Work with thought leaders and academic experts in Applied Mathematics
Companies can benefit from working with someone whose expertise is in the field of Applied Mathematics in several ways. Applied Mathematics researchers can help companies solve complex problems by applying mathematical models and algorithms. They can also assist in data analysis and provide insights for making data-driven decisions. Additionally, they can develop optimization algorithms to improve efficiency and reduce costs. Applied Mathematics experts can also contribute to the development of predictive models for forecasting market trends and optimizing business strategies. Overall, collaborating with an Applied Mathematics researcher can provide companies with a competitive edge and help them leverage the power of data.
Researchers on NotedSource with backgrounds in Applied Mathematics include Michael Sebek, PhD.Heydy Castillejos, Ping Luo, Sarah Hicks, Ph.D., Tyler Ransom, Nicolangelo Iannella, Edoardo Airoldi, Jeffrey Townsend, Ryan Howell, Tim Osswald, Jo Boaler, and Dmitry Batenkov, Ph.D..
Michael Sebek
Northeastern University
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21 total publications
Synchronization of three electrochemical oscillators: From local to global coupling
Chaos: An Interdisciplinary Journal of Nonlinear Science / Apr 01, 2018
Liu, Y., Sebek, M., Mori, F., & Kiss, I. Z. (2018). Synchronization of three electrochemical oscillators: From local to global coupling. Chaos: An Interdisciplinary Journal of Nonlinear Science, 28(4). https://doi.org/10.1063/1.5012520
Revival of oscillations from deaths in diffusively coupled nonlinear systems: Theory and experiment
Chaos: An Interdisciplinary Journal of Nonlinear Science / Jun 01, 2017
Zou, W., Sebek, M., Kiss, I. Z., & Kurths, J. (2017). Revival of oscillations from deaths in diffusively coupled nonlinear systems: Theory and experiment. Chaos: An Interdisciplinary Journal of Nonlinear Science, 27(6). https://doi.org/10.1063/1.4984927
Plasticity facilitates pattern selection of networks of chemical oscillations
Chaos: An Interdisciplinary Journal of Nonlinear Science / Aug 01, 2019
Sebek, M., & Kiss, I. Z. (2019). Plasticity facilitates pattern selection of networks of chemical oscillations. Chaos: An Interdisciplinary Journal of Nonlinear Science, 29(8). https://doi.org/10.1063/1.5109784
Finding influential nodes in networks using pinning control: Centrality measures confirmed with electrochemical oscillators
Chaos: An Interdisciplinary Journal of Nonlinear Science / Sep 01, 2023
Bomela, W., Sebek, M., Nagao, R., Singhal, B., Kiss, I. Z., & Li, J.-S. (2023). Finding influential nodes in networks using pinning control: Centrality measures confirmed with electrochemical oscillators. Chaos: An Interdisciplinary Journal of Nonlinear Science, 33(9). https://doi.org/10.1063/5.0163899
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PhD.Heydy Castillejos
Research professor, Universidad Autónoma del Estado de Hidalgo
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15 total publications
Wavelet Transform Fuzzy Algorithms for Dermoscopic Image Segmentation
Computational and Mathematical Methods in Medicine / Jan 01, 2012
Castillejos, H., Ponomaryov, V., Nino-de-Rivera, L., & Golikov, V. (2012). Wavelet Transform Fuzzy Algorithms for Dermoscopic Image Segmentation. Computational and Mathematical Methods in Medicine, 2012, 1–11. https://doi.org/10.1155/2012/578721
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Ping Luo
Bioinformatics Specialist at Princess Margaret Cancer Centre with experience in deep learning
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23 total publications
Disease Gene Prediction by Integrating PPI Networks, Clinical RNA-Seq Data and OMIM Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics / Jan 01, 2019
Luo, P., Tian, L.-P., Ruan, J., & Wu, F.-X. (2019). Disease Gene Prediction by Integrating PPI Networks, Clinical RNA-Seq Data and OMIM Data. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 16(1), 222–232. https://doi.org/10.1109/tcbb.2017.2770120
Identifying cell types from single-cell data based on similarities and dissimilarities between cells
BMC Bioinformatics / May 01, 2021
Li, Y., Luo, P., Lu, Y., & Wu, F.-X. (2021). Identifying cell types from single-cell data based on similarities and dissimilarities between cells. BMC Bioinformatics, 22(S3). https://doi.org/10.1186/s12859-020-03873-z
Ensemble disease gene prediction by clinical sample-based networks
BMC Bioinformatics / Mar 01, 2020
Luo, P., Tian, L.-P., Chen, B., Xiao, Q., & Wu, F.-X. (2020). Ensemble disease gene prediction by clinical sample-based networks. BMC Bioinformatics, 21(S2). https://doi.org/10.1186/s12859-020-3346-8
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Sarah Hicks, Ph.D.
