(Now Outdated :-)! As an example for the type of research I do, here is the youtube video of the Godfried Toussaint Memorial Lecture I recently gave at the 32nd CCCG in summer 2020.

I work in geometric and topological data analysis. I am particularly interested in (1) developing effective and theoretically justified algorithms to analyze complex data using geometric and topological ideas and methods, and (2) integrating algorithmic, geometric, and topological methods with modern machine learning frameworks, especially in graph learning and in geometric deep learning.

An important theme of my work is to apply theoretical insights obtained to develop effective machine learning methods for practical domains, including chip design, material science, neuroscience, and computational biology.

My work lies at the intersection of computer science (especially algorithms), applied mathematics (especially applied topology, discrete and combinatorial geometry), as well as several application domains. My research has been supported by NSF, NIH and DOE.

I am looking for motivated graduate students with interests in geometric/topological algorithms and data analysis, as well as strong background in algorithms, theory, and/or mathematics. I am happy to work with exceptional undergraduate students on data analysis projects. Students should have strong interests in algorithms design and data analysis applications.

Recent Tutorials:


  • My colleagues Misha Belkin, Ery Arias-Castro, Lily Weng and myself recently gave a two-week long UCSD-MSRI Summer School on Machine Learning in summer 2023. See the course materials (video recordings) here. My part of the lectures is on topological data analysis.
  • I gave a minicourse on "Some theoretical aspects of Graph neural networks (and higher order variants) at IHP (Institut Henri Poincare), Paris, in Oct 2022.

All Journal / Conference Publications -- Chronological order


  • BOOK ANNOUNCEMENT! Computational Topology for Data Analysis.
    T. K. Dey and Y. Wang. Cambridge University Press, April 2022. [PDF version here.] Order link is [here (Cambridge University Press)] or [here (on Amazon)].
  • New! NN-Steiner: A Mixed Neural-Algorithmic Approach for the Rectilinear Steiner Minimum Tree Problem.
    A. B. Kahng, R. R. Nerem, Y. Wang, and C. Yang. 38th Annual AAAI Conference on Artificial Intelligence (AAAI 2024), to appear, 2024.
  • New! Learning ultrametric trees for optimal transport regression.
    S. Chen, P. Tabaghi, and Y. Wang. 38th Annual AAAI Conference on Artificial Intelligence (AAAI 2024), to appear, 2024.
  • New! DE-HNN: An effective neural model for Circuit Netlist representation.
    Z. Luo, T. Hy, P. Tabaghi, D. Koh, M. Defferrard, E. Rezaei, R. Xarey, R. Davis, R. Jain and Y. Wang. 27th Intl. Conf. Artificial Intelligence and Statistics (AISTATS), to appear, 2024.
  • New! On the Theoretical Expressive Power and the Design Space of Higher-Order Graph Transformers.
    C. Zhou, R. Yu, and Y. Wang. 27th Intl. Conf. Artificial Intelligence and Statistics (AISTATS), to appear, 2024.
  • New! Distances for Markov Chains, and Their Differentiation.
    T. Brugere, Z. Wan, and Y. Wang. 35th Intl. Conf. Algorithmic Learning Theory (ALT), to appear, 2024.
  • New! Universal Representation of Permutation-Invariant Functions on Vectors and Tensors.
    P. Tabaghi and Y. Wang. 35th Intl. Conf. Algorithmic Learning Theory (ALT), to appear, 2024.
  • Neural approximation of Wasserstein distance via a universal architecture for symmetric and factorwise group invariant functions.
    S. Chen and Y. Wang. 37th Neural Information Processing Systems (NeurIPS), 2023.
  • Cycle Invariant Positional Encoding for Graph Representation Learning.
    Z. Yan, T. Ma, L. Gao, Z. Tang, C. Chen, and Y. Wang. Learning on Graphs Conference (LoG), 2023 (Oral presentation).
  • The Numerical Stability of Hyperbolic Representation Learning.
