Motion Planning

  • H.H. Gonzαlez-Baños, D. Hsu, and J.C. Latombe. Motion planning: Recent developments. In S.S. Ge and F.L. Lewis, editors, Autonomous Mobile Robots: Sensing, Control, Decision-Making and Applications, CRC Press, 2006.
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  • M. Erdmann, D. Hsu, M. Overmars, and F. van der Stappen, editors. Algorithmic Foundations of Robotics VI, Springer, 2005.
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  • J. Basch, L.J. Guibas, D. Hsu, and A.T. Nguyen. Disconnection proofs for motion planning. In Proc. IEEE Int. Conf. on Robotics & Automation, pp. 1765–1772, 2001.
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Probabilistic Motion Planning

  • L.J. Guibas, D. Hsu, H. Kurniawati, and E. Rehman. Bounded uncertainty roadmaps for path planning. In Proc. Int. Workshop on the Algorithmic Foundations of Robotics (WAFR), 2008.
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  • D. Hsu, J.C. Latombe, and H. Kurniawati. On the probabilistic foundations of probabilistic roadmap planning. Int. J. Robotics Research, 25(7):627–643, 2006.
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  • D. Hsu, J.C. Latombe, and H. Kurniawati. On the probabilistic foundations of probabilistic roadmap planning. In Proc. Int. Symp. on Robotics Research, 2005.
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  • D. Hsu. Randomized Single-query Motion Planning in Expansive Spaces. Ph.D. Thesis, Dept. of Computer Science, Stanford University, Stanford, CA, 2000.
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  • D. Hsu, J.C. Latombe, R. Motwani, and L.E. Kavraki. Capturing the connectivity of high-dimensional geometric spaces by parallelizable random sampling techniques. In P.M. Pardalos and S. Rajasekaran, editors, Advances in randomized parallel computing, pp. 159–182, Kluwer Academic Publishers, Boston, MA, 1999.
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  • D. Hsu, J.C. Latombe, and R. Motwani. Path planning in expansive configuration spaces. Int. J. Computational Geometry & Applications, 9(4-5):495–512, 1999.
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  • D. Hsu, J.C. Latombe, and R. Motwani. Path planning in expansive configuration spaces. In Proc. IEEE Int. Conf. on Robotics & Automation, pp. 2719–2726, 1997.
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Sampling Strategies for PRM Planning

  • H.-L. Cheng, D. Hsu, J.-C. Latombe, and G. Sαnchez-Ante. Multi-level free-space dilation for sampling narrow passages in PRM planning. In Proc. IEEE Int. Conf. on Robotics & Automation, pp. 1255–1260, 2006.
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  • H. Kurniawati and D. Hsu. Workspace-based connectivity oracle: An adaptive sampling strategy for PRM planning. In S. Akella and others, editors, Proc. Int. Workshop on the Algorithmic Foundations of Robotics (WAFR), Springer, 2006.
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  • D. Hsu, G. Sαnchez-Ante, and Z. Sun. Hybrid PRM sampling with a cost-sensitive adaptive strategy. In Proc. IEEE Int. Conf. on Robotics & Automation, pp. 3885–3891, 2005.
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  • Z. Sun, D. Hsu, T. Jiang, H. Kurniawati, and J. Reif. Narrow passage sampling for probabilistic roadmap planners. IEEE Trans. on Robotics, 21(6):1105–1115, 2005.
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  • D. Hsu and Z. Sun. Adaptively combining multiple sampling strategies for probabilistic roadmap planning. In Proc. IEEE Conf. on Robotics, Automation, and Mechatronics, pp. 774–779, 2004.
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  • D. Hsu and Z. Sun. Adaptive hybrid sampling for probabilistic roadmap planning. Technical Report TRA5/04, National University of Singapore, School of Computing, 2004.
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  • H. Kurniawati and D. Hsu. Workspace importance sampling for probabilistic roadmap planning. In Proc. IEEE/RSJ Int. Conf. on Intelligent Robots & Systems, pp. 1618–1623, 2004.
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  • D. Hsu, T. Jiang, J. Reif, and Z. Sun. The bridge test for sampling narrow passages with probabilistic roadmap planners. In Proc. IEEE Int. Conf. on Robotics & Automation, pp. 4420–4426, 2003.
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  • D. Hsu, L.E. Kavraki, J.C. Latombe, R. Motwani, and S. Sorkin. On finding narrow passages with probabilistic roadmap planners. In P.K. Agarwal and others, editors, Robotics: The Algorithmic Perspective---Proc. Workshop on the Algorithmic Foundations of Robotics (WAFR), pp. 141–154, A. K. Peters, Wellesley, MA, 1998.
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Kinodynamic Motion Planning