Independent Researcher of Electro-Optics of liquid crystal and polymer materials.
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Other Research Expertise (6)
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Most Relevant Publications (1+)
10 total publications
P‐111: Effect of Pressure on Polymer Stabilized Cholesteric Texture Light Shutter
SID Symposium Digest of Technical Papers / Jun 01, 2009
Ma, J., Hicks, S., Hurley, S., & Yang, D. (2009). P‐111: Effect of Pressure on Polymer Stabilized Cholesteric Texture Light Shutter. SID Symposium Digest of Technical Papers, 40(1), 1532–1535. Portico. https://doi.org/10.1889/1.3256605
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Tyler Ransom
Associate Professor of Economics at the University of Oklahoma
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Other Research Expertise (14)
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15 total publications
Understanding migration aversion using elicited counterfactual choice probabilities
Journal of Econometrics / Nov 01, 2022
Koşar, G., Ransom, T., & van der Klaauw, W. (2022). Understanding migration aversion using elicited counterfactual choice probabilities. Journal of Econometrics, 231(1), 123–147. https://doi.org/10.1016/j.jeconom.2020.07.056
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Nicolangelo Iannella
Senior Research fellow, The University of Oslo, Faculty of Mathematics and Natural Sciences
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Most Relevant Publications (2+)
47 total publications
The Brain & Neural Networks / Jan 01, 2011
(2011). The Brain & Neural Networks, 18(1), 2–3. https://doi.org/10.3902/jnns.18.2
Firing properties of a stochastic PDE model of a rat sensory cortex layer 2/3 pyramidal cell
Mathematical Biosciences / Mar 01, 2004
Iannella, N., Tuckwell, H. C., & Tanaka, S. (2004). Firing properties of a stochastic PDE model of a rat sensory cortex layer 2/3 pyramidal cell. Mathematical Biosciences, 188(1–2), 117–132. https://doi.org/10.1016/j.mbs.2003.10.002
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Edoardo Airoldi
Professor of Statistics & Data Science Temple University & PI, Harvard University
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Other Research Expertise (43)
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Most Relevant Publications (5+)
106 total publications
Stochastic blockmodels with a growing number of classes
Biometrika / Apr 17, 2012
Choi, D. S., Wolfe, P. J., & Airoldi, E. M. (2012). Stochastic blockmodels with a growing number of classes. Biometrika, 99(2), 273–284. https://doi.org/10.1093/biomet/asr053
Model-assisted design of experiments in the presence of network-correlated outcomes
Biometrika / Aug 06, 2018
Basse, G. W., & Airoldi, E. M. (2018). Model-assisted design of experiments in the presence of network-correlated outcomes. Biometrika, 105(4), 849–858. https://doi.org/10.1093/biomet/asy036
Testing for arbitrary interference on experimentation platforms
Biometrika / Sep 30, 2019
Pouget-Abadie, J., Saint-Jacques, G., Saveski, M., Duan, W., Ghosh, S., Xu, Y., & Airoldi, E. M. (2019). Testing for arbitrary interference on experimentation platforms. Biometrika, 106(4), 929–940. https://doi.org/10.1093/biomet/asz047
Who wrote Ronald Reagan's radio addresses?
