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migrate update-papers to Python
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‎index.html

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@@ -103,62 +103,61 @@ <h3 class="mb-0">Postdocs and PhD students</h3>
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<div class="resume-section-content">
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<h2 class="mb-5">Recent Publications</h2>
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<!-- PUBS -->
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<h3 class="mb-0">2023</h3><ul><li>Leonardo Pellegrina <emph><a href="https://doi.org/10.1145/3580305.3599325">Efficient Centrality Maximization with Rademacher Averages.</a></emph> KDD</li>
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<li>Andrea Tonon, Fabio Vandin <emph><a href="https://doi.org/10.1007/s10115-022-01800-7">caSPiTa: mining statistically significant paths in time series data from an unknown network.</a></emph> Knowl. Inf. Syst.</li>
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<h3 class="mb-0">2023</h3><ul><li>Andrea Tonon, Fabio Vandin <emph><a href="https://doi.org/10.1007/s10115-022-01800-7">caSPiTa: mining statistically significant paths in time series data from an unknown network.</a></emph> Knowl. Inf. Syst.</li>
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<li>Dario Simionato, Fabio Vandin <emph><a href="https://doi.org/10.24963/ijcai.2023/726">Bounding the Family-Wise Error Rate in Local Causal Discovery Using Rademacher Averages (Extended Abstract).</a></emph> IJCAI</li>
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<li>Enrico Dandolo, Andrea Pietracaprina, Geppino Pucci <emph><a href="https://doi.org/10.1007/978-3-031-39698-4_32">Distributed k-Means with Outliers in General Metrics.</a></emph> Euro-Par</li>
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<li>Leonardo Pellegrina <emph><a href="https://doi.org/10.1145/3580305.3599325">Efficient Centrality Maximization with Rademacher Averages.</a></emph> KDD</li>
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<li>Matteo Ceccarello, Andrea Pietracaprina, Geppino Pucci, Federico Soldà <emph><a href="https://doi.org/10.1186/s40537-023-00717-4">Scalable and space-efficient Robust Matroid Center algorithms.</a></emph> J. Big Data</li>
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<li>Matteo Ceccarello, Anton Dignös, Johann Gamper, Christina Khnaisser <emph><a href="https://doi.org/10.1145/3603719.3603732">Indexing Temporal Relations for Range-Duration Queries.</a></emph> SSDBM</li>
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<li>Paolo Pellizzoni, Andrea Pietracaprina, Geppino Pucci <emph><a href="https://doi.org/10.1007/978-3-031-38906-1_41">Fully Dynamic Clustering and Diversity Maximization in Doubling Metrics.</a></emph> WADS</li>
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<li>Paolo Pellizzoni, Fabio Vandin <emph><a href="https://doi.org/10.1109/ICDE55515.2023.00190">VC-dimension and Rademacher Averages of Subgraphs, with Applications to Graph Mining.</a></emph> ICDE</li>
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<li>Shiyuan Deng, Francesco Silvestri, Yufei Tao <emph><a href="https://doi.org/10.4230/LIPIcs.ICDT.2023.4">Enumerating Subgraphs of Constant Sizes in External Memory.</a></emph> ICDT</li></ul>
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<h3 class="mb-0">2022</h3><ul><li>Fabio Vandin <emph><a href="https://doi.org/10.1145/3535334">Technical perspective: Evaluating sampled metrics is challenging.</a></emph> Commun. ACM</li>
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<li>A. Guiotto, G. Bortolami, A. Ciniglio, F. Spolaor, G. Guarneri, A. Avogaro, F. Cibin, F. Silvestri, Z. Sawacha. <emph><a href="https://www.sciencedirect.com/science/article/abs/pii/S0966636222005537">Machine learning approach to diabetic foot risk classification with biomechanics data</a></emph> Gait & Posture</li>
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<li>Shiyuan Deng, Francesco Silvestri , Yufei Tao <emph><a href="https://doi.org/10.4230/LIPIcs.ICDT.2023.4">Enumerating Subgraphs of Constant Sizes in External Memory.</a></emph> ICDT</li></ul>
