Discrete optimization problems on large-scale graphs that are used to accelerate the performance of scientific computing algorithms. Examples include (hyper)graph partitioning, reordering, and coloring to improve load-balancing, task mapping, and data locality on HPC.
A broad range of scientific problems involve multiple scales. Traditional monoscale approaches have proven to be inadequate, even with the largest supercomputers, because of the prohibitively large number of variables involved. We develop multiscale approaches in which a hierarchy of coarse scale approximations is used to solve large-scale problems efficiently.
Quantum computers are expected to accelerate scientific discovery spanning many different areas such as medicine, AI, material science, and financial predictions. Quantum hardware manipulates with much more complex than binary information that is represented in classical computers. We are interested in quantum algorithms and methods of their hybridization with classical computing systems.
Machine Learning and Data Mining
Many standard machine learning and data mining algorithms are prohibitive for large-scale number of variables. For example, this can happen because of the slow convergence or NP-hardness of underlying optimization problems (such as in support vector machines and cut-based clustering). We are interested in algorithms that cope with such problems.
Literature Based Discovery and Text Mining
Hypothesis generation is becoming a crucial time-saving family of techniques which allow researchers to quickly discover implicit connections between important concepts. We are interested in such techniques and complex text mining problems, in general. Applications include biomedical discovery with scientific texts, healthcare and social media.
Computational, modeling, theory and data problems related to complex networks in social/natural/information sciences, and engineering. The analysis often includes frequent pattern discovery, outliers detection, quantitative methods for importance ranking of network elements, time-dependent data analysis, evolution modeling, visualization, and community detection.
Congratulations to our PhD student Justin Sybrandt for being selected in top 12 among more than 3000 summer interns based on his achievements. Over the summer, Justin was an intern at Facebook working on Instagram.
New papers submitted
Chris Gropp, Alexander Herzog, Ilya Safro, Paul Wilson, Amy Apon "Clustered Latent Dirichlet Allocation for Scientific Discovery",preprint at https://arxiv.org/pdf/1610.07703.pdf
Zirou Qiu, Ruslan Shaydulin, Xiaoyuan Liu, Yuri Alexeev, Christopher S. Henry, Ilya Safro "Network Alignment by Propagating Reliable Similarities", preprint at arXiv
Justin Sybrandt, Ruslan Shaydulin, Ilya Safro "Hypergraph Partitioning with Embeddings", preprint at https://arxiv.org/abs/1909.04016
Justin Sybrandt, Ilya Safro "FOBE and HOBE: First- and High-Order Bipartite Embeddings", preprint at https://arxiv.org/abs/1905.10953
Ruslan Shaydulin received travel awards from SIAM PP2020, Supercomputing 2019, and IGSCC 2020
Joey Liu received travel award from FOCS 2019
Accepted paper at IEEE High Performance Extreme Computing Conference (HPEC) 2019 with best student paper award!
Ruslan Shaydulin, Ilya Safro, Jeffrey Larson "Multistart Methods for Quantum Approximate Optimization", preprint at https://arxiv.org/abs/1905.08768
Accepted paper at IEEE Computer
Ruslan Shaydulin, Hayato Ushijima-Mwesigwa, Christian F.A. Negre, Ilya Safro, Susan M. Mniszewski, Yuri Alexeev "Hybrid Approach for Solving Optimization Problems on Small Quantum Computers", 2019
Accepted paper at Journal of Neuroimmune Pharmacology
Marina Aksenova, Justin Sybrandt, Biyun Cui, Vitali Sikirzhytski, Hao Ji, Diana Odhiambo, Mathew Lucius, Jill R. Turner, Eugenia Broude, Edsel Pea, Sofia Lizzaraga, Jun Zhu, Ilya Safro, Michael D Wyatt, Michael Shtutman "Inhibition of the DDX3 prevents HIV-1 Tat and cocaine-induced neurotoxicity by targeting microglia activation", 2019
Accepted paper in Machine Learning
Ehsan Sadrfaridpour, Talayeh Razzaghi, Ilya Safro "Engineering fast multilevel support vector machines", 2019, preprint at arXiv:1707.07657
Link Machine Learning, https://doi.org/10.1007/s10994-019-05800-7, Springer, 2019
Congratulations to Varsha Chauhan for successfully defending her MSc thesis "Planar Graph Generation With Application To Water Distribution Networks".
11 January, 2019
Congratulations to Dr. Hayato Ushijima-Mwesigwa for successfully defending his Ph.D. thesis "Models for Networks with Consumable Resources"!
16 November, 2018
Accepted paper at SIAM Multiscale Modeling and Simulations Ruslan Shaydulin, Jie Chen, Ilya Safro "Relaxation-Based Coarsening for Multilevel Hypergraph Partitioning", 2019, preprint at arXiv:1710.06552
Link Multiscale Model. Simul., 17(1), pp. 482–506, 2019
Accepted paper at 3rd International Workshop on Post Moore's Era Supercomputing (PMES 2018)
Ruslan Shaydulin, Haayto Ushijima-Mwesigwa, Ilya Safro, Susan Mniszewski, Yuri Alexeev "Community Detection Across Emerging Quantum Architectures", preprint at arXiv:1810.07765, 2018
Link Proceedings of Post Moore's Era Supercomputing (PMES 2018)
Congratulations to Justin Sybrandt and Ruslan Shaydulin for receiving travel awards to present their papers at #IEEEBigData2018 and #APS2018!
Three papers are accepted at IEEE Big Data 2018
Saroj K. Dash, I. Safro, Ravisutha S. Srinivasamurthy "Spatio-temporal prediction of crimes using network analytic approach", preprint at arXiv:1808.06241, 2018
Justin Sybrandt, Angelo Carrabba, Alexander Herzog, Ilya Safro "Are Abstracts Enough for Hypothesis Generation?", preprint at arXiv:1804.05942, 2018
Justin Sybrandt, Michael Shtutman, Ilya Safro "Large-Scale Validation of Hypothesis Generation Systems via Candidate Ranking", preprint at arXiv:1802.03793, 2018