Work with thought leaders and academic experts in Computational Mathematics
Companies can greatly benefit from working with experts in Computational Mathematics. These researchers have a deep understanding of data analysis, optimization, and machine learning techniques. By collaborating with them, companies can enhance their decision-making processes, improve efficiency, and gain a competitive edge. Computational Mathematics experts can help companies solve complex problems, develop innovative algorithms, and optimize various processes. They can also assist in developing predictive models, improving risk management strategies, and identifying patterns and trends in large datasets. Overall, partnering with a Computational Mathematics researcher can lead to improved data-driven decision-making, increased productivity, and better business outcomes.
Researchers on NotedSource with backgrounds in Computational Mathematics include Nicolangelo Iannella, Siddharth Maddali, Dr. Fantai Kong, Ph.D., Ping Luo, Jeffrey Townsend, Emmanouil Mentzakis, Hector Klie, Tim Osswald, Dmitry Batenkov, Ph.D., Edoardo Airoldi, and Ariel Aptekmann.
Nicolangelo Iannella
Senior Research fellow, The University of Oslo, Faculty of Mathematics and Natural Sciences
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Other Research Expertise (18)
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47 total publications
Optimization in the Design of Natural Structures, Biomaterials, Bioinformatics and Biometric Techniques for Solving Physiological Needs and Ultimate Performance of Bio-devices
Current Bioinformatics / Jun 28, 2019
Wong, K. K. L. (2019). Optimization in the Design of Natural Structures, Biomaterials, Bioinformatics and Biometric Techniques for Solving Physiological Needs and Ultimate Performance of Bio-devices. Current Bioinformatics, 14(5), 374–375. https://doi.org/10.2174/157489361405190628122355
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Siddharth Maddali
Computational physicist with a specialization in X-ray and optical imaging and microscopy for condensed matter and materials systems.
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Other Research Expertise (21)
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29 total publications
9Cr steel visualization and predictive modeling
Computational Materials Science / Oct 01, 2019
Krishnamurthy, N., Maddali, S., Hawk, J. A., & Romanov, V. N. (2019). 9Cr steel visualization and predictive modeling. Computational Materials Science, 168, 268–279. https://doi.org/10.1016/j.commatsci.2019.03.015
Topology-faithful nonparametric estimation and tracking of bulk interface networks
Computational Materials Science / Dec 01, 2016
Maddali, S., Ta’asan, S., & Suter, R. M. (2016). Topology-faithful nonparametric estimation and tracking of bulk interface networks. Computational Materials Science, 125, 328–340. https://doi.org/10.1016/j.commatsci.2016.08.021
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Dr. Fantai Kong, Ph.D.
Hunt Energy
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Other Research Expertise (19)
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31 total publications
CT-MEAM interatomic potential of the Li-Ni-O ternary system for Li-ion battery cathode materials
Computational Materials Science / Feb 01, 2017
Kong, F., Longo, R. C., Liang, C., Yeon, D.-H., Zheng, Y., Park, J.-H., Doo, S.-G., & Cho, K. (2017). CT-MEAM interatomic potential of the Li-Ni-O ternary system for Li-ion battery cathode materials. Computational Materials Science, 127, 128–135. https://doi.org/10.1016/j.commatsci.2016.10.030
Charge-transfer modified embedded-atom method for manganese oxides: Nanostructuring effects on MnO2 nanorods
Computational Materials Science / Aug 01, 2016
Kong, F., Longo, R. C., Zhang, H., Liang, C., Zheng, Y., & Cho, K. (2016). Charge-transfer modified embedded-atom method for manganese oxides: Nanostructuring effects on MnO2 nanorods. Computational Materials Science, 121, 191–203. https://doi.org/10.1016/j.commatsci.2016.04.029
A large-scale simulation method on complex ternary Li–Mn–O compounds for Li-ion battery cathode materials
Computational Materials Science / Feb 01, 2016
Kong, F., Zhang, H., Longo, R. C., Lee, B., Yeon, D.-H., Yoon, J., Park, J.-H., Doo, S.-G., & Cho, K. (2016). A large-scale simulation method on complex ternary Li–Mn–O compounds for Li-ion battery cathode materials. Computational Materials Science, 112, 193–204. https://doi.org/10.1016/j.commatsci.2015.10.027
Influence of interstitial beryllium on properties of ZnO: A first-principle research
Computational Materials Science / Aug 01, 2012
Kong, F. T., & Gong, H. R. (2012). Influence of interstitial beryllium on properties of ZnO: A first-principle research. Computational Materials Science, 61, 127–133. https://doi.org/10.1016/j.commatsci.2012.04.008
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Ping Luo
Bioinformatics Specialist at Princess Margaret Cancer Centre with experience in deep learning
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Other Research Expertise (21)