    G. Mishne, Z. Wan, Y. Wang and S. Yang. 40th Intl. Conf. Machine Learning (ICML), 2023.
  • On the Connection Between MPNN and Graph Transformer.
    C. Cai, T. S. Hy, R. Yu and Y. Wang. 40th Intl. Conf. Machine Learning (ICML), 2023.
  • Understanding Oversquashing in GNNs through the Lens of Effective Resistance.
    M. Black, A. Nayyeri, Z. Wan and Y. Wang. 40th Intl. Conf. Machine Learning (ICML), 2023.
  • A generalization of the persistent Laplacian to simplicial maps.
    A. Guelen, F. Memoli, Z. Wan and Y. Wang. Intl. Sympos. Comput. Geom. (SoCG), 2023.
  • Implicit Graphon Neural Representation
    X. Xia, G. Mishne, and Y. Wang. 25th International Conference on Artificial Intelligence and Statistics (AISTATS) (selected for Oral Presentation), 2023.
  • Neural approximation of extended persistent homology on graphs
    Z. Yan, T. Ma, L. gao, Z. Tang, Y. Wang and C. Chen. 36th Conf. Neural Infor. Processing Systems (NeurIPS) , 2022.
  • On the clique number of noisy random geometric graphs
    M. Kahle, M. Tian and Y. Wang. Random Structures & Algorithms (RSA), 2023. An earlier version of the paper appeared in arXiv at arXiv:2208.10558 ([link]).
  • Graph skeletonization of high-dimensional point cloud data via topological method
    L. Magee and Y. Wang. Journal on Computational Geometry, 2022. An earlier version appeared on arXiv at arXiv:2109.07606 .
  • Convergence of Invariant Graph Networks
    C. Cai and Y. Wang. 39th Intl. Conf. Machine Learning (ICML), 2457--2484 2022. (Also available on arXiv at arXiv:2201.10129 )
  • Weisfeiler-Lehman meets Gromov-Wasserstein
    S. Chen, S. Lim, F. Memoli, Z. Wan and Y. Wang. 39th Intl. Conf. Machine Learning (ICML), 3371--3416, 2022. (Also available on arXiv at arXiv:2202.02495 )
  • Generative Coarse-Graining of Molecular Conformations.
    W. Wang, M. Xu, C. Cai, B. K. Miller, T. Smidt, Y. Wang, J. Tang, and R. Gómez-Bombarelli. 39th Intl. Conf. Machine Learning (ICML), 23213--23236, 2022.
  • Persistent Laplacians: properties, algorithms and implications.
    F. Memoli, Z. Wan and Y. Wang. SIAM Journal on Mathematics of Data Science (SIMODS), 2022. An earlier version can be found at arXiv:2012.02808 ([link]).
  • Composition design of high-entropy alloys with deep sets learning.
    J. Zhang, C. Cai, G. Kim, Y. Wang and W. Chen. npj (Nature Publishing Group) Computational Materials, 8, Article number 89, 2022. DOI: https://doi.org/10.1038/s41524-022-00779-7 .
  • Equivariant geometric learning for digital rock physics: Estimating formation factor and effective permeability tensors from Morse graph.
    C. Cai, N. Vlassis, L. Magee, R. Ma, Z. Xiong, B. Bahmani, T. Wong, Y. Wang, W. Sun. International Journal for Multiscale Computational Engineering, 2022.
  • NN-Baker: A neural-network infused algorithmic framework for optimization problems on geometric intersection graphs E. McCarty, Q. Zhao, A. Sidiropoulos, and Y. Wang. 35th Annual Conference on Neural Information Processing Systems (NeurIPS 2021). 23023-23035, 2021.
  • Elder-Rule-Staircodes for Augmented Metric Spaces. C. Cai, W. Kim, F. Memoli, Y. Wang. SIAM J. Appl. Algebra Geom. 5(3): 417-454 (2021). DOI: 10.1137/21M1435471
  • The Middle Science: Traversing Scale In Complex Many-Body Systems A. E. Clark, H. Adams, R. Hernandez, A. I. Krylov, A. M. N. Niklasson, S. Sarupria, Y. Wang, S. M. Wild, and Q. Yang. ACS Central Science 7(8), 1271-1287 (2021). DOI: 10.1021/acscentsci.1c00685
  • Approximation algorithms for 1-Wasserstein distance between persistence diagrams.
