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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 Siddharth Maddali, Dr. Fantai Kong, Ph.D., Ping Luo, Jeffrey Townsend, Emmanouil Mentzakis, Tim Osswald, Dmitry Batenkov, Ph.D., Edoardo Airoldi, Ariel Aptekmann, Denys Dutykh, Jose Nino, Ph.D, Abbas Alameer, and Hector Klie.

Siddharth Maddali

Fremont, California, United States of America
Computational physicist with a specialization in X-ray and optical imaging and microscopy for condensed matter and materials systems.
Most Relevant Research Interests
Computational Mathematics
Other Research Interests (21)
Computational microscopy
Fourier/physical optics
signal processing
physics
HPC
And 16 more
About
Computational materials, imaging and microscopy scientist with **8 years combined experience** in industry and national laboratories. Expert in physics-based imaging and characterization with X-rays and optical probes, high-performance computing for light-matter interaction and materials data analysis. Experienced in machine learning for materials discovery. Previous experience at the National Energy Technology Laboratory, Argonne National Laboratory and KLA Corporation. <br>
Most Relevant Publications (2+)

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.

Dallas, Texas, United States of America
Hunt Energy
Most Relevant Research Interests
Computational Mathematics
Other Research Interests (19)
Energy Storage
Renewable Energy, Sustainability and the Environment
Electronic, Optical and Magnetic Materials
Surfaces, Coatings and Films
Materials Chemistry
And 14 more
About
With over 10 years of research and development experience in the field of energy storage and conversion technologies, I have gained extensive expertise in diverse areas such as Li-ion batteries, Zn-ion batteries, Na-ion batteries, Li extraction, fuel cells, and topological insulators. Throughout my career, I have actively collaborated with partners from industrial, national labs, and universities to overcome technology challenges and develop innovative solutions that have resulted in a prolific publication record, including more than 30 peer-reviewed articles, and 10 awarded/pending patents.
Most Relevant Publications (4+)

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

Toronto, Ontario, Canada
Bioinformatics Specialist at Princess Margaret Cancer Centre with experience in deep learning
Most Relevant Research Interests
Computational Mathematics
Other Research Interests (21)
single-cell genomics
deep learning
complex network analysis
Genetics (clinical)
Genetics
And 16 more
About
8 years of science and engineering experience integrating multi-omics data to identify biomarkers for cancer studies. Seeking to apply data analytics expertise to develop new diagnosis and treatment strategies.
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

New Haven, CT
Professor of Biostatistics and Ecology & Evolutionary Biology
Most Relevant Research Interests
Computational Mathematics
Other Research Interests (52)
Evolutionary Genomics
Microbiology
Infectious Diseases
Genetics
Cell Biology
And 47 more
About
Jeffrey Townsend is a Professor of Organismic and Evolutionary Biology at Yale University. He received his Ph.D. from Harvard University in 2002 and his Sc.B. from Brown University in 1994. He has been a teacher at St. Ann's School and an Assistant Professor at the University of Connecticut. He is currently the Elihu Professor of Biostatistics at Yale University.
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

London
Health Economist, Professor at City University of London
Most Relevant Research Interests
Computational Mathematics
Other Research Interests (21)
Pulmonary and Respiratory Medicine
Pediatrics, Perinatology and Child Health
Economics and Econometrics
Finance
Accounting
And 16 more
About
Senior academic and policy advisor. Public and private sector consultant with remit ranging from health ministries and public organizations to insurance and pharmaceutical companies. Cross-institutional leader in research and admin roles focusing on excellence, efficiency, innovation, and community. Strategic and proactive thinker with clear vision and plan, approaching challenges with creativity and adaptability. Highly motivational manager with strong communication skills and impeccable project management track-record.   Expert scholar and educator in health economics, discrete choice experiments, research study design and observational epidemiology. Long experience setting-up and coordinating multi-disciplinary teams into delivering high quality research.
Most Relevant Publications (1+)

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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Tim Osswald