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<h3 class="mb-0">2022</h3><ul><li>A. Guiotto, G. Bortolami, A. Ciniglio, F. Spolaor, G. Guarneri, A. Avogaro, F. Cibin, F. Silvestri, Z. Sawacha. <emph><a href="https://www.sciencedirect.com/science/article/abs/pii/S0966636222005537">Machine learning approach to diabetic foot risk classification with biomechanics data</a></emph> Gait & Posture</li>
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<li>Adam Charane, Matteo Ceccarello, Anton Dignös, Johann Gamper <emph><a href="https://doi.org/10.1007/978-3-031-09850-5_17">Efficient Computation of All-Window Length Correlations.</a></emph> DB&amp;IS</li>
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<li>Andrea Tonon, Fabio Vandin <emph><a href="https://doi.org/10.1007/s10115-022-01689-2">gRosSo: mining statistically robust patterns from a sequence of datasets.</a></emph> Knowl. Inf. Syst.</li>
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<li>Dario Simionato, Fabio Vandin <emph><a href="https://doi.org/10.1007/978-3-031-26419-1_16">Bounding the Family-Wise Error Rate in Local Causal Discovery Using Rademacher Averages.</a></emph> ECML/PKDD</li>
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<li>Davide Buffelli, Fabio Vandin <emph><a href="https://doi.org/10.1109/IJCNN55064.2022.9892010">Graph Representation Learning for Multi-Task Settings: a Meta-Learning Approach.</a></emph> IJCNN</li>
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<li>Davide Buffelli, Fabio Vandin <emph><a href="https://doi.org/10.3390/data7010010">The Impact of Global Structural Information in Graph Neural Networks Applications.</a></emph> Data</li>
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<li>Davide Buffelli, Fabio Vandin <emph><a href="https://doi.org/10.1109/IJCNN55064.2022.9892010">Graph Representation Learning for Multi-Task Settings: a Meta-Learning Approach.</a></emph> IJCNN</li>
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<li>Davide Buffelli, Pietro Lió, Fabio Vandin <emph><a href="https://papers.nips.cc/paper_files/paper/2022/hash/ceeb3fa5be458f08fbb12a5bb783aac8-Abstract-Conference.html">SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks</a></emph> NeurIPS</li>
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<li>Davide Buffelli, Pietro Lió, Fabio Vandin <emph><a href="http://papers.nips.cc/paper_files/paper/2022/hash/ceeb3fa5be458f08fbb12a5bb783aac8-Abstract-Conference.html">SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks.</a></emph> NeurIPS</li>
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<li>Diego Santoro, Ilie Sarpe <emph><a href="https://doi.org/10.1145/3485447.3512204">ONBRA: Rigorous Estimation of the Temporal Betweenness Centrality in Temporal Networks.</a></emph> WWW</li>
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<li>Diego Santoro, Leonardo Pellegrina, Matteo Comin, Fabio Vandin <emph><a href="https://doi.org/10.1093/bioinformatics/btac180">SPRISS: approximating frequent k-mers by sampling reads, and applications.</a></emph> Bioinform.</li>
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<li>Fabio Vandin <emph><a href="https://doi.org/10.1145/3535334">Technical perspective: Evaluating sampled metrics is challenging.</a></emph> Commun. ACM</li>
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<li>Johann Gamper, Matteo Ceccarello, Anton Dignös <emph><a href="https://doi.org/10.1007/978-3-031-15740-0_5">What&apos;s New in Temporal Databases?</a></emph> ADBIS</li>
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<li>Leonardo Pellegrina, Cyrus Cousins, Fabio Vandin, Matteo Riondato <emph><a href="https://doi.org/10.1145/3532187">MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining.</a></emph> ACM Trans. Knowl. Discov. Data</li>
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<li>Leonardo Pellegrina, Fabio Vandin <emph><a href="https://pubmed.ncbi.nlm.nih.gov/36124798/">Discovering significant evolutionary trajectories in cancer phylogenies</a></emph> Bioinformatics</li>
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<li>Martin Aumüller, Matteo Ceccarello <emph><a href="https://doi.org/10.5441/002/edbt.2022.07">Implementing Distributed Similarity Joins using Locality Sensitive Hashing.</a></emph> EDBT</li>
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<li>Martin Aumüller, Sariel Har-Peled, Sepideh Mahabadi, Rasmus Pagh, Francesco Silvestri <emph><a href="https://doi.org/10.1145/3502867">Sampling a Near Neighbor in High Dimensions - Who is the Fairest of Them All?</a></emph> ACM Trans. Database Syst.</li>