    S. Chen and Y. Wang. 19th Symposium on Experimental Algorithms. 2021.
  • Graph coarsening with neural networks.
    C. Cai, D. Wang and Y. Wang. Intl. Conf. Learning Representations (ICLR). 2021.
  • Topology-Aware Segmentation Using Discrete Morse Theory.
    X. Hu and Y. Wang and F. Li and D. Samaras and C. Chen. Intl. Conf. Learning Representations (ICLR). 2021.
  • Semantic segmentation of microscopic neuroanatomical data by combining topological priors with encoder-decoder deep networks.
    S. Banerjee, L. Magee, D. Wang, X. Li, B. Huo, J. Jayakumar, K. Matho, M. Lin, K. Ram, M. Sivaprakasam, J. Huang, Y. Wang, and P. Mitra. Nature Machine Intelligence. 2, pages 585-594 (2020). (Also available on biorxiv at 2020.02.18.955237 .)
  • An improved cost function for hierarchical cluster trees
    D. Wang and Y. Wang. J. Comput. Geom. 11(1): 283-331 (2020).
  • Elder-rule-staircodes for augmented metric spaces
    C. Cai, W. Kim, F. Memoli and Y. Wang. In 36th Intl. Sympos. Comput. Geom. (SoCG) , 26:1-26:17, 2020.
  • An efficient algorithm for 1-dimensional (persistent) path homology
    T. K. Dey, T. Li and Y. Wang. In 36th Intl. Sympos. Comput. Geom. (SoCG) , 36:1-36:15, 2020.
  • Persistence enhanced Graph Neural Network.
    Q. Zhao, Z. Ye, C. Chen and Y. Wang. In 23rd Intl. Conf. Artificial Intelligence and Statistics (AISTATS), 2896-2906, 2020.
  • Map matching using shortest paths.
    E. Chambers, B. Fasy, Y. Wang and C. Wenk. ACM Transactions on Spatial Algorithms and Systems, 6(1): 6:1-6:17 (2020).
  • On homotopy types of Vietoris{Rips complexes of metric gluings.
    M. Adamaszek, H. Adams, E. Gasparovic, M. Gommel, E. Purvine, R. Sazdanovic, B. Wang, Y. Wang and L. Ziegelmeier. Journal of Applied and Computational Topology, 4(3): 425-454 (2020).
  • Learning metrics for persistence-based summaries and applications for graph classification
    Q. Zhao and Y. Wang. In 33rd Conf. Neural Information Processing Systems (NeuRIPS), 9855--9866, 2019.
  • The relationship between the intrinsic Cech and persistence distortion distances for metric graphs.
    E. Gasparovic, M. Gommel, E. Purvine, R. Sazdanovic, B. Wang, Y. Wang and L. Ziegelmeier. Journal of Computational Geometry (JoCG), 477-499 (2019).
  • Road Network reconstruction from Satellite Images with Machine Learning Supported by Topological Methods
    T. K. Dey, J. Wang and Y. Wang. In ACM SIGSPATIAL, 520--253, 2019. The full version is at arXiv:1909.06728 ([link]).
  • A Structural Average of Labeled Merge Trees for Uncertainty Visualization.
    L. Yan, Y. Wang, E. Munch, E. Gasparovic, and B. Wang. In IEEE Conference on Scientific Visualization (IEEE Vis, SciVis Track), 2019. Also appear in a special issue of IEEE Transactions on Visualization and Computer Graphics (TVCG) 26(1), 832--842, 2020.
  • FPT-algorithms for computing Gromov-Hausdorff and interleaving distances between trees
    E. Farahbakhsh Touli and Y. Wang. In 27th Annual European Sympos. Algorithms (ESA), 83:1-83:14, 2019. The full version is at arXiv:1811.02425 ([link]).
