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, Ping Luo, Nicolangelo Iannella, Jeffrey Townsend, Emmanouil Mentzakis, Tim Osswald, