001, 295–304.

[58] A. Efrat, S. Har-Peled, L. Guibas and T.M. Murali, Morphing Between Curves. Proc. 12th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 2001, 680–689.

[59] A. Efrat, F. Hoffmann, K. Kriegel, C. Schultz and C. Wenk, Geometric Algorithms for the Analysis of2D-Electrophoresis Gels, The Fifth Annual International Conference on Computational Molecular Biology(RECOMB), 2001, 114-123.

[60] C. A. Duncan, A. Efrat, S. G. Kobourov and C. Wenk, Drawing with Fat Edges, Graph Drawing (GD)2001. LNCS vol. 2265, 162–177.

[61] A. Amir, A. Efrat and S. Srinivasan, Developments in Phonetic Word Retrieval, ACM Tenth InternationalConference on Information and Knowledge Management (CIKM) 2001 , 580–582.

[62] A. Efrat, F. Hoffmann, K. Kriegel, C. Knauer, G. Rote and C. Wenk, Covering Shapes by Ellipses Proc.13th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 2002, 453–454.

[63] A. Efrat and S. Har-Peled, Locating Guards in Art Galleries, 2nd IFIP International Conference on Theo-retical Computer Science 2002, 181–192.

[64] A. Efrat, S. Kobourov and A. Lubiw, Computing Homotopic Shortest Paths Efficiently, European Symposiumon Algorithms (ESA) 2002, 411–423.

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[65] H. Alt, A. Efrat, G. Rote and C. Wenk, Matching Planar Maps, Proc. 14th Annual ACM-SIAM Symposiumon Discrete Algorithms (SODA) 2003, 589–592.

[66] A. Efrat, H. H. Gonzalez-Banos, S. Kobourov and L. Palaniappan, Optimal Motion Strategies to Track andCapture a Predictable Target, IEEE International Conference on Robotics and Automation, 2003.

[67] M. Dror, A. Efrat, A. Lubiw and J. S. B. Mitchell, Touring a Sequence of Polygons, ACM Symposium onTheory of Computing (STOC) 2003, 473–482.

[68] P. Brass, E. Cenek, C. A. Duncan, A. Efrat, C. Erten, D. Ismailescu, S. G. Kobourov, A. Lubiw and J. S.B. Mitchell, On Simultaneous, Planar Graph Embeddings, Workshop on Data Structures (WADS) 2003,243–255.

[69] A. Efrat, C. Erten and S. Kobourov, Fixed-Location Circular-Arc Drawing of Planar Graphs, Graph Drawing(GD) 2003 147–158.

[70] O. Cheong, A. Efrat and S. Har-Peled, On Finding a Guard that Sees Most and a Shop that Sells Most,Proc. 15th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 2004, 1098–1107.

[71] A. Amir, A. Efrat, J. Myllymaki, L. Palaniappan and K. Wampler, Buddy tracking — efficient proximitydetection among mobile friends The 23rd Conference of the IEEE Communications Society (INFOCOM)2004.

[72] J. Arango, M. Degermark, A. Efrat and S. Pink, an efficient flooding algorithm for mobile Ad-hoc networks,Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt), 2004.

[73] B. Aronov, A. Efrat, V. Koltun and M. Sharir, On the Union of κ-Round Objects in Three and FourDimensions, Proc. 20th Annual Symposium on Computational Geometry (SoCG) (2004), 383–390.

[74] Q. Fan, A. Efrat, V. Koltun, S. Krishnan and S. Venkatasubramanian, Hardware-Assisted Natural NeighborInterpolation. Workshop on Algorithm Engineering and Experiments (ALENEX) 2005. .

[75] A. Efrat, S. Har-Peled and J. Mitchell. Approximation Algorithms for Two Optimal Location Problems inSensor Networks. IEEE 2nd Int. conf. on Broadband Communication, Networks and systems. (BROAD-NET) 2005.

[76] A. Iyer, A. Efrat, C. Erten, D. Forrester and S. Kobourov, Force-Directed Approaches to Sensor Localization,Workshop on Algorithm Engineering and Experiments (ALENEX) 2006.

[77] E. Ezra, M. Sharir and A. Efrat, On the ICP Algorithm, Proc. 22th Annual Symposium on ComputationalGeometry (SoCG), 2006.