  • Heuristic Search for Homology Localization Problem and Its Application in Cardiac Trabeculae Reconstruction.
    X. Zhang, P. Wu, C. Yuan, Y. Wang, D. Metaxas, C. Chen. In 28th Intl. Joint Conf. Artificial Intelligence (IJCAI), 1312-1318, 2019.
  • A topological regularizer for classifiers via persistent homology
    C. Chen, X. Ni, Q. Bai and Y. Wang. In AISTATS (22nd Intl. Conf. Artificial Intelligence and Stats) , PMLR 89: 2573--2582, 2019.
  • Genetic Single Neuron Anatomy Reveals Fine Granularity of Cortical Axo-Axonic Cells.
    X. Wang, J. Tucciarone, S. Jiang, F. Yin, B. Wang, D. Wang, Y. Jia, X. Jia, Y. Li, T. Yang, Z. Xu, M. A. Akram, Y. Wang, S. Zeng, G. A. Ascoli, P. Mitra, H. Gong, Q. Luo, and Z. J. Huang. Cell Reports 26(11): 3145--3159.e5, 2019. ([DOI]).
  • SimBa: An Efficient Tool for Approximating Rips-filtration Persistence via Simplicial Batch Collapse.
    T. K. Dey, D. Shi, and Y. Wang. J. Exp. Algorithmics 24, 1, Article 1.5, 16 pages, 2019.
  • Computing the Gromov-Hausdorff distance for metric trees
    P. K. Agarwal, K. Fox, A. Nath, A. Sidiropoulos, and Y. Wang. ACM Trans. Algorithms 14(2): 24:1--24:20 (2018). (An earlier short conference version appeared in ISAAC 2015.)
  • Graph reconstruction by discrete Morse theory
    T. Dey, J. Wang and Y. Wang. 34th Sympos. Comput. Geom (SoCG), 31:1--31:15, 2018.
  • Vietoris-Rips and Cech complexes of metric gluings
    M. Adamaszek, H. Adams, E. Gasparovic, M. Gommel, E. Purvine, R. Sazdanovic, B. Wang, Y. Wang and L. Ziegelmeier. 34th Sympos. Comput. Geom (SoCG), 3:1--3:15, 2018.
  • Unperturbed: spectral analysis beyond Davis-Kahan
    J. Eldridge, M. Belkin and Y. Wang. ALT 2018, 321--358.
  • Uniformization and Density Adaptation for Point Cloud Data Via Graph Laplacian
    C. Luo, X. Ge, and Y. Wang. Comput. Graph. Forum 37(1): 325-337 (2018).
  • Efficient algorithms for computing a minimal homology basis
    T. Dey, T. Li and Y. Wang. LATIN 2018, 376--398, 2018.
  • A complete characterization of the one-dimensional intrinsic Cech persistence diagrams for metric graphs
    E. Gasparovic, M. Gommel, E. Purvine, R. Sazdanovic, B. Wang, Y. Wang and L. Ziegelmeier. Research in Computational Topology, 33--56, 2018.
  • Improved road network reconstructon using Discrete Morse theory
    T. Dey, J. Wang and Y. Wang. GIS SIGSPATIAL 2017.
  • Visualizing attributed graphs via terrain metaphor.
    Y. Zhang, Y. Wang and S. Parthasarathy. 23rd SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2017, pages 1325-1334.
  • Composing Tree Graphical Models with Persistent Homology Features for Clustering Mixed-Type Data.
    X. Ni, N. Quadrianto, Y. Wang and C. Chen. 34th Intl. Conf. Machine Learning (ICML) 2017, pages 2622-2631.
  • A quest to unravel the metric structure behind perturbed networks.
    S. Parthasarathy, D. Sivakoff, M. Tian and Y. Wang. Sympos. Comput. Geom. (SoCG) 2017, 53:1-53:16.
  • Declutter and Resample: Towards parameter free denoising.