  • D. Hsu, R. Kindel, J.C. Latombe, and S. Rock. Randomized kinodynamic motion planning with moving obstacles. Int. J. Robotics Research, 21(3):233–255, 2002.
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  • D. Hsu, R. Kindel, J.C. Latombe, and S. Rock. Control-based randomized motion planning for dynamic environments. In B.R. Donald and others, editors, Algorithmic and Computational Robotics: New Directions---Proc. Workshop on the Algorithmic Foundations of Robotics (WAFR), pp. 247–264, A. K. Peters, Wellesley, MA, 2000.
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  • R. Kindel, D. Hsu, J.C. Latombe, and S. Rock. Kinodynamic motion planning amidst moving obstacles. In Proc. IEEE Int. Conf. on Robotics & Automation, pp. 537–543, 2000.
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Motion Planning Applications

  • J. Yin, Y. Wang, and D. Hsu. Digital violin tutor: An integrated system for beginning violin learners. In Proc. ACM Multimedia, pp. 976–985, 2005.
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  • J. Yin, A. Dhanik, D. Hsu, and Y. Wang. The creation of a music-driven digital violinist. In Proc. ACM Multimedia, pp. 476–479, 2004.
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  • D. Hsu, J.C. Latombe, and S. Sorkin. Placing a robot manipulator amid obstacles for optimized execution. In Proc. IEEE Int. Symp. on Assembly & Task Planning, pp. 280–285, 1999.
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Target Tracking

  • T. Bandyopadhyay, N. Rong, Ang Jr., M.H., D. Hsu, and W.S. Lee. Motion planning for people tracking in uncertain and dynamic environments. In IEEE Int. Conf. on Robotics & Automation, Workshop on People Detection & Tracking, 2009.
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  • T. Bandyopadhyay, D. Hsu, and Ang Jr., M.H.. Motion strategies for people tracking in cluttered dynamic environments. In Proc. Int. Symp. on Experimental Robotics, 2008.
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  • D. Hsu, W.S. Lee, and N. Rong. A point-based POMDP planner for target tracking. In Proc. IEEE Int. Conf. on Robotics & Automation, pp. 2644–2650, 2008.
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  • T. Bandyopadhyay, Ang Jr., M.H., and D. Hsu. Motion planning for 3-D target tracking among obstacles. In Proc. Int. Symp. on Robotics Research, 2007.
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  • T. Bandyopadhyay, Y.P. Li, Ang Jr., M.H., and D. Hsu. A greedy strategy for tracking a locally predictable target among obstacles. In Proc. IEEE Int. Conf. on Robotics & Automation, pp. 2342–2347, 2006.
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  • T. Bandyopadhyay, Y.P. Li, Ang Jr., M.H., and D. Hsu. Stealth tracking of an unpredictable target among obstacles. In M. Erdmann and others, editors, Algorithmic Foundations of Robotics VI---Proc. Workshop on the Algorithmic Foundations of Robotics (WAFR), pp. 43–58, Springer, 2004.
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POMDP Planning