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<li>Martin Aumüller, Sariel Har-Peled, Sepideh Mahabadi, Rasmus Pagh, Francesco Silvestri <emph><a href="https://doi.org/10.1145/3543667">Sampling near neighbors in search for fairness.</a></emph> Commun. ACM</li>
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<li>Martin Aumüller , Matteo Ceccarello <emph><a href="https://doi.org/10.5441/002/edbt.2022.07">Implementing Distributed Similarity Joins using Locality Sensitive Hashing.</a></emph> EDBT</li>
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<li>Martin Aumüller , Sariel Har-Peled, Sepideh Mahabadi, Rasmus Pagh, Francesco Silvestri <emph><a href="https://doi.org/10.1145/3543667">Sampling near neighbors in search for fairness.</a></emph> Commun. ACM</li>
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<li>Martin Aumüller , Sariel Har-Peled, Sepideh Mahabadi, Rasmus Pagh, Francesco Silvestri <emph><a href="https://doi.org/10.1145/3502867">Sampling a Near Neighbor in High Dimensions - Who is the Fairest of Them All?</a></emph> ACM Trans. Database Syst.</li>
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<li>Matteo Ceccarello, Johann Gamper <emph><a href="https://www.vldb.org/pvldb/vol15/p3841-ceccarello.pdf">Fast and Scalable Mining of Time Series Motifs with Probabilistic Guarantees.</a></emph> Proc. VLDB Endow.</li>
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<li>Paolo Pellizzoni, Andrea Pietracaprina, Geppino Pucci <emph><a href="https://doi.org/10.1007/s41060-022-00318-z">Adaptive k-center and diameter estimation in sliding windows.</a></emph> Int. J. Data Sci. Anal.</li>
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<li>Paolo Pellizzoni, Andrea Pietracaprina, Geppino Pucci <emph><a href="https://doi.org/10.3390/a15020052">k-Center Clustering with Outliers in Sliding Windows.</a></emph> Algorithms</li>
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<li>Paolo Sylos Labini, Massimo Bernaschi, Werner Nutt, Francesco Silvestri, Flavio Vella <emph><a href="https://doi.org/10.1109/IA356718.2022.00009">Blocking Sparse Matrices to Leverage Dense-Specific Multiplication.</a></emph> IA3@SC</li></ul>
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<li>Paolo Pellizzoni, Andrea Pietracaprina, Geppino Pucci <emph><a href="https://doi.org/10.1007/s41060-022-00318-z">Adaptive k-center and diameter estimation in sliding windows.</a></emph> Int. J. Data Sci. Anal.</li>
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<li>Paolo Sylos Labini, Massimo Bernaschi, Werner Nutt, Francesco Silvestri , Flavio Vella <emph><a href="https://doi.org/10.1109/IA356718.2022.00009">Blocking Sparse Matrices to Leverage Dense-Specific Multiplication.</a></emph> IA3@SC</li></ul>
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<h3 class="mb-0">2021</h3><ul><li>Andrea Tonon, Fabio Vandin <emph><a href="https://doi.org/10.1109/ICDM51629.2021.00075">CASPITA: Mining Statistically Significant Paths in Time Series Data from an Unknown Network.</a></emph> ICDM</li>
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<li>Davide Buffelli, Fabio Vandin <emph><a href="https://ieeexplore.ieee.org/document/9382331">Attention-Based Deep Learning Framework for Human Activity Recognition With User Adaptation</a></emph> IEEE Sensors</li>
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<li>Diego Santoro, Leonardo Pellegrina, Fabio Vandin <emph><a href="https://arxiv.org/abs/2101.07117">SPRISS: Approximating Frequent k-mers by Sampling Reads, and Applications</a></emph> RECOMB</li>
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<li>Elia Costa, Francesco Silvestri <emph><a href="https://doi.org/10.4230/OASIcs.ATMOS.2021.5">On the Bike Spreading Problem.</a></emph> ATMOS</li>
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<li>Elia Costa, Francesco Silvestri <emph><a href="https://doi.org/10.4230/OASIcs.ATMOS.2021.5">On the Bike Spreading Problem.</a></emph> ATMOS</li>
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<li>Federico Altieri, Andrea Pietracaprina, Geppino Pucci, Fabio Vandin <emph><a href="https://doi.org/10.1137/1.9781611976700.73">Scalable Distributed Approximation of Internal Measures for Clustering Evaluation.</a></emph> SDM</li>
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<li>Ilie Sarpe, Fabio Vandin <emph><a href="https://doi.org/10.1137/1.9781611976700.17">PRESTO: Simple and Scalable Sampling Techniques for the Rigorous Approximation of Temporal Motif Counts.</a></emph> SDM</li>