    • M. Buchet, T. Dey, J. Wang and Y. Wang. Sympos. Comput. Geom. (SoCG) 2017, 23:1-23:16
  • Topological Analysis of Nerves, Reeb Spaces, Mappers, and Multiscale Mappers.
    T. Dey, F. Memoli and Y. Wang. Sympos. Comput. Geom. (SoCG) 2017, 36:1-36:16.
  • Metric embeddings with outliers
    A. Sidiropoulos, D. Wang and Y. Wang. ACM-SIAM Sympos. Discrete Alg. (SoDA) , pages 670--689, 2017.
  • Parameter-free topology inference and sparsification for data on manifolds
    T. K. Dey, Z. Dong and Yusu Wang. ACM-SIAM Sympos. Discrete Alg. (SoDA) , pages 2733--2747, 2017.
  • Metrics for comparing neuronal tree shapes based on persistent homology
    Y. Li, D. Wang, G. A. Ascoli, P. Mitra and Y. Wang. PLOS One, 12(8), 2017.
  • Optimal topological cycles and their application in cardiac trabeculae restoration
    P. Wu, C. Chen, Y. Wang, S. Zhang, C. Yuan, Z. Qian, D. Metaxas, L. Axel. 25th Biennial Intl. Conf. Information Processing in Medical Imaging (IPMI), 2017, pages 80-92 (Selected for Oral presentation.)
  • Graphons, mergeons, and so on!
    J. Eldridge, M. Belkin and Y. Wang. NIPS 2016, pages 2307-2315. (Selected for Oral presentation.)
  • SimBa: An Efficient Tool for Approximating Rips-Filtration Persistence via Simplicial Batch-Collapse
    T. K. Dey, D. Shi and Y. Wang. Euro. Symp. Algorithms (ESA) 2016, 35:1--35:16. The software package (developed by Dayu Shi) can be downloaded [here]
  • Multiscale Mapper: Topological summarization via codomain covers
    T. K. Dey, F. Memoli and Y. Wang. ACM-SIAM Sympos. Discrete Alg. (SoDA) 2016, pages 997-1013. Also available on arXiv:1504.03763v1
  • Efficient map reconstruction and augmentation via topological methods
    S. Wang, Y. Wang, and Y. Li. ACM SIGSPATIAL 2015. [project page] ( Won Best Paper Award!)
  • Beyond Hartigan consistency: Merge distortion metric for hierarchical clustering
    J. Eldridge, M. Belkin and Y. Wang. Conf. Learning Theory (COLT) 2015, pages 588-606. ( Won Best Student Paper Award! ).
  • Comparing graphs via persistence distortion
    T. Dey, D. Shi and Y. Wang. Sympos. Comput. Geom. (SoCG) 2015 , pages 491-506. Code by Dayu Shi can be downloaded here: [source-zip] and [exe-zip]
  • Strong equivalence of the interleaving and functional distortion metrics for Reeb graphs
    U. Bauer, E. Munch and Y. Wang. Sympos. Comput. Geom. (SoCG) 2015 , pages 461-475.
  • Maintaining contour trees of dynamic terrains
    P.K. Agarwal, T. Molhave, M. Revsbak, I. Safa, Y. Wang and J. Yang. Sympos. Comput. Geom. (SoCG) 2015 , pages 796-811.
  • Topological analysis of scalar fields with outliers
    M. Buchet, F. Chazal, T. Dey, F. Fan, S. Oudot and Y. Wang. Sympos. Comput. Geom. (SoCG) 2015 , pages 827-841.
  • Computing the Gromov-Hausdorff distance for metric trees
    P. Agarwal, K. Fox, A. Nath, A. Sidiroupolos and Y. Wang. ISAAC 2015 .
  • Graph induced complex on point data
    T. K. Dey, F. Fan and Y. Wang. Comput. Geom. 48(8): 575-588, 2015.
  • Learning with Fredholm Kernels
    Q. Que, M. Belkin and Y. Wang. NIPS 2014 , pages 2951-2959.
  • A collaborative visual analytics suite for protein folding research
    W. Harvey, I. Park, O. Rubel, V. Pascucci, P-T. Bremer, C. Li and Y. Wang. Journal of Molecular Graphics and Modelling (JMG) , 53, 59--71, 2014. The link is [here].