Bayesian Analysis / Jun 01, 2006
Airoldi, E. M., Anderson, A. G., Fienberg, S. E., & Skinner, K. K. (2006). Who wrote Ronald Reagan’s radio addresses? Bayesian Analysis, 1(2). https://doi.org/10.1214/06-ba110
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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Jeffrey Townsend
Professor of Biostatistics and Ecology & Evolutionary Biology
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207 total publications
Identifying modules of cooperating cancer drivers
Molecular Systems Biology / Mar 01, 2021
Klein, M. I., Cannataro, V. L., Townsend, J. P., Newman, S., Stern, D. F., & Zhao, H. (2021). Identifying modules of cooperating cancer drivers. Molecular Systems Biology, 17(3). Portico. https://doi.org/10.15252/msb.20209810
Codon Deviation Coefficient: a novel measure for estimating codon usage bias and its statistical significance
BMC Bioinformatics / Mar 22, 2012
Zhang, Z., Li, J., Cui, P., Ding, F., Li, A., Townsend, J. P., & Yu, J. (2012). Codon Deviation Coefficient: a novel measure for estimating codon usage bias and its statistical significance. BMC Bioinformatics, 13(1). https://doi.org/10.1186/1471-2105-13-43
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Ryan Howell
Professor of Psychology, Department of Psychology, San Francisco State University
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64 total publications
The File Drawer Problem in Reliability Generalization
Educational and Psychological Measurement / Jun 06, 2007
Howell, R. T., & Shields, A. L. (2007). The File Drawer Problem in Reliability Generalization. Educational and Psychological Measurement, 68(1), 120–128. https://doi.org/10.1177/0013164407301528
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Tim Osswald
Polymers Professor - University of Wisconsin
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117 total publications
Boundary integral equations for analyzing the flow of a chopped fiber reinforced polymer compound in compression molding
Journal of Non-Newtonian Fluid Mechanics / Jan 01, 1987
Barone, M. R., & Osswald, T. A. (1987). Boundary integral equations for analyzing the flow of a chopped fiber reinforced polymer compound in compression molding. Journal of Non-Newtonian Fluid Mechanics, 26(2), 185–206. https://doi.org/10.1016/0377-0257(87)80004-6
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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Jo Boaler
Professor of Mathematics Education, Stanford University
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81 total publications
Mathematics from Another World: Traditional Communities and the Alienation of Learners
The Journal of Mathematical Behavior / Jun 01, 2000
Boaler, J. (2000). Mathematics from Another World: Traditional Communities and the Alienation of Learners. The Journal of Mathematical Behavior, 18(4), 379–397. https://doi.org/10.1016/s0732-3123(00)00026-2
The many colors of algebra: The impact of equity focused teaching upon student learning and engagement
The Journal of Mathematical Behavior / Mar 01, 2016
Boaler, J., & Sengupta-Irving, T. (2016). The many colors of algebra: The impact of equity focused teaching upon student learning and engagement. The Journal of Mathematical Behavior, 41, 179–190. https://doi.org/10.1016/j.jmathb.2015.10.007
Designing mathematics classes to promote equity and engagement
The Journal of Mathematical Behavior / Mar 01, 2016
Boaler, J. (2016). Designing mathematics classes to promote equity and engagement. The Journal of Mathematical Behavior, 41, 172–178. https://doi.org/10.1016/j.jmathb.2015.01.002
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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 (15+)
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
On the Accuracy of Solving Confluent Prony Systems
SIAM Journal on Applied Mathematics / Jan 01, 2013
Batenkov, D., & Yomdin, Y. (2013). On the Accuracy of Solving Confluent Prony Systems. SIAM Journal on Applied Mathematics, 73(1), 134–154. https://doi.org/10.1137/110836584
Super-resolution of near-colliding point sources
Information and Inference: A Journal of the IMA / May 11, 2020
Batenkov, D., Goldman, G., & Yomdin, Y. (2020). Super-resolution of near-colliding point sources. Information and Inference: A Journal of the IMA, 10(2), 515–572. https://doi.org/10.1093/imaiai/iaaa005
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
Stability and super-resolution of generalized spike recovery
Applied and Computational Harmonic Analysis / Sep 01, 2018