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<li>Ilie Sarpe, Fabio Vandin <emph><a href="https://doi.org/10.1145/3459637.3482459">odeN: Simultaneous Approximation of Multiple Motif Counts in Large Temporal Networks.</a></emph> CIKM</li>
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<li>Martin Aumüller, Matteo Ceccarello <emph><a href="https://doi.org/10.1016/j.is.2021.101807">The role of local dimensionality measures in benchmarking nearest neighbor search.</a></emph> Inf. Syst.</li>
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<li>Martin Aumüller, Sariel Har-Peled, Sepideh Mahabadi, Rasmus Pagh, Francesco Silvestri <emph><a href="https://doi.org/10.1145/3471485.3471496">Fair near neighbor search via sampling.</a></emph> SIGMOD Rec.</li>
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<li>Ilie Sarpe, Fabio Vandin <emph><a href="https://doi.org/10.1137/1.9781611976700.17">PRESTO: Simple and Scalable Sampling Techniques for the Rigorous Approximation of Temporal Motif Counts.</a></emph> SDM</li>
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<li>Martin Aumüller , Matteo Ceccarello <emph><a href="https://doi.org/10.1016/j.is.2021.101807">The role of local dimensionality measures in benchmarking nearest neighbor search.</a></emph> Inf. Syst.</li>
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<li>Martin Aumüller , Sariel Har-Peled, Sepideh Mahabadi, Rasmus Pagh, Francesco Silvestri <emph><a href="https://doi.org/10.1145/3471485.3471496">Fair near neighbor search via sampling.</a></emph> SIGMOD Rec.</li>
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<li>Matteo Comin, Barbara Di Camillo, Cinzia Pizzi, Fabio Vandin <emph><a href="https://doi.org/10.1093/bib/bbaa121">Comparison of microbiome samples: methods and computational challenges.</a></emph> Briefings Bioinform.</li>
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<li>Rezaul Chowdhury, Francesco Silvestri, Flavio Vella <emph><a href="https://doi.org/10.1007/978-3-030-85665-6_22">Algorithm Design for Tensor Units.</a></emph> Euro-Par</li></ul>
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<li>Rezaul Chowdhury, Francesco Silvestri , Flavio Vella <emph><a href="https://doi.org/10.1007/978-3-030-85665-6_22">Algorithm Design for Tensor Units.</a></emph> Euro-Par</li></ul>
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<h3 class="mb-0">2020</h3><ul><li>Andrea Tonon, Fabio Vandin <emph><a href="https://doi.org/10.1109/ICDM50108.2020.00064">GRosSo: Mining Statistically Robust Patterns from a Sequence of Datasets.</a></emph> ICDM</li>
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<li>Diego Santoro, Andrea Tonon, Fabio Vandin <emph><a href="https://doi.org/10.3390/a13050123">Mining Sequential Patterns with VC-Dimension and Rademacher Complexity.</a></emph> Algorithms</li>
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<li>Leonardo Pellegrina, Cinzia Pizzi, Fabio Vandin <emph><a href="https://doi.org/10.1089/cmb.2019.0314">Fast Approximation of Frequent k-Mers and Applications to Metagenomics.</a></emph> J. Comput. Biol.</li>
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<li>Leonardo Pellegrina, Cyrus Cousins, Fabio Vandin, Matteo Riondato <emph><a href="https://doi.org/10.1145/3394486.3403267">MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining.</a></emph> KDD</li>
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<li>Leonardo Pellegrina, Fabio Vandin <emph><a href="https://doi.org/10.1007/s10618-020-00687-8">Efficient mining of the most significant patterns with permutation testing.</a></emph> Data Min. Knowl. Discov.</li>
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<li>Martin Aumüller, Matteo Ceccarello <emph><a href="https://doi.org/10.1007/978-3-030-60936-8_31">Running Experiments with Confidence and Sanity.</a></emph> SISAP</li>
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<li>Martin Aumüller, Rasmus Pagh, Francesco Silvestri <emph><a href="https://doi.org/10.1145/3375395.3387648">Fair Near Neighbor Search: Independent Range Sampling in High Dimensions.</a></emph> PODS</li>
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<li>Martin Aumüller , Matteo Ceccarello <emph><a href="https://doi.org/10.1007/978-3-030-60936-8_31">Running Experiments with Confidence and Sanity.</a></emph> SISAP</li>