  • Z. Zhang, D. Hsu, W.S. Lee, Z.W. Lim, and A. Bai. PLEASE: Palm leaf search for POMDPs with large observation spaces. In Proc. Int. Conf. on Automated Planning & Scheduling, 2015.
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  • H.Y. Bai, D. Hsu, and W.S. Lee. Integrated perception and planning in the continuous space: A POMDP approach. Int. J. Robotics Research, 33(9):1288–1302, 2014.
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  • Z. Zhang, D. Hsu, and L.S. Lee. Covering number for efficient heuristic-based POMDP planning. In Proc. Int. Conf. on Machine Learning. 2014.
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  • A. Somani, N. Ye, D. Hsu, and W.S. Lee. DESPOT: online POMDP planning with regularization. In Advances in Neural Information Processing Systems (NIPS). 2013.
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  • H.Y. Bai, D. Hsu, and W.S. Lee. Integrated perception and planning in the continuous space: A POMDP approach. In Proc. Robotics: Science and Systems, 2013.
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  • Z.W. Lim, D. Hsu, and W.S. Lee. Monte Carlo value iteration with macro-actions. In Advances in Neural Information Processing Systems (NIPS), 2011.
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  • H.Y. Bai and D. Hsu and W.S. Lee and V.A. Ngo. Monte Carlo value iteration for continuous-state POMDPs. In D. Hsu et al., editors, Algorithmic Foundations of Robotics IX---Proc. Int. Workshop on the Algorithmic Foundations of Robotics (WAFR), 2010.
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  • H. Kurniawati and Y. Du and D. Hsu and W.S. Lee. Motion planning under uncertainty for robotic tasks with long time horizons. Int. J. Robotics Research, 30(3):308-323, 2011.
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  • S.C.W. Ong, S.W. Png, D. Hsu, and W.S. Lee. Planning under Uncertainty for Robotic Tasks with Mixed Observability. Int. J. Robotics Research, 29(8):1053–1068, 2010.
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  • H. Kurniawati, Y. Du, D. Hsu, and W.S. Lee. Motion planning under uncertainty for robotic tasks with long time horizons. In Proc. Int. Symp. on Robotics Research, 2009.
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  • S.C.W. Ong, S.W. Png, D. Hsu, and W.S. Lee. POMDPs for robotic tasks with mixed observability. In Proc. Robotics: Science and Systems, 2009.
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  • H. Kurniawati, D. Hsu, and W.S. Lee. SARSOP: Efficient point-based POMDP planning by approximating optimally reachable belief spaces. In Proc. Robotics: Science and Systems, 2008.
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  • D. Hsu, W.S. Lee, and N. Rong. What makes some POMDP problems easy to approximate?. In Advances in Neural Information Processing Systems (NIPS), 2007.
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  • D. Hsu, W.S. Lee, and N. Rong. Accelerating point-based POMDP algorithms through successive approximations of the optimal reachable space. Technical Report TRA4/07, National University of Singapore. School of Computing, 2007.
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Planning & Learning under Uncertainty

  • Z.W. Lim, D. Hsu, and W.S. Lee. Adaptive stochastic optimization: from sets to paths. In Advances in Neural Information Processing Systems, 2015.
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  • Z.W. Lim, D. Hsu, and W.S. Lee. Adaptive informative path planning in metric spaces. In Algorithmic Foundations of Robotics XI—Proc. Int. Workshop on the Algorithmic Foundations of Robotics (WAFR). 2014.
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  • P. Chaudhari, S. Karaman, D. Hsu, and E. Frazzoli. Sampling-based algorithms for continuous-time POMDPs. In Proc. American Control Conf., 2013.
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  • H.Y. Bai, D. Hsu, and W.S. Lee. Planning how to learn. In Proc. IEEE Int. Conf. on Robotics & Automation, 2013.
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  • T. Bandyopadhyay, K.S. Won, E. Frazzoli, D. Hsu, W.S. Lee, and D. Rus. Intention-aware motion planning. In Proc. Int. Workshop on the Algorithmic Foundations of Robotics (WAFR), 2012.
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  • T.H.D. Nguyen and D. Hsu and W.S. Lee and T.Y. Leong and L.P. Kaelbling and T. Lozano-Perez and A.H. Grant. CAPIR: Collaborative action planning with intention recognition. Proc. AI and Interactive Digital Entertainment Conference, 2011.
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  • L.L. Ko, D. Hsu, W.S. Lee, and S.C.W. Ong. Structured parameter elicitation. In Proc. AAAI Conf. on Artificial Intelligence, 2010.
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Reinforcement Learning

  • A. Kupcsik, D. Hsu, and W.S. Lee. Learning dynamic robot-to-human object handover from human feedback. In Proc. Int. Symp. on Robotics Research, 2015.
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  • Y. Wang, K.S. Won, D. Hsu, and W.S. Lee. Monte Carlo Bayesian reinforcement learning. In Proc. Int. Conf. on Machine Learning, 2012.
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POMDP Applications

