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Below you find a complete list of publications published and edited by scientists at the Department of Computer Science

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Tziavelis, N., Giannakopoulos, I., Doka, K., Koziris, N. & Karras, P. (2019). Equitable Stable Matchings in Quadratic Time. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox & R. Garnett (Eds.), Advances in Neural Information Processing Systems 32 (NIPS 2019): Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019 (Vol. 32, pp. 455-465). Curran Associates, Inc.. https://papers.nips.cc/paper/8337-equitable-stable-matchings-in-quadratic-time

Tzavelis, N., Giannakopoulos, I., Johansen, R. Q., Doka, K., Koziris, N. & Karras, P. (2020). Fair Procedures for Fair Stable Marriage Outcomes. In AAAI 2020 - 34th AAAI Conference on Artificial Intelligence (Vol. 34, no. 5, pp. 7269-7276). AAAI Press. https://doi.org/10.1609/aaai.v34i05.6218

Turon, A., Dreyer , D. & Birkedal, L. (2013). Unifying refinement and hoare-style reasoning in a logic for higher-order concurrency. In G. Morrisett & T. Uustalu (Eds.), Proceedings of the 18th ACM SIGPLAN International Conference on Functional Programming, ICFP '13 (pp. 377-390 ). Association for Computing Machinery. https://doi.org/10.1145/2500365.2500600

Türkmen, R., Pfeuffer, K., Barrera MacHuca, M. D., Batmaz, A. U. & Gellersen, H. (2022). Exploring Discrete Drawing Guides to Assist Users in Accurate Mid-air Sketching in VR. In CHI 2022 - Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems Article 276 Association for Computing Machinery. https://doi.org/10.1145/3491101.3519737

Turkmen, R., Gelmez, Z. E., Batmaz, A. U., Stuerzlinger, W., Asente, P., Sarac, M., Pfeuffer, K. & Machuca, M. D. B. (2024). EyeGuide & EyeConGuide: Gaze-based Visual Guides to Improve 3D Sketching Systems. In CHI 2024 - Proceedings of the 2024 CHI Conference on Human Factors in Computing Sytems Article 178 Association for Computing Machinery. https://doi.org/10.1145/3613904.3641947

Tunç, H. C., Mathur, U., Pavlogiannis, A. & Viswanathan, M. (2023). Sound Dynamic Deadlock Prediction in Linear Time. Proceedings of the ACM on Programming Languages , 7, 1733-1758. https://doi.org/10.1145/3591291

Tunç, H. C., Abdulla, P. A., Chakraborty, S., Krishna, S., Mathur, U. & Pavlogiannis, A. (2023). Optimal Reads-From Consistency Checking for C11-Style Memory Models. Proceedings of the ACM on Programming Languages , 7(PLDI), 761–785. Article 137. https://doi.org/10.1145/3591251

Tunç, H. C., Deshmukh, A. P., Çirisci, B., Enea, C. & Pavlogiannis, A. (2024). CSSTs: A Dynamic Data Structure for Partial Orders in Concurrent Execution Analysis. In ASPLOS '24: Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (Vol. 3, pp. 223-238). Association for Computing Machinery. https://doi.org/10.1145/3620666.3651358

Tunç, H. C. (2025). Efficient Data Structures and Algorithms for Dynamic Analysis of Concurrent Programs. [PhD thesis, Aarhus University].

Tunç, H. C., Dong, Y., Deshmukh, A. P., Cirisci, B., Enea, C. & Pavlogiannis, A. (2025). Efficient Dynamic Concurrency Analysis with Collective Sparse Segment Trees. ACM transactions on computer systems. Advance online publication. https://doi.org/10.1145/3773085

Tu, S., Aslay, C. & Gionis, A. (2020). Co-exposure maximization in online social networks. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan & H. Lin (Eds.), Advances in Neural Information Processing Systems: NeurIPS 2020 (Vol. 33). Neural Information Processing Systems Foundation. https://proceedings.neurips.cc/paper/2020/hash/212ab20dbdf4191cbcdcf015511783f4-Abstract.html

