Combinatorial Scientific Computing

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.

Multiscale Methods

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 Computing

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.

AI, 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.

Network Science

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.

Recent News

New paper submitted


2020

Accepted paper in the International Workshop on Big Data Reduction (IEEE BIGDATA)

  • Farah Alshanik, Amy Apon, Alexander Herzog, Ilya Safro, Justin Sybrandt "Accelerating Text Mining Using Domain-Specific Stop Word Lists", preprint at https://arxiv.org/pdf/2012.02294.pdf, 2020

    Preprint 2020

    • Accepted paper in the IEEE International Conference on Big Data (BIGDATA)

  • Ehsan Sadrfaridpour, Korey Palmer, Ilya Safro "AML-SVM: Adaptive Multilevel Learning with Support Vector Machines", preprint at https://arxiv.org/pdf/2011.02592.pdf, 2020

    Preprint 2020

    • Drs. Alexeev, Safro and Shaydulin co-organized the tutorial at IEEE International Conference on Quantum Computing and Engineering (QCE20) on solving combinatorial optimization problems on quantum computers. Watch recorded sessions at

    2020

    Accepted paper in ACM Transactions on Quantum Computing

  • Hayato Ushijima-Mwesigwa, Ruslan Shaydulin, Susan Mniszewski, Christian Negre, Yuri Alexeev, Ilya Safro "Multilevel Combinatorial Optimization Across Quantum Architectures"
    Preprint

    2020

    • NSF awarded grant for University of Chicago-Clemson collaborative project to develop large-scale QAOA simulator.

    2020

    Accepted paper in the 29TH ACM International Conference on Information and Knowledge Management (CIKM)

    • Sybrandt, Tyagin, Shtutman, Safro "AGATHA: Automatic Graph-mining and Transformer based Hypothesis Generation Approach", preprint at http://arxiv.org/pdf/2002.05635.pdf
      Preprint 2020

    Accepted papers in the ACM KDD 2020 Workshop on Mining and Learning with Graphs

    • Ding, Zhang, Sybrandt, Safro "Unsupervised Hierarchical Graph Representation Learning by Mutual Information Maximization", preprint at https://arxiv.org/pdf/2003.08420.pdf
      Preprint 2020
    • Sybrandt, Safro "FOBE and HOBE: First- and High-Order Bipartite Embeddings", preprint at https://arxiv.org/abs/1905.10953
      Preprint 2020

    Congratulations to Dr. Ehsan Sadrfaridpour for successfully defending his Ph.D. thesis "Fast Machine Learning Algorithms for Massive Datasets with Applications in Biomedical Domain"! Ehsan will join Lowe's data science team this summer.

    2020

    ACS Lab in News

    Our quantum computing work in news
    More ...

    2020

    Congratulations to Dr. Ruslan Shaydulin for successfully defending his Ph.D. thesis "Quantum and Classical Multilevel Algorithms for (Hyper)Graphs"! Ruslan will join Argonne National Lab with MGM fellowship in August.

    2020

    PhD student Ankit Kulshrestha joins our team! Ankit will be working on machine learning algorithms

    2020

    ACS Lab in News

    Artificial intelligence could aid in fight against COVID-19
    More ...

    2020

    NSF awarded grant for a project led by Dr. Safro to tackle COVID-19 using our AI literature based discovery system.

    2020

    ACS Lab in News

    Combing the best of quantum computing and classical computing
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    2020

    Congratulations to Zirou Qiu for successfully defending his M.Sc. thesis "ELRUNA: Elimination Rule-based Network Alignment"!

    2020

    ACS Lab in News

    Justin Sybrandt gave invited talk about our work in AI for literature based discovery at the A.I. Socratic Circles (#AISC) community
    More ...

    2020

    Congratulations to Dr. Justin Sybrandt for successfully defending his Ph.D. thesis "Exploiting Latent Features of Text and Graphs"! Justin will join Google Brain team this summer.

    2020

    New papers submitted

    • Manuel Penschuck, Ulrik Brandes, Michael Hamann, Sebastian Lamm, Ulrich Meyer, Ilya Safro, Peter Sanders, Christian Schulz "Recent Advances in Scalable Network Generation", 2020, https://arxiv.org/abs/2003.00736
    • Zirou Qiu, Ruslan Shaydulin, Xiaoyuan Liu, Yuri Alexeev, Christopher S. Henry, Ilya Safro "ELRUNA: Elimination Rule-based Network Alignment", submitted, preprint at https://arxiv.org/abs/1911.05486, 2020


    2020

    DARPA awarded $1.03M grant for a project led by Dr. Safro to develop hybrid quantum-classical algorithms in collaboration with Argonne National Laboratory.

    2020

    Dr. Safro and Ruslan Shaydulin co-organized

    • Tutorial at SIAM PP 2020 on solving combinatorial optimization problems on quantum computers
    • Mini-symposium at SIAM PP 2020 on recent advances and trends in hybrid quantum-classical algorithms

    2020

    New papers submitted

    • Justin Sybrandt, Ilya Safro "CBAG: Conditional Biomedical Abstract Generation", submitted, preprint at http://arxiv.org/pdf/2002.05637.pdf, 2020
    • Justin Sybrandt, Ilya Tyagin, Michael Shtutman, Ilya Safro "AGATHA: Automatic Graph-mining and Transformer based Hypothesis Generation Approach", submitted, preprint at http://arxiv.org/pdf/2002.05635.pdf, 2020


    2020

    Dr. Safro will participate in the program committees of

    • International Workshop on Quantum Computing: Circuits Systems Automation and Applications 2020 (QC-CSAA, co-located with IEEE Computer Society Annual Symposiumon VLSI)
    • INFORMS Optimization Society 2020
    • International Workshop on Literature-Based Discovery (LBD 2020, co-located with PKDD/ECML)

    2019

    Congratulations to Justin Sybrandt for accepting a position at Google Brain starting Summer 2020! Justin is a Ph.D. candidate working on machine learning and natural language processing.

    Link
    2019

    Congratulations to Ruslan Shaydulin for accepting a highly competitive named postdoctoral position at Argonne National Laboratory starting Fall 2020! Ruslan is a Ph.D.candidate working on quantum computing and optimization.

    Link
    2019

    New paper submitted
    Xiaoyuan Liuy, Hayato Ushijima-Mwesigwa, Avradip Mandal, Sarvagya Upadhyay, Ilya Safro, Arnab Roy "On Modeling Local Search with Special-Purpose Combinatorial Optimization Hardware", submitted, 2019, preprint at https://people.cs.clemson.edu/~isafro/papers/modeling-local-search.pdf

    Link
    2019

    Accepted paper in the International Workshop on Big Data Tools, Methods, and Use Cases for Innovative Scientific Discovery (BTSD) 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

    Preprint 2019

    Accepted paper in Journal of Industrial and Management Optimization Hayato Ushijima-Mwesigwa, MD Zadid Khan, Mashrur Chowdhury and Ilya Safro "Optimal Placement of Wireless Charging Lanes in Road Networks", preprint at https://arxiv.org/abs/1704.01022

    Preprint 2019

    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
    Hayato Ushijima-Mwesigwa, Ruslan Shaydulin, Susan Mniszewski, Christian Negre, Yuri Alexeev, Ilya Safro "Multilevel Combinatorial Optimization Across Quantum Architectures", submitted, 2019, preprint at https://arxiv.org/abs/1910.09985

    Link
    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

    Link
    Zirou Qiu, Ruslan Shaydulin, Xiaoyuan Liu, Yuri Alexeev, Christopher S. Henry, Ilya Safro "Network Alignment by Propagating Reliable Similarities", preprint at arXiv

    Link
    Justin Sybrandt, Ruslan Shaydulin, Ilya Safro "Hypergraph Partitioning with Embeddings", preprint at https://arxiv.org/abs/1909.04016

    Link
    Justin Sybrandt, Ilya Safro "FOBE and HOBE: First- and High-Order Bipartite Embeddings", preprint at https://arxiv.org/abs/1905.10953

    Link

    2019

    Ruslan Shaydulin received travel awards from SIAM PP2020, Supercomputing 2019, and IGSCC 2020
    Joey Liu received travel award from FOCS 2019

    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

    2019

    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

    Link IEEE Computer, vol. 52(6), pp. 18-26, 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

    Preprint

    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

    Accepted paper in Journal of Sound and Vibration
    William Locke, Justin Sybrandt, Ilya Safro, Sez Atamturktur "Using Drive-by Health Monitoring to Detect Bridge Damage Considering Environmental and Operational Effects", 2019, preprint at https://engrxiv.org/ntfdp/

    Link Journal of Sound and Vibration, 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!

    2018

    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

    2018
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