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<li>Martin Aumüller , Rasmus Pagh, Francesco Silvestri <emph><a href="https://doi.org/10.1145/3375395.3387648">Fair Near Neighbor Search: Independent Range Sampling in High Dimensions.</a></emph> PODS</li>
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<li>Matteo Ceccarello, Andrea Pietracaprina, Geppino Pucci <emph><a href="https://doi.org/10.1145/3402448">A General Coreset-Based Approach to Diversity Maximization under Matroid Constraints.</a></emph> ACM Trans. Knowl. Discov. Data</li>
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<li>Matteo Ceccarello, Andrea Pietracaprina, Geppino Pucci, Eli Upfal <emph><a href="https://doi.org/10.3390/a13090216">Distributed Graph Diameter Approximation.</a></emph> Algorithms</li>
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<li>Matteo Riondato, Fabio Vandin <emph><a href="https://doi.org/10.1145/3385653">MiSoSouP: Mining Interesting Subgroups with Sampling and Pseudodimension.</a></emph> ACM Trans. Knowl. Discov. Data</li>
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<li>Paolo Pellizzoni, Andrea Pietracaprina, Geppino Pucci <emph><a href="https://doi.org/10.1109/DSAA49011.2020.00032">Dimensionality-adaptive k-center in sliding windows.</a></emph> DSAA</li>
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<li>Rezaul Chowdhury, Francesco Silvestri, Flavio Vella <emph><a href="https://doi.org/10.1145/3350755.3400252">A Computational Model for Tensor Core Units.</a></emph> SPAA</li>
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<li>Thomas D. Ahle, Francesco Silvestri <emph><a href="https://doi.org/10.1007/978-3-030-60936-8_6">Similarity Search with Tensor Core Units.</a></emph> SISAP</li>
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<li>Rezaul Chowdhury, Francesco Silvestri , Flavio Vella <emph><a href="https://doi.org/10.1145/3350755.3400252">A Computational Model for Tensor Core Units.</a></emph> SPAA</li>
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<li>Thomas D. Ahle, Francesco Silvestri <emph><a href="https://doi.org/10.1007/978-3-030-60936-8_6">Similarity Search with Tensor Core Units.</a></emph> SISAP</li>
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<li>Yoo-Ah Kim, D. Wojtowicz, R. Sarto Basso, I. Sason, W. Robinson, D. S. Hochbaum, M. Leiserson, R. Sharan, F. Vandin, T. Przytycka. <emph><a href="https://pubmed.ncbi.nlm.nih.gov/32471470/">Network-based approaches elucidate differences within APOBEC and clock-like signatures in breast cancer</a></emph> Genome Medicine</li></ul>
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<!-- PUBS -->
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‎manual_pubs.json

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{
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"authors": [
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"Leonardo Pellegrina",
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"Fabio Vandin"
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],
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"key": "PellegrinaV2022",
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"title": "Discovering significant evolutionary trajectories in cancer phylogenies",
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"url": "https://pubmed.ncbi.nlm.nih.gov/36124798/",
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"venue": "Bioinformatics",
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"year": "2022"
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{
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"authors": [
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"Pietro Li\u00f3",
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"key": "BuffelliLV2022",
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"title": "SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks",
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"url": "https://papers.nips.cc/paper_files/paper/2022/hash/ceeb3fa5be458f08fbb12a5bb783aac8-Abstract-Conference.html",
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"venue": "NeurIPS",
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},
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{
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"authors": [
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"A. Guiotto",
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"G. Bortolami",