  • K. Wu, W.S. Lee, and D. Hsu. POMDP to the rescue: Boosting performance for Robocup rescue. In Proc. IEEE/RSJ Int. Conf. on Intelligent Robots & Systems, 2015.
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  • V. Sezer, T. Bandyopadhyay, D. Rus, E. Frazzoli, and D. Hsu. Towards autonomous navigation of unsignalized intersections under uncertainty of human driver intent. In Proc. IEEE/RSJ Int. Conf. on Intelligent Robots & Systems, 2015.
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  • H.Y. Bai, S.J. Cai, N. Ye, D. Hsu, and W.S. Lee. Intention-aware online POMDP planning for autonomous driving in a crowd. In Proc. IEEE Int. Conf. on Robotics & Automation, 2015.
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  • X.X. Wang, Y. Wang, D. Hsu, and Y. Wang. Exploration in interactive personalized music recommendation: A reinforcement learning approach. ACM Trans. on Multimedia Computing, Communications & Applications, 11(1), 2014.
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  • H.Y. Bai and D. Hsu and Mykel J. Kochenderfer and W.S. Lee. Unmanned aircraft collision avoidance using continuous-state POMDPs. In Proc. Robotics: Science and Systems, 2011.
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  • Y.Z. Du, D. Hsu, H. Kurniawati, W.S. Lee, S.C.W. Ong, and S.W. Png. A POMDP Approach to Robot Motion Planning under Uncertainty. In Int. Conf. on Automated Planning & Scheduling, Workshop on Solving Real-World POMDP Problems, 2010.
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  • S.C.W. Ong, D. Hsu, W.S. Lee, and H. Kurniawati. Partially observable Markov decision process POMDP technologies for sign language based human-computer interaction. In Proc. Int. Conf. on Human-Computer Interaction, 2009.
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Computational Structural Biology

  • B. Gipson, D. Hsu, L.E. Kavraki, and J.C. Latombe. Computational models of protein kinematics and dynamics: Beyond simulation. Annu. Rev. Anal. Chem, 5:273–291, 2012. DOI: 10.1146/annurev-anchem-062011-143024.
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  • T.-H. Chiang, D. Hsu, and J.C. Latombe. Markov dynamic models for long-timescale protein motion. Bioinformatics, 26(12):i269–i277, 2010. Special issue on Int. Conf. on Intelligent Systems for Molecular Biology (ISMB) 2010.
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  • A. Nigham and D. Hsu. Protein conformational flexibility analysis with noisy data. J. Computational Biology, 15(7):813–828, 2008.
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  • A. Nigham, L. Tucker-Kellogg, I. Mihalek, C. Verma, and D. Hsu. pFlexAna: Detecting conformational changes in remotely related proteins. Nucleic Acids Res., 36:W246–W251, 2008. DOI: 10.1093/nar/gkn259.
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  • T.-H. Chiang, M.S. Apaydin, D.L. Brutlag, D. Hsu, and J.C. Latombe. Using stochastic roadmap simulation to predict experimental quantities in protein folding kinetics: Folding rates and phi-values. J. Computational Biology, 14(5):578–593, 2007.
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  • A. Nigham and D. Hsu. Protein conformational flexibility analysis with noisy data. In Proc. ACM Int. Conf. on Computational Biology (RECOMB), pp. 396–411, 2007.
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  • T.-H. Chiang, M.S. Apaydin, D.L. Brutlag, D. Hsu, and J.C. Latombe. Predicting experimental quantities in protein folding kinetics using stochastic roadmap simulation. In Proc. ACM Int. Conf. on Computational Biology (RECOMB), pp. 410–424, Springer, 2006.
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  • M.S. Apaydin, D.L. Brutlag, C. Guestrin, D. Hsu, J.C. Latombe, and C. Varma. Stochastic roadmap simulation: an efficient representation and algorithm for analyzing molecular motion. J. Computational Biology, 10(3-4):247–281, 2003.
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  • M.S. Apaydin, D.L. Brutlag, C. Guestrin, D. Hsu, and J.C. Latombe. Stochastic roadmap simulation: An efficient representation and algorithm for analyzing molecular motion. In Proc. ACM Int. Conf. on Computational Biology (RECOMB), pp. 12–21, 2002.
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  • M.S. Apaydin, D.L. Brutlag, C. Guestrin, D. Hsu, and J.C. Latombe. Stochastic conformational roadmaps for computing ensemble properties of molecular motion. In J.-D. Boissonnat and others, editors, Algorithmic Foundations of Robotics V---Proc. Workshop on the Algorithmic Foundations of Robotics (WAFR), pp. 131–148, Springer, 2002.
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  • R.-P. Berretty, D. Hsu, L. Kettner, A. Mascarenhas, M.R. Redinbo, and J. Snoeyink. Ligand binding to the pregnane X receptor by geometric matching of hydrogen bonding. In L. Florea and others, editors, Currents in Computational Molecular Biology, pp. 22–23, 2002. The booklet contains extended poster abstracts from RECOMB 2002.
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  • D. Brutlag, M.S. Apaydin, C. Guestrin, D. Hsu, C. Varma, A. Singh, and J.-C. Latombe. Using robotics to fold proteins and dock ligands. Bioinformatics, 18:S74–S83, 2002.
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Computational Systems Biology