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Most Relevant Publications (1+)
23 total publications
Enhancing the prediction of disease–gene associations with multimodal deep learning
Bioinformatics / Mar 02, 2019
Luo, P., Li, Y., Tian, L.-P., & Wu, F.-X. (2019). Enhancing the prediction of disease–gene associations with multimodal deep learning. Bioinformatics, 35(19), 3735–3742. https://doi.org/10.1093/bioinformatics/btz155
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Jeffrey Townsend
Professor of Biostatistics and Ecology & Evolutionary Biology
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Other Research Expertise (52)
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Most Relevant Publications (4+)
207 total publications
PathScore: a web tool for identifying altered pathways in cancer data
Bioinformatics / Aug 08, 2016
Gaffney, S. G., & Townsend, J. P. (2016). PathScore: a web tool for identifying altered pathways in cancer data. Bioinformatics, 32(23), 3688–3690. https://doi.org/10.1093/bioinformatics/btw512
H-CLAP: hierarchical clustering within a linear array with an application in genetics
Statistical Applications in Genetics and Molecular Biology / Jan 01, 2015
Ghosh, S., & Townsend, J. P. (2015). H-CLAP: hierarchical clustering within a linear array with an application in genetics. Statistical Applications in Genetics and Molecular Biology, 14(2). https://doi.org/10.1515/sagmb-2013-0076
AuthorReward: increasing community curation in biological knowledge wikis through automated authorship quantification
Bioinformatics / Jun 03, 2013
Dai, L., Tian, M., Wu, J., Xiao, J., Wang, X., Townsend, J. P., & Zhang, Z. (2013). AuthorReward: increasing community curation in biological knowledge wikis through automated authorship quantification. Bioinformatics, 29(14), 1837–1839. https://doi.org/10.1093/bioinformatics/btt284
LOX: inferring Level Of eXpression from diverse methods of census sequencing
Bioinformatics / Jun 10, 2010
Zhang, Z., López-Giráldez, F., & Townsend, J. P. (2010). LOX: inferring Level Of eXpression from diverse methods of census sequencing. Bioinformatics, 26(15), 1918–1919. https://doi.org/10.1093/bioinformatics/btq303
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Emmanouil Mentzakis
Health Economist, Professor at City University of London
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Other Research Expertise (21)
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46 total publications
Characterizing dynamic communication in online eating disorder communities: a multiplex network approach
Applied Network Science / Apr 17, 2019
Wang, T., Brede, M., Ianni, A., & Mentzakis, E. (2019). Characterizing dynamic communication in online eating disorder communities: a multiplex network approach. Applied Network Science, 4(1). https://doi.org/10.1007/s41109-019-0125-4
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Hector Klie
CEO @ DeepCast.ai | AI-driven Industrial Solutions, Technical Innovation
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Other Research Expertise (23)
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Most Relevant Publications (3+)
81 total publications
On optimization algorithms for the reservoir oil well placement problem
Computational Geosciences / Aug 17, 2006
Bangerth, W., Klie, H., Wheeler, M. F., Stoffa, P. L., & Sen, M. K. (2006). On optimization algorithms for the reservoir oil well placement problem. Computational Geosciences, 10(3), 303–319. https://doi.org/10.1007/s10596-006-9025-7
Reduced-order modeling for thermal recovery processes
Computational Geosciences / Sep 01, 2013
Rousset, M. A. H., Huang, C. K., Klie, H., & Durlofsky, L. J. (2013). Reduced-order modeling for thermal recovery processes. Computational Geosciences, 18(3–4), 401–415. https://doi.org/10.1007/s10596-013-9369-8
A family of physics-based preconditioners for solving elliptic equations on highly heterogeneous media
Applied Numerical Mathematics / Jun 01, 2009
Aksoylu, B., & Klie, H. (2009). A family of physics-based preconditioners for solving elliptic equations on highly heterogeneous media. Applied Numerical Mathematics, 59(6), 1159–1186. https://doi.org/10.1016/j.apnum.2008.06.002
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Tim Osswald
Polymers Professor - University of Wisconsin
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117 total publications
Analysis of fiber damage mechanisms during processing of reinforced polymer melts
Engineering Analysis with Boundary Elements / Jul 01, 2002
Hernandez, J. P., Raush, T., Rios, A., Strauss, S., & Osswald, T. A. (2002). Analysis of fiber damage mechanisms during processing of reinforced polymer melts. Engineering Analysis with Boundary Elements, 26(7), 621–628. https://doi.org/10.1016/s0955-7997(02)00018-8
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Dmitry Batenkov, Ph.D.
A highly experienced applied mathematician working in academia (faculty) and industry (consulting), with 15+ years of research and teaching expertise in inverse problems, signal processing, and data science.