Tsourakakis, C. E., Mitzenmacher, M., Larsen, K. G., Blasiok, J., Lawson, B., Nakkiran, P. & Nakos, V. (2018). Predicting Positive and Negative Links with Noisy Queries: Theory Practice. arxiv.org. http://arxiv.org/abs/1709.07308

Tsitsulin, A., Mottin, D., Karras, P., Bronstein, A. & Müller, E. (2018). NetLSD: Hearing the Shape of a Graph. In Y. Guo & F. Farooq (Eds.), KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 2347-2356). Association for Computing Machinery. https://doi.org/10.1145/3219819.3219991

Tsitsulin, A., Mottin, D., Karras, P. & Müller, E. (2018). VERSE: Versatile Graph Embeddings from Similarity Measures. In P.-A. Champin, F. Gandon & L. Médini (Eds.), The Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018: WWW '18 (pp. 539-548). Association for Computing Machinery. https://doi.org/10.1145/3178876.3186120

Tsitsulin, A., Mottin, D., Karras, P., Bronstein, A. & Müller, E. (2019). Spectral graph complexity. In L. Liu & R. White (Eds.), The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019 (pp. 308-309). Association for Computing Machinery. https://doi.org/10.1145/3308560.3316589

Tsitsulin, A., Munkhoeva, M., Mottin, D., Karras, P., Bronstein, A., Oseledets, I. & Müller, E. (2020). The Shape of Data: Intrinsic Distance for Data Distributions. Paper presented at The International Conference on Learning Representations (ICLR), Ababa, Ethiopia.

Tsitsulin, A., Munkhoeva, M., Mottin, D., Karras, P., Oseledets, I. & Mueller, E. (2021). FREDE: Anytime Graph Embeddings. Proceedings of the VLDB Endowment, 14(6), 1102-1110. https://doi.org/10.14778/3447689.3447713

Tsirogiannis, K. & de Berg, M. (2011). Exact and approximate computations of watersheds on triangulated terrains. In Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (pp. 74-83). Association for Computing Machinery. https://doi.org/10.1145/2093973.2093985

Tsirogiannis, K. & Haverkort, H. (2011). Flow on noisy terrains: an experimental evaluation. In Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (pp. 84-91). Association for Computing Machinery. https://doi.org/10.1145/2093973.2093986

Tsirogiannis, C., Sandel, B. S. & Cheliotis, D. (2012). Efficient Computation of Popular Phylogenetic Tree Measures. Lecture Notes in Computer Science, 7534, 30-43. https://doi.org/10.1007/978-3-642-33122-0_3

Tsirogiannis, C. & Sandel, B. S. (2013). Computing the Skewness of the Phylogenetic Mean Pairwise Distance in Linear Time. In A. Darling & J. Stoye (Eds.), Algorithms in Bioinformatics: 13th International Workshop, WABI 2013, Sophia Antipolis, France, September 2-4, 2013. Proceedings (pp. 170-184). Springer VS. https://doi.org/10.1007/978-3-642-40453-5_14

Tsirogiannis, C., Sandel, B. S. & Kalvisa, A. (2014). New Algorithms for Computing Phylogenetic Biodiversity. In D. Brown & B. Morgenstern (Eds.), Algorithms in Bioinformatics: 14th International Workshop, WABI 2014, Wroclaw, Poland, September 8-10, 2014. Proceedings (pp. 187-203). Springer VS. https://doi.org/10.1007/978-3-662-44753-6_15

Tsirogiannis, C. & Sandel, B. S. (2014). Computing the Skewness of the Phylogenetic Mean Pairwise Distance in Linear Time. Algorithms for Molecular Biology, 9(1), Article 15. https://doi.org/10.1186/1748-7188-9-15

Tsirogiannis, C. & Tsirogiannis, C. (2016). Uncovering the Hidden Routes: Algorithms for Identifying Paths and Missing Links in Trade Networks . In T. Brughmans, A. Collar & F. Coward (Eds.), The Connected Past: Challenges to Network Studies in Archaeology and History Oxford University Press.