Polymers Professor - University of Wisconsin
Most Relevant Research Interests
Computational Mathematics
Other Research Interests (44)
Polymer Engineering
Advanced Manufacturing
Composites
Additive Manufacturing
Materials Chemistry
And 39 more
About
T. Osswald is Hoeganaes Professor of Materials at the University of Wisconsin-Madison, where he has been a faculty member since 1989. Osswald received the PhD in Mechanical Engineering from the University of Illinois at Urbana-Champaign in 1987, the MS in Mechanical Engineering from the South Dakota School of Mines and Technology in 1982, and the BS in Mechanical Engineering from the South Dakota School of Mines and Technology in 1981. Before joining the UW-Madison faculty, Osswald was a Humboldt Fellow at the Rheinisch Westfalische Technische Hochschule Aachen. Osswald’s research interests are in the areas of processing-structure-property relationships for metals and composites, with a focus on powder metallurgy and metal injection molding. His research has been supported by the National Science Foundation, the Department of Energy, the US Army Research Office, and industry. Osswald is a Fellow of ASM International and the American Academy of Mechanics, and he has received the Extrusion Division Award, the Powder Metallurgy Division Award, and the Distinguished Teaching Award from TMS.
Most Relevant Publications (1+)

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.

New York City, New York, United States of America
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.
Most Relevant Research Interests
Computational Mathematics
Other Research Interests (30)
Applied Harmonic Analysis
Sparse Representations
Numerical Analysis
Approximation Theory
Inverse Problems
And 25 more
About
I am passionate about solving big problems with scientific and computational tools. 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. A highly-skilled software engineer and analyst/architect with 6+ years of experience as a technical lead in professional software development.
Most Relevant Publications (6+)

51 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

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
Most Relevant Research Interests
Computational Mathematics
Other Research Interests (43)
Statistics
Causal Inference
Network Science
Cell Biology
Molecular Biology
And 38 more
About
Edoardo Airoldi is a Professor in the Department of Machine Learning at Temple University. He is also the Director of the Center for Machine Learning and Health. He is a world-renowned expert in the fields of machine learning and artificial intelligence, with a focus on applications to health. Airoldi is a member of the prestigious Association for the Advancement of Artificial Intelligence (AAAI) and the International Machine Learning Society (IMLS). He has published over 200 papers in leading journals and conferences, and his work has been covered by various media outlets including The New York Times, The Wall Street Journal, The Economist, and Wired.
Most Relevant Publications (2+)

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
Most Relevant Research Interests
Computational Mathematics
Other Research Interests (21)
Computational biology
bioinformatics
metagenomics
evolution
Genetics
And 16 more
About
I am a bioinformatician and researcher in computational biology. My research focuses on the application of machine learning and data analysis to understand the molecular basis of disease. I also work on developing novel software tools for data management and analysis.
Most Relevant Publications (1+)

24 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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Denys Dutykh

Professional Applied Mathematician, Modeller, and Advisor
Most Relevant Research Interests
Computational Mathematics
Other Research Interests (50)
Applied mathematics
fluid mechanics
scientific computing
numerical methods
Fluid Flow and Transfer Processes
And 45 more
About
Dr. Denys Dutykh initially comes from the broad field of Applied Mathematics. He did his Master's degree in numerical methods applied to the problems of Continuum Mechanics and a Ph.D. thesis at Ecole Normale Supérieure de Cachan (France) on the mathematical modeling of tsunami waves. After this, he was hired as a permanent research scientist at the Institute of Mathematics (INSMI) at the Centre National de la Recherche Scientifique (CNRS). His research activities have been conducted in the following years at the picturesque University Savoie Mont Blanc (USMB, France) in the field of mathematical methods applied to the modeling and simulation of nonlinear waves (mostly in Fluid Dynamics). The Habilitation thesis of Dr. Dutykh was defended there on the topic of the mathematical methods in the environment. Since then, his research interests have significantly broadened to include the Dimensionality Reduction methods in Machine Learning, modeling of PV panels, and even some more theoretical questions in the Number Theory.
Most Relevant Publications (14+)

186 total publications

Finite volume schemes for dispersive wave propagation and runup

Journal of Computational Physics / Apr 01, 2011

Dutykh, D., Katsaounis, T., & Mitsotakis, D. (2011). Finite volume schemes for dispersive wave propagation and runup. Journal of Computational Physics, 230(8), 3035–3061. https://doi.org/10.1016/j.jcp.2011.01.003

Efficient computation of steady solitary gravity waves

Wave Motion / Jan 01, 2014

Dutykh, D., & Clamond, D. (2014). Efficient computation of steady solitary gravity waves. Wave Motion, 51(1), 86–99. https://doi.org/10.1016/j.wavemoti.2013.06.007