  • G.M. Gyori, G. Venkatachalam, P.S. Thiagarajan, D. Hsu, and M.V. Clement. OpenComet: An automated tool for comet assay image analysis. Redox Biology, 2(2):457–465, 2014.
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  • S.K. Palaniappan, B.M. Gyori, B. Liu, D. Hsu, and P.S. Thiagarajan. Statistical model checking based calibration and analysis of bio-pathway models. In Proc. Conf. on Computational Methods in Systems Biology, 2013.
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  • G. Koh, D. Hsu, and P. S. Thiagarajan. Component-based construction of bio-pathway models: The parameter estimation problem. Theoretical Computer Science, 412(26):2840–2853, 2011.
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  • B. Liu, D. Hsu, and P. S. Thiagarajan. Probabilistic approximations of ODEs-based bio-pathway dynamics. Theoretical Computer Science, 412(21):2188-2206, 2011.
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  • B. Liu, J. Zhang, P.Y. Tan, D. Hsu, A.M. Blom, B. Leong, S. Sethi, B. Ho, J.L. Ding, and P.S. Thiagarajan. A computational and experimental study of the regulatory mechanisms of the complement system. PloS Computational Biology, 7(1), 2011.
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  • G. Koh, D. Hsu, and P.S. Thiagarajan. Incremental signaling pathway modeling by data integration. In Proc. ACM Int. Conf. on Computational Biology (RECOMB), 2010.
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  • B. Liu, P.S. Thiagarajan, and D. Hsu. Probabilistic approximations of bio-pathway dynamics. In ACM Int. Conf. on Computational Biology (RECOMB) Poster Book, 2009.
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  • B. Liu, P.S. Thiagarajan, and D. Hsu . Probabilistic approximations of signaling pathway dynamics. In Proc. Conf. on Computational Methods in Systems Biology, 2009.
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  • G. Koh, D. Hsu, and P.S. Thiagarajan. Composition of signaling pathway models and its application to parameter estimation. In ACM Int. Conf. on Computational Biology (RECOMB) Poster Book, 2008.
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  • G. Koh, L. Tucker-Kellogg, D. Hsu, and P.S. Thiagarajan. Globally consistent pathway parameter estimates through belief propagation. In Proc. Workshop on Algorithms in Bioinformatics (WABI), 2007.
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  • G. Koh, H.F.C. Teong, M.-V. Clιment, D. Hsu, and P.S. Thiagarajan. A decompositional approach to parameter estimation in pathway modeling: A case study of the Akt and MAPK pathways and their crosstalk. Bioinformatics, 22(14):271–280, 2006. Special issue on Int. Conf. on Intelligent Systems for Molecular Biology (ISMB) 2006.
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Geometric Computation

  • X. Wu, D. Hsu, and A.K.H. Tung. Efficient constrained Delaunay triangulation for large spatial databases. Technical Report TRA1/06, National University of Singapore, School of Computing, 2006.
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  • C. Xia, D. Hsu, and A.K.H. Tung. A fast filter for obstructed nearest neighbour queries. In Proc. British National Conferences on Databases, 2004.
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  • L.J. Guibas, D. Hsu, and L. Zhang. A hierarchical method for real-time distance computation among moving convex bodies. Computational Geometry: Theory and Applications, 15(1-3):51–68, 2000.
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  • L.J. Guibas, D. Hsu, and L. Zhang. H-Walk: hierarchical distance computation for moving convex bodies. In Proc. ACM Symp. on Computational Geometry, pp. 265–273, 1999.
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  • H. Alt, D. Hsu, and J. Snoeyink. Computing the largest inscribed isothetic rectangle. In Proc. Canadian Conf. on Computational Geometry, pp. 67–72, 1995.
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