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"G. Guarneri",
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"A. Avogaro",
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"F. Cibin",
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"F. Silvestri",
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"Z. Sawacha."
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"key": "GuiottoEtAl2022",
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"title": "Machine learning approach to diabetic foot risk classification with biomechanics data",
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"url": "https://www.sciencedirect.com/science/article/abs/pii/S0966636222005537",
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"venue": "Gait & Posture",
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"year": "2022"
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},
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{
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"authors": [
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"Diego Santoro",
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"Leonardo Pellegrina",
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"title": "SPRISS: Approximating Frequent k-mers by Sampling Reads, and Applications",
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"url": "https://arxiv.org/abs/2101.07117",
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"venue": "RECOMB",
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{
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"authors": [
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"Davide Buffelli",
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"Fabio Vandin"
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"title": "Attention-Based Deep Learning Framework for Human Activity Recognition With User Adaptation",
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"url": "https://ieeexplore.ieee.org/document/9382331",
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"venue": "IEEE Sensors",
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"year": "2021"
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},
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{
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"authors": [
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"Yoo-Ah Kim",
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"D. Wojtowicz",
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"R. Sarto Basso",
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"I. Sason",
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"W. Robinson",
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"D. S. Hochbaum",
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"M. Leiserson",
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"R. Sharan",
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"F. Vandin",
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"T. Przytycka."
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"key": "KimetAl2020",
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"title": "Network-based approaches elucidate differences within APOBEC and clock-like signatures in breast cancer",
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"url": "https://pubmed.ncbi.nlm.nih.gov/32471470/",
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"venue": "Genome Medicine",
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"year": "2020"
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{
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"authors": [
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"R. Sarto Basso",
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"A. S. Liu",
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"F. Vandin",
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"T. Przytycka"
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],
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"key": "KimetAl2020",
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"title": "Identifying Drug Sensitivity Subnetworks with NETPHIX",
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"url": "https://pubmed.ncbi.nlm.nih.gov/33089107/",
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"venue": "iScience",
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"year": "2020"
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