  • Measuring Distance Between Reeb Graphs
    U. Bauer, X. Ge, and Y. Wang. ACM Sympos. Comput. Geom. (SoCG) 2014, pages 464-473.
  • JS-Graph of Join and Split Trees
    S. Wang, Y. Wang and R. Wenger. ACM Sympos. Comput. Geom. (SoCG) 2014, pages 539-548.
  • Computing Topological Persistence for Simplicial Maps
    T. K. Dey, F. Fan, and Y. Wang. ACM Sympos. Comput. Geom. (SoCG) 2014, pages 345-354. The paper is [here]. The full version is also on arXiv at [here]. The software package (developed by Dayu Shi) is [here].
  • An efficient computation of handle and tunnel loops via Reeb graphs
    T. K. Dey, F. Fan, and Y. Wang. ACM Trans. Graphics (Special issue from SIGGRAPH), 2013, 32(4): 32:1-32:10. The paper is [here], and the supplementary file for proofs is [here]. The software can be downloaded [here].
  • Bilateral Blue Noise Sampling
    J. Chen, X. Ge, L-Y. Wei, B. Wang, Y. Wang, H. Wang, Y. Fei, K. Qian, J. Yong, W. Wang. ACM Trans. Graphics (Special issue from SIGGRAPH Asia), 2013, 32(4): 216:1-216:11. [Project page]
  • Measuring similarity between curves on 2-manifolds via homotopy area
    with E. W. Chambers. ACM Sympos. Comput. Geom. (SoCG) , 2013. The archive version is [here].
  • Graph induced complex on point data
    with T. K. Dey and F. Fan. ACM Sympos. Comput. Geom. (SoCG) , 2013. Full version is [here.] The accompanying software is [here.]
  • Weighted graph Laplace operator under Topological noise
    with T. K. Dey and P. Ranjan. ACM-SIAM Sympos. Discrete Alg. (SODA) , 2013. [pdf]
  • Reeb Graphs: Approximation and Persistence
    with T. K. Dey. Discrete and Computational Geometry 2012. [pdf]
  • Feature-preserving reconstruction of singular surfaces
    with T. K. Dey, X. Ge, Q. Que, I. Safa, and L. Wang. Computer Graphics Forum (special issue from SGP 2012), 31 (5), 1787--1796. 2012. [pdf]
  • Eigen-deformation of 3D models.
    with T. K. Dey and P. Ranjan. The Visual Computer 28 (6--8): 585--595, 2012. [video] (Conference version appeared in Computer Graphics International (CGI) 2012. )
  • Feature-aware streamline generation of planar vector fields via topological methods.
    with C. Luo and I. Safa. Computers and Graphics 36(6): 754--766, 2012.
  • Approximating Cycles in a Shortest Basis of the First Homology Group from Point Data
    with T. K. Dey and J. Sun. Inverse Problems 2012. [full-version][software by O. Busaryev]
  • Towards understading complex spaces: graph Laplacians on manifolds with singularities and boundaries
    with M. Belkin, Q. Que, and X. Zhou. COLT 2012. , 2012. [pdf]
  • Annotating simplices with a homology basis and its applications
    with O. Busaryev, S. Cabello, C. Chen and T. Dey. SWAT , 189--200, 2012.