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Most Relevant Publications (5+)
50 total publications
Algebraic Fourier reconstruction of piecewise smooth functions
Mathematics of Computation / Jan 01, 2012
Batenkov, D., & Yomdin, Y. (2012). Algebraic Fourier reconstruction of piecewise smooth functions. Mathematics of Computation, 81(277), 277–318. https://doi.org/10.1090/s0025-5718-2011-02539-1
Complete algebraic reconstruction of piecewise-smooth functions from Fourier data
Mathematics of Computation / Feb 19, 2015
Batenkov, D. (2015). Complete algebraic reconstruction of piecewise-smooth functions from Fourier data. Mathematics of Computation, 84(295), 2329–2350. https://doi.org/10.1090/s0025-5718-2015-02948-2
Accuracy of Algebraic Fourier Reconstruction for Shifts of Several Signals
Sampling Theory in Signal and Image Processing / May 01, 2014
Batenkov, D., Sarig, N., & Yomdin, Y. (2014). Accuracy of Algebraic Fourier Reconstruction for Shifts of Several Signals. Sampling Theory in Signal and Image Processing, 13(2), 151–173. https://doi.org/10.1007/bf03549577
On inverses of Vandermonde and confluent Vandermonde matrices
Numerische Mathematik / Dec 01, 1962
Gautschi, W. (1962). On inverses of Vandermonde and confluent Vandermonde matrices. Numerische Mathematik, 4(1), 117–123. https://doi.org/10.1007/bf01386302
Sampling, Metric Entropy, and Dimensionality Reduction
SIAM Journal on Mathematical Analysis / Jan 01, 2015
Batenkov, D., Friedland, O., & Yomdin, Y. (2015). Sampling, Metric Entropy, and Dimensionality Reduction. SIAM Journal on Mathematical Analysis, 47(1), 786–796. https://doi.org/10.1137/130944436
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Edoardo Airoldi
Professor of Statistics & Data Science Temple University & PI, Harvard University
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106 total publications
Quantitative visualization of alternative exon expression from RNA-seq data
Bioinformatics / Jan 22, 2015
Katz, Y., Wang, E. T., Silterra, J., Schwartz, S., Wong, B., Thorvaldsdóttir, H., Robinson, J. T., Mesirov, J. P., Airoldi, E. M., & Burge, C. B. (2015). Quantitative visualization of alternative exon expression from RNA-seq data. Bioinformatics, 31(14), 2400–2402. https://doi.org/10.1093/bioinformatics/btv034
A Network Analysis Model for Disambiguation of Names in Lists
Computational and Mathematical Organization Theory / Jul 01, 2005
Malin, B., Airoldi, E., & Carley, K. M. (2005). A Network Analysis Model for Disambiguation of Names in Lists. Computational and Mathematical Organization Theory, 11(2), 119–139. https://doi.org/10.1007/s10588-005-3940-3
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Ariel Aptekmann
Bioinformatician at Hackensack Meridian Hospital Center for Discovery and Innovation
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23 total publications
mebipred: identifying metal-binding potential in protein sequence
Bioinformatics / May 27, 2022
Aptekmann, A. A., Buongiorno, J., Giovannelli, D., Glamoclija, M., Ferreiro, D. U., & Bromberg, Y. (2022). mebipred: identifying metal-binding potential in protein sequence. Bioinformatics, 38(14), 3532–3540. https://doi.org/10.1093/bioinformatics/btac358
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Example Computational Mathematics projects
How can companies collaborate more effectively with researchers, experts, and thought leaders to make progress on Computational Mathematics?
Optimizing Supply Chain Management
A Computational Mathematics expert can develop algorithms to optimize supply chain management, reducing costs and improving efficiency. By analyzing data on inventory levels, transportation routes, and demand patterns, they can identify bottlenecks and suggest strategies to streamline operations.
Predictive Maintenance in Manufacturing
By analyzing sensor data and historical maintenance records, a Computational Mathematics researcher can develop predictive models to identify potential equipment failures in manufacturing processes. This can help companies schedule maintenance activities proactively, minimizing downtime and reducing maintenance costs.
Fraud Detection in Financial Services
Using advanced machine learning techniques, a Computational Mathematics expert can develop models to detect fraudulent activities in financial transactions. By analyzing patterns and anomalies in large datasets, they can help financial institutions identify and prevent fraudulent transactions, protecting both the company and its customers.
Optimizing Energy Consumption
A Computational Mathematics researcher can analyze energy consumption data and develop optimization algorithms to minimize energy usage in various industries. This can lead to significant cost savings and environmental benefits by identifying energy-efficient practices and optimizing resource allocation.
Improving Healthcare Analytics
By analyzing healthcare data, including patient records, medical imaging, and genomic data, a Computational Mathematics expert can develop models to improve disease diagnosis, treatment planning, and patient outcomes. This can help healthcare companies provide personalized and effective care to their patients.