Tsirogiannis, C. & Sandel, B. S. (2016). PhyloMeasures: A package for computing phylogenetic biodiversity measures and their statistical moments. Ecography, 39(7), 709-714. https://doi.org/10.1111/ecog.01814

Tsirogiannis, C. & Sandel, B. (2016). Fast phylogenetic biodiversity computations under a non-uniform random distribution. In M. Singh (Ed.), Research in Computational Molecular Biology - 20th Annual Conference, RECOMB 2016, Proceedings (Vol. 9649, pp. 225-236). Springer VS. https://doi.org/10.1007/978-3-319-31957-5_16

Tsirogiannis, C. & Sandel, B. (2016). Fast computations for measures of phylogenetic beta diversity. PLoS One, 11(4), Article 0151167. https://doi.org/10.1371/journal.pone.0151167

Tsirogiannis, C., Staals, F. & Pellissier, V. (2018). Computing the expected value and variance of geometric measures. ACM Journal of Experimental Algorithmics, 23(1), 2.4:1-2.4:32. Article 2.4. https://doi.org/10.1145/3228331

Tsimbalistaia, U., Berger, C. P., Gellersen, H. & Manakhov, P. (2025). On-body Icons: Designing a 3D Interface for Launching Apps in Augmented Reality. In CHI 2025 - Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (pp. 1-15). Article 629 Association for Computing Machinery. https://doi.org/10.1145/3706598.3713954

Tsigaridas, E. (2011). Improved bounds for continued fractions variants for real root isolation. Paper presented at 4th Internatinal Conference on Mathematical Aspects of Computer Information, Beijing, China.

Tsakalidis, K. (2011). Dynamic Data Structures: Orthogonal Range Queries and Update Efficiency. Department of Computer Science, Aarhus University.

Truong, T. T., Airao, J., Hojati, F., Ilvig, C. F., Azarhoushang, B., Karras, P. & Aghababaei, R. (2024). Data-driven prediction of tool wear using Bayesian regularized artificial neural networks. Measurement: Journal of the International Measurement Confederation, 238, Article 115303. https://doi.org/10.1016/j.measurement.2024.115303

Truong, T. T., Airao, J., Fattahi, S., Azarhoushang, B., Karras, P. & Aghababaei, R. (2025). Image-based machine learning model for tool wear estimation in milling Inconel 718. Wear, 571, Article 205865. https://doi.org/10.1016/j.wear.2025.205865

Truică, C. O., Apostol, E. S., Darmont, J. & Assent, I. (2021). TextBenDS: a Generic Textual Data Benchmark for Distributed Systems. Information Systems Frontiers, 23(1), 81-100. https://doi.org/10.1007/s10796-020-09999-y

Truica, C.-O., Apostol, E. S., Stefu, T. & Karras, P. (2021). A Deep Learning Architecture for Audience Interest Prediction of News Topic on Social Media. In Y. Velegrakis, D. Zeinalipour-Yazti, P. K. Chrysanthis & F. Guerra (Eds.), Advances in Database Technology - EDBT 2021: 24th International Conference on Extending Database Technology, Proceedings (pp. 588-599). openproceedings.org. https://doi.org/10.5441/002/edbt.2021.69

Truelsen, J. (2015). Space Efficient Data Structures and External Terrain Algorithms. Department of Computer Science, Aarhus University.