  • Smolign: A Spatial Motifs Based Protein Multiple Structural Alignment Method
    with H. Sun, A. Sacan and H. Ferhatosmanoglu. IEEE//ACM Transactions on Computational Biology and Bioinformatics. 2011. [pdf]
  • Data Skeletonization via Reeb Graphs
    with X. Ge, I. Safa and M. Belkin. NIPS 2011. [pdf (full version)][software (in matlab)]
  • Reeb Graphs: Approximation and Persistence
    with T. K. Dey. ACM Symposium on Computational Geometry (SOCG) 2011. [pdf]
  • Enhanced Topology-sensitive Clustering by Reeb Graph Shattering
    with W. Harvey, O. Rubel, V. Pascucci, and P. -T. Bremer. TopoInVis 2011. [pdf]
  • Tracking a generator by persistence
    with O. Busaryev and T. K. Dey. Discrete Mathematics, Algorithms and Applications, 2 (4): 539--552, 2010. [DOI:10.1142/S1793830910000875]
  • Hausdorff distance under translation for points and balls
    with P. K. Agarwal, S. Har-Peled, and M. Sharir. ACM Trans. Alg. 6 (4), 2010. [ pdf ]
  • Persistent Heat Signature for Pose-oblivious Matching of Incomplete Models
    with T. K. Dey, K. Li, C. Luo, P. Ranjan, and I. Safa. Comput. Graph. Forum 2010 (SGP) . [pdf]
  • Generating and Exploring a Collection of Topological Landscapes for Visualization of Scalar-Valued Functions
    with W. Harvey. Comput. Graph. Forum 2010 (EuroVis) . (Won 3rd Best Paper Award at EuroVis 2010! ) [pdf]
  • A Randomized O(m log m) Time Algorithm for Computing Reeb Graph of Arbitrary Simplicial Complexes
    with W. Harvey and R. Wenger. ACM Symposium on Computational Geometry (SOCG) 2010. [pdf] and [code by W. Harvey]
  • Approximating Loops in a Shortest Homology Basis from Point Data
    with T. K. Dey and J. Sun. ACM Symposium on Computational Geometry (SOCG) 2010. [full-version][software by O. Busaryev]
    Note: the title for the journal version is Approximating Cycles in a Shortest Basis of the First Homology Group from Point Data.
  • Convergence, Stability, and Discrete Approximation of Laplace Spectra
    with T. K. Dey and P. Rajan. ACM-SIAM Symposium on Discrete Algorithms (SODA), 2010. [original pdf][modified pdf]
    (The modified pdf has a minor correction in the paper after Lemma 3.3.)
  • Integral Estimation from Point Cloud in d-Dimensional Space: A Geometric View
    with C. Luo and J. Sun. ACM Symposium on Computational Geometry (SOCG) , 2009. [pdf]
  • Constructing Laplace Operator from Point Clouds in R^d
    with M. Belkin and J. Sun. ACM-SIAM Symposium on Discrete Algorithms (SODA), 2009, 1031--1040. [pdf] [code (by J. Sun) ]
  • Exact Partial Curve Matching under the Frechet Distance
    ACM-SIAM Symposium on Discrete Algorithms (SODA), 2009, 645--654. [Original-version]
    with K. Buchin and M. Buchin, [ !! Improved-version !!]
  • Approximating Gradients for Meshes and Point Clouds via Diffusion Metric
    with C. Luo and I. Safa. Comput. Graph. Forum 2009, 28(5): 1497--1508. [pdf]
  • An enhanced partial order curve comparison algorithm and its application to analyzing protein folding trajectories
    with H. Sun and H. Ferhatosmanoglu. BMC BioInformatics 2008, 9 : 344. [web-server] [pdf ]
  • Distributed Roadmap Aided Routing in Sensor Networks
    with Z. Zheng, K. Fan, and P. Sinha. IEEE MASS 2008, 347--352. [pdf]
  • Discrete Laplace Operator for Meshed Surfaces
    with M. Belkin and J. Sun. ACM SOCG 2008. [pdf ] [code (by J. Sun) ]
  • Approximating Nearest Neighbor Among Triangles in Convex Position
    Information Processing Letters. 2008. [ pdf ]
  • Towards Unsupervised Segmentation of Semi-rigid Low-resolution Molecular Surfaces
    with L. J. Guibas. Algorithmica. 48 (4): 433--448 (Aug. 2007). [ pdf ]
  • Placement-Proximity-Based Voltage Island Grouping under Performance Requirement
    with H. Wu, M. Wong, and I. Liu. IEEE Trans. Computer-Aided Design. 26 (7): 1256--1269 (July 2007). [ pdf ]
  • LFM-Pro: A Tool for Detecting Significant Local Structural Sites in Proteins
    with A. Sacan, O. Ozturk and H. Ferhatosmanoglu. BioInformatics. 23 (6): 709--716 (Mar. 2007). [ pdf ]
  • Efficient Algorithms for Contact-map Overlap Problem
    with P. K. Agarwal and N. Mustafa. J. Comput. Biology (JCB) . 14 (2): 131--143 (Mar. 2007). [ pdf ]
  • An Enhanced Partial Order Curve Comparison over Multiple Protein Folding Trajectories
    with H. Sun, H. Ferhatosmanoglu, and M. Ota, Proc. Intl. Conf. Computational Systems Bioinformatics, 2007, 299--310.