On the Galerkin/Finite-Element Method for the Serre Equations

Journal of Scientific Computing / Feb 05, 2014

Mitsotakis, D., Ilan, B., & Dutykh, D. (2014). On the Galerkin/Finite-Element Method for the Serre Equations. Journal of Scientific Computing, 61(1), 166–195. https://doi.org/10.1007/s10915-014-9823-3

Geometric numerical schemes for the KdV equation

Computational Mathematics and Mathematical Physics / Feb 01, 2013

Dutykh, D., Chhay, M., & Fedele, F. (2013). Geometric numerical schemes for the KdV equation. Computational Mathematics and Mathematical Physics, 53(2), 221–236. https://doi.org/10.1134/s0965542513020103

Free Surface Flows in Electrohydrodynamics with a Constant Vorticity Distribution

Water Waves / Oct 07, 2020

Hunt, M. J., & Dutykh, D. (2020). Free Surface Flows in Electrohydrodynamics with a Constant Vorticity Distribution. Water Waves, 3(2), 297–317. https://doi.org/10.1007/s42286-020-00043-9

A comparative study of bi-directional Whitham systems

Applied Numerical Mathematics / Jul 01, 2019

Dinvay, E., Dutykh, D., & Kalisch, H. (2019). A comparative study of bi-directional Whitham systems. Applied Numerical Mathematics, 141, 248–262. https://doi.org/10.1016/j.apnum.2018.09.016

Evaluation of the reliability of building energy performance models for parameter estimation

Вычислительные технологии / Jun 17, 2019

Берже,   Жулиан, & Дутых,   Денис. (2019). Evaluation of the reliability of building energy performance models for parameter estimation. Вычислительные Технологии, 3(24). https://doi.org/10.25743/ict.2019.24.3.002

On some model equations for pulsatile flow in viscoelastic vessels

Wave Motion / Aug 01, 2019

Mitsotakis, D., Dutykh, D., Li, Q., & Peach, E. (2019). On some model equations for pulsatile flow in viscoelastic vessels. Wave Motion, 90, 139–151. https://doi.org/10.1016/j.wavemoti.2019.05.004

Wave dynamics on networks: Method and application to the sine-Gordon equation

Applied Numerical Mathematics / Sep 01, 2018

Dutykh, D., & Caputo, J.-G. (2018). Wave dynamics on networks: Method and application to the sine-Gordon equation. Applied Numerical Mathematics, 131, 54–71. https://doi.org/10.1016/j.apnum.2018.03.010

Asymptotic nonlinear and dispersive pulsatile flow in elastic vessels with cylindrical symmetry

Computers &amp; Mathematics with Applications / Jun 01, 2018

Mitsotakis, D., Dutykh, D., & Li, Q. (2018). Asymptotic nonlinear and dispersive pulsatile flow in elastic vessels with cylindrical symmetry. Computers &amp; Mathematics with Applications, 75(11), 4022–4047. https://doi.org/10.1016/j.camwa.2018.03.011

On supraconvergence phenomenon for second order centered finite differences on non-uniform grids

Journal of Computational and Applied Mathematics / Dec 01, 2017

Khakimzyanov, G., & Dutykh, D. (2017). On supraconvergence phenomenon for second order centered finite differences on non-uniform grids. Journal of Computational and Applied Mathematics, 326, 1–14. https://doi.org/10.1016/j.cam.2017.05.006

On the nonlinear dynamics of the traveling-wave solutions of the Serre system

Wave Motion / Apr 01, 2017

Mitsotakis, D., Dutykh, D., & Carter, J. (2017). On the nonlinear dynamics of the traveling-wave solutions of the Serre system. Wave Motion, 70, 166–182. https://doi.org/10.1016/j.wavemoti.2016.09.008

Efficient computation of capillary–gravity generalised solitary waves

Wave Motion / Sep 01, 2016

Dutykh, D., Clamond, D., & Durán, A. (2016). Efficient computation of capillary–gravity generalised solitary waves. Wave Motion, 65, 1–16. https://doi.org/10.1016/j.wavemoti.2016.04.007