Batenkov, D. (2018). Stability and super-resolution of generalized spike recovery. Applied and Computational Harmonic Analysis, 45(2), 299–323. https://doi.org/10.1016/j.acha.2016.09.004
Geometry and Singularities of the Prony mapping
Journal of Singularities / Jan 01, 2014
Batenkov, D., & Yomdin, Y. (2014). Geometry and Singularities of the Prony mapping. Journal of Singularities. https://doi.org/10.5427/jsing.2014.10a
Moment inversion problem for piecewise D -finite functions
Inverse Problems / Sep 16, 2009
Batenkov, D. (2009). Moment inversion problem for piecewise D -finite functions. Inverse Problems, 25(10), 105001. https://doi.org/10.1088/0266-5611/25/10/105001
Decimated Prony's Method for Stable Super-Resolution
IEEE Signal Processing Letters / Jan 01, 2023
Katz, R., Diab, N., & Batenkov, D. (2023). Decimated Prony’s Method for Stable Super-Resolution. IEEE Signal Processing Letters, 30, 1467–1471. https://doi.org/10.1109/lsp.2023.3324553
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
Super-resolution of generalized spikes and spectra of confluent Vandermonde matrices
Applied and Computational Harmonic Analysis / Jul 01, 2023
Batenkov, D., & Diab, N. (2023). Super-resolution of generalized spikes and spectra of confluent Vandermonde matrices. Applied and Computational Harmonic Analysis, 65, 181–208. https://doi.org/10.1016/j.acha.2023.03.002
Single-exponential bounds for the smallest singular value of Vandermonde matrices in the sub-Rayleigh regime
Applied and Computational Harmonic Analysis / Nov 01, 2021
Batenkov, D., & Goldman, G. (2021). Single-exponential bounds for the smallest singular value of Vandermonde matrices in the sub-Rayleigh regime. Applied and Computational Harmonic Analysis, 55, 426–439. https://doi.org/10.1016/j.acha.2021.07.003
Stable soft extrapolation of entire functions
Inverse Problems / Dec 07, 2018
Batenkov, D., Demanet, L., & Mhaskar, H. N. (2018). Stable soft extrapolation of entire functions. Inverse Problems, 35(1), 015011. https://doi.org/10.1088/1361-6420/aaedde
Uniform upper bounds for the cyclicity of the zero solution of the Abel differential equation
Journal of Differential Equations / Dec 01, 2015
Batenkov, D., & Binyamini, G. (2015). Uniform upper bounds for the cyclicity of the zero solution of the Abel differential equation. Journal of Differential Equations, 259(11), 5769–5781. https://doi.org/10.1016/j.jde.2015.07.009
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
Integral Geometry Problems with Incomplete Data
Journal of Mathematical Sciences / Sep 02, 2014
Batenkov, D. V., Golubyatnikov, V. P., & Yomdin, Y. N. (2014). Integral Geometry Problems with Incomplete Data. Journal of Mathematical Sciences, 202(1), 25–39. https://doi.org/10.1007/s10958-014-2031-8
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Example Applied Mathematics projects
How can companies collaborate more effectively with researchers, experts, and thought leaders to make progress on Applied Mathematics?
Optimizing Supply Chain Management
An Applied Mathematics expert can develop mathematical models and algorithms to optimize supply chain management. By analyzing data on inventory levels, transportation costs, and customer demand, they can help companies minimize costs and improve efficiency in their supply chain operations.
Predictive Maintenance in Manufacturing
By analyzing sensor data and historical maintenance records, an Applied Mathematics researcher can develop predictive models to identify potential equipment failures in manufacturing processes. This can help companies schedule maintenance activities proactively, reduce downtime, and optimize maintenance costs.
Risk Analysis in Finance
Applied Mathematics experts can help financial institutions analyze and manage risks by developing mathematical models and algorithms. They can assess the probability of default, calculate value-at-risk, and optimize investment portfolios to maximize returns while minimizing risks.
Optimizing Energy Consumption
By analyzing energy consumption data and considering factors such as weather conditions and occupancy patterns, an Applied Mathematics researcher can develop optimization algorithms to minimize energy usage in buildings. This can help companies reduce their carbon footprint and lower energy costs.
Data Analysis for Healthcare
Applied Mathematics researchers can analyze healthcare data to identify patterns and trends, predict disease outbreaks, and optimize resource allocation. This can help healthcare providers improve patient outcomes, optimize staffing levels, and allocate resources efficiently.