[78] J. Arango, A. Efrat, S. Ramasubramanian and M. Krunz, Retransmission and Back-off Strategies for Broad-casting in Multi-hop Wireless Networks IEEE 3rd Int. conf. on Broadband Communication, Networks andsystems. (BROADNET) 2006. ()

[79] Y. Shi, Y. T. Hou, and A. Efrat, Algorithm Design for Base Station Placement Problems in Sensor Networks,3rd Int. Conf. on Quality of Service in Heterogeneous Wired/Wireless Networks 2006.

[80] Jesus Arango, A. Efrat, M. Krunz and S. Ramasubramanian, Onroad Vehicular Broadcast, 15th Interna-tional Conference on Computer Communications and Networks (ICCCN) 2006, to appear.

[81] A. Efrat, R. Balasubramanian and S. Ramasubramanian, Coverage time optimization in sensor networks,Third IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS) 2006, to appear.

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[82] Quanfu Fan, A. Amir, K, Barnard, A. Efrat, and L. Ming, Matching Slides To Presentation Videos, ACMSIGMM International Workshop on Multimedia Information Retrieval, 2006, to appear.

[83] Adam L. Buchsbaum Alon Efrat Shaili Jain and Suresh Venkatasubramanian, Restricted Strip Coveringand the Sensor Cover Problem, Symposyum on Discrete Algorithms (SODA) 2007, to appear.

D. Papers in Non-Competitive Venues.

[84] *R. Bar-Yehuda, A. Efrat and A. Itai, A Simple Algorithm for Maintaining the Center of a Planar Point-Set,Proc. 5th Canad. Conf. Comput. Geom. (CCCG), 1993, 252–257.

[85] *A. Efrat, M. Lindenbaum and M. Sharir, Finding Maximally Consistent Sets of Halfspaces, Proc. 5thCanad. Conf. Comput. Geom. (CCCG), 1993, 432–436.

[86] *A. Efrat, M. Sharir and G. Rote, On the Union of Fat Wedges and Separating a Collection of Segments bya Line, Proc. 5th Canad. Conf. Comput. Geom. (CCCG), 1993, 115–120.

[87] A. Efrat, S. G. Kobourov, M. Stepp, and C. Wenk, Growing Fat Graphs, video contribution, Proc. 18thAnnual Symposium on Computational Geometry (SoCG), 277–278, 2002.

[88] C. Wenk, H. Alt, A. Efrat, L. Palaniappan and Gnter Rote, Finding a curve in a map, video contribution,Proc. 19th Annual Symposium on Computational Geometry (SoCG) 384–385, 2003.

[89] A. Efrat, H. H. Gonzalez-Banos, S. Kobourov and L. Palaniappan, Optimal motion strategies to track andcapture a predictable target, Fall Workshop on Computational Geometry, 2003.

[90] C. Wenk, H. Alt, A. Efrat, L. Palaniappan and G. Rote, Finding a curve in a map, video contribution, FallWorkshop on Computational Geometry, 2003.

[91] K. Barnard, A. Connolly, L. Denneau, A. Efrat, J. N. Heasley, R. Jedicke, J. M. Kubica, B. Moon, A.Moore, S. Morris, P. Rao, the LSST moving object pipeline, Observatory operations: strategies, processes,and systems, proceedings of SPIE Vol. #6270, 2006 (to appear).

[92] J. Kubica, T. Axelrod, K. Barnard, A. Connolly, L. Denneau, A. Efrat, J. Heasley, R. Jedicke, B. Moon,A. Moore, S. Morris, P. Rao, ”Efficiently Tracking Moving Sources in the LSST”, the 207th meeting of theAmerican Astronomical Association.

E. Manuscripts and Technical Reports

[93] N. Cohen, A. Efrat and F. Bruckstein, On Ants Crickets and Frogs in Circular Pursuit. Report. CIS-9309.Faculty of Computer Science, Technion - IIT.

[94] Y. Shi, Y.T. Hou, and A. Efrat, ”Design of low-complexity approximation algorithms for base stationplacement problems in sensor networks,” Technical Report, The Bradley Dept. of ECE, Virginia Tech, July2005.

F. In Preparation

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[95] M. L. Narro, F. Yang, R. Kraft, C. Wenk, A. Efrat, and L. L. Restifo, ”NeuronMetrics: Software for RapidSemi-Automated Processing of Cultured-Neuron Images” submitted to Brain Research.

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