A new run-up algorithm based on local high-order analytic expansions

Journal of Computational and Applied Mathematics / May 01, 2016

Khakimzyanov, G., Shokina, N. Yu., Dutykh, D., & Mitsotakis, D. (2016). A new run-up algorithm based on local high-order analytic expansions. Journal of Computational and Applied Mathematics, 298, 82–96. https://doi.org/10.1016/j.cam.2015.12.004

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Jose Nino, Ph.D

Salt Lake City, Utah, United States of America
Ph.D. candidate in computational materials science | Research/Data Scientist | Python, SQL, Matlab
Most Relevant Research Interests
Computational Mathematics
Other Research Interests (7)
Mechanics of Materials
Aging
Genetics
Cell Biology
Molecular Biology
And 2 more
About
Ph.D. in Engineering with an emphasis on Computational Materials Science. Working on projects for different industries, I have gained significant experience with programming (Python, SQL, Matlab, VBA) and mechanical design, including proficiency in SolidWorks. I'm skilled in handling projects from start to finish, which includes developing hypotheses, collecting and analyzing data, and presenting results.
Most Relevant Publications (2+)

2 total publications

Evolution of crystallographic texture and grain boundary network structure during anisotropic grain growth

Computational Materials Science / May 01, 2024

Niño, J., & Johnson, O. K. (2024). Evolution of crystallographic texture and grain boundary network structure during anisotropic grain growth. Computational Materials Science, 240, 113023. https://doi.org/10.1016/j.commatsci.2024.113023

Influence of grain boundary energy anisotropy on the evolution of grain boundary network structure during 3D anisotropic grain growth

Computational Materials Science / Jan 01, 2023

Niño, J. D., & Johnson, O. K. (2023). Influence of grain boundary energy anisotropy on the evolution of grain boundary network structure during 3D anisotropic grain growth. Computational Materials Science, 217, 111879. https://doi.org/10.1016/j.commatsci.2022.111879

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Abbas Alameer

Assistant Professor of Bioinformatics at Kuwait University
Most Relevant Research Interests
Computational Mathematics
Other Research Interests (10)
Bioinformatics
Computational Theory and Mathematics
Computer Science Applications
Molecular Biology
Biochemistry
And 5 more
About
Abbas Alameer is an Assistant Professor of Bioinformatics at Kuwait University. He received his PhD in Bioinformatics and Molecular Modelling from the University of Leicester in 2014 and his MRes in Bioinformatics and Computational Biology from the University of Leeds in 2006. He has over 10 years of experience in bioinformatics related research and teaching. His research focuses on the computational analysis and modelling of biological molecules, and the development of novel algorithms and Bioinformatics tools. He has published several articles in leading journals in the field and has presented his work at international conferences.
Most Relevant Publications (2+)

3 total publications

geoCancerPrognosticDatasetsRetriever: a bioinformatics tool to easily identify cancer prognostic datasets on Gene Expression Omnibus (GEO)

Bioinformatics / Dec 22, 2021

Alameer, A., & Chicco, D. (2021). geoCancerPrognosticDatasetsRetriever: a bioinformatics tool to easily identify cancer prognostic datasets on Gene Expression Omnibus (GEO). Bioinformatics, 38(6), 1761–1763. https://doi.org/10.1093/bioinformatics/btab852

Towards a potential pan-cancer prognostic signature for gene expression based on probesets and ensemble machine learning

BioData Mining / Nov 03, 2022

Chicco, D., Alameer, A., Rahmati, S., & Jurman, G. (2022). Towards a potential pan-cancer prognostic signature for gene expression based on probesets and ensemble machine learning. BioData Mining, 15(1). https://doi.org/10.1186/s13040-022-00312-y

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Hector Klie

CEO @ DeepCast.ai | AI-driven Industrial Solutions, Technical Innovation
Most Relevant Research Interests
Computational Mathematics
Other Research Interests (23)
Artificial Intelligence
Machine Learning
Data Science
optimization
Computational Theory and Mathematics
And 18 more
About
**Results-driven AI leader with 20+ years of success spearheading model development and optimization initiatives in the energy industry and academia. Proven track record in leveraging computational data science, scientific machine learning, and AI to drive breakthrough physics-data solutions and deliver tangible business value. Adept at translating complex scientific concepts into robust AI models. Skilled in numerical simulation, scientific machine learning, and bilingual communication to optimize project outcomes.**
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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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.