  • Relations Between Two Common Types of Rectangular Tiling
    Proc. Intl. Symp. Algorithms and Computation, LNCS 4288, Springer-Verlag, 2006, 193--202.
  • Frechet Distances for Curves, Revisited
    with B. Aronov, S. Har-Peled, C. Kauner and C. Wenk, ESA 2006, 52--63.
  • Distance-sensitive routing and information brokerage in sensor networks
    with S. Funke, L. J. Guibas and A. Nguyen. DOCSS 2006, 234--251.
  • A Two-Dimensional Kinetic Triangulation with Near-Quadratic Topological Changes
    with P. K. Agarwal and H. Yu. Discrete and Computational Geometry (DCG) 36 (4): 573--592 (Dec. 2006). [ pdf ]
  • Extreme Elevation on a 2-Manifold
    with P. K. Agarwal, H. Edelsbrunner, and J. Harer. Discrete and Computational Geometry (DCG). 36 (4): 553--572 (Dec. 2006).
  • Segmenting molecular surfaces
    with V. Natarajan, P. Bremer, V. Pascucci and B. Hamann. Computer Aided Geometric Design (CAGD). 23: 495--509 (June 2006). [ pdf ]
  • Near-linear time approximation algorithms for curve simplification in two and three-dimensions
    with P. K. Agarwal, S. Har-Peled, and N. Mustafa. Algorithmica. 42(3/4): 203--221 (2005). [ pdf ]
  • Post-placement voltage island generation under performance requirement
    with H. Wu, I. Liu, and M. D. F. Wong, ICCAD 2005, 309--316
  • Low bounds for sparse geometric spanners
    with P. K. Agarwal and P. Yin, SODA 2005, 670--671
  • Coarse and reliable geometric alignment for protein docking
    with P. K. Agarwal, P. Brown, H. Edelsbrunner and J. Rudolph, PSB 2005, 66--77
  • Shape fitting with outliers
    with S. Har-Peled. SIAM J. Comput. 33(2): 269--285 (2004). [ pdf ]
  • Computing the writhing number of a polygonal knot
    with P. K. Agarwal and H. Edelsbrunner. Discrete and Computational Geometry (DCG) 32(1): 37--53 (2004). [ pdf ]
  • A 2D kinetic triangulation with near-quadratic topological changes
    with P. K. Agarwal and H. Yu, SOCG 2004, 180--189
  • Extreme elevation on a 2-manifold
    with P. K. Agarwal, H. Edelsbrunner and J. Harer, SOCG 2004, 357--365
  • Hausdorff distance under translation for points and balls
    with P. K. Agarwal, S. Har-Peled and M. Sharir, SOCG 2003, 282--291
  • Shape fitting with outliers
    with S. Har-Peled, SOCG 2003, 29--38
  • Near-linear time approximation algorithms for curve simplification
    with P. K. Agarwal, S. Har-Peled, and N. Mustafa, ESA 2002, 29--41
  • Computing the writhing number of a polygonal knot
    with P. K. Agarwal and H. Edelsbrunner, SODA 2002, 791--799
  • Occlusion culling for fast walkthrough in urban areas
    with P. K. Agarwal and S. Har-Peled, EuroGraphics (short paper) 2001.