Troullinou, G., Kondylakis, H., Lissandrini, M. & Mottin, D. (2021). SOFOS: Demonstrating the Challenges of Materialized View Selection on Knowledge Graph. In Proceedings of the 2021 International Conference on Management of Data (SIGMOD/PODS '21) (pp. 2789–2793). Association for Computing Machinery. https://doi.org/10.1145/3448016.3452765

Trittenbach, H., Böhm, K. & Assent, I. (2020). Active Learning of SVDD Hyperparameter Values. In G. Webb, Z. Zhang, V. S. Tseng, G. Williams, M. Vlachos & L. Cao (Eds.), Proceedings - 2020 IEEE 7th International Conference on Data Science and Advanced Analytics, DSAA 2020 (pp. 109-117). Article 9260103 IEEE. https://doi.org/10.1109/DSAA49011.2020.00023

Trigg, R., Bødker, S. & Grønbæk, K. (1990). A Video-based Analysis of the Cooperative Prototyping Process. In R. Hellman, M. Ruohonen & P. Sørgaard (Eds.), Proceedings of the 13th IRIS conference

Trifiletti, R., Nielsen, J. B., Frederiksen, T. K. & Jakobsen, T. P. (2016). On the Complexity of Additively Homomorphic UC Commitments. In E. Kushilevitz & T. Malkin (Eds.), Theory of Cryptography - 13th International Conference, TCC 2016-A, Proceedings (Vol. 9562, pp. 542-565). Springer VS. https://doi.org/10.1007/978-3-662-49096-9

Trifiletti, R. (2017). Amortizing Maliciously Secure Two-party Computation. Department of Computer Science, Aarhus University.

Trier, P., Noe, K. Ø., Sørensen, M. S. & Mosegaard, J. (2008). The visible ear surgery simulator. In Medicine Meets Virtual Reality 16 (pp. 523-5). IOS Press.

Triandopoulos, N., Papamanthou, C. & Tamassia, R. (2008). Authenticated hash tables: Proceedings of the 15th ACM Conference on Computer and communications security. In Proceedings of the 15th ACM Conference on Computer and communications security (pp. 437-448). Association for Computing Machinery. https://doi.org/10.1145/1455770.1455826

Triandopoulos, N., Goodrich, M. T. & Tamassia, R. (2008). Super-Efficient Verification of Dynamic Outsourced Databases: Topics in Cryptology - CT-RSA 2008. Lecture Notes in Computer Science, 407-424. https://doi.org/10.1007/978-3-540-79263-5

Triandopoulos, N., Goodrich, M. T., Papamanthou, C. & Tamassia, R. (2008). Athos: Efficient Authentication of Outsourced File Systems: Information Security. Lecture Notes in Computer Science, 80-96. https://doi.org/10.1007/978-3-540-85886-7_6

Triandopoulos, N., Cornelius, C., Kapadia, A., Kotz, D., Peebles, D. & Shin, M. (2008). Anonysense: privacy-aware people-centric sensing. In Proceeding of the 6th international conference on Mobile systems, applications, and services (pp. 211-224). Association for Computing Machinery. https://doi.org/10.1145/1378600.1378624

Triandopoulos, N., Kapadia, A., Cornelius, C., Peebles, D. & Kotz, D. (2008). AnonySense: Opportunistic and Privacy-Preserving Context Collection. Lecture Notes in Computer Science, 280-297. https://doi.org/10.1007/978-3-540-79576-6_17

Tran, H. V., Zhang, Z., Pham, T. D., Doan, N. P., Hoang, A.-T., Li, P., Vandierendonck, H., Assent, I. & Mai, T. S. (2025). InteDisUX: intepretation-guided discriminative user-centric explanation for time series. Proceedings of the AAAI Conference on Artificial Intelligence, 39(20), 20921-20928. https://doi.org/10.1609/aaai.v39i20.35387

Tran, H. V., Doan, N. P., Zhang, Z., Pham, T. D., Nguyen, P. H., Nguyen, X. H., Vandierendonck, H., Assent, I. & Mai, T. S. (2025). MIX: A Multi-view Time-Frequency Interactive Explanation Framework for Time Series Classification. Poster session presented at The Thirty-ninth Annual Conference on Neural Information Processing Systems, San Diego, California, United States. https://neurips.cc/virtual/2025/loc/san-diego/poster/117533

Tozawa, K., Morita, H. & Mizuki, T. (2025). Single-shuffle card-based protocol with eight cards per gate and its extensions. Natural Computing, 24(1), 131-147. https://doi.org/10.1007/s11047-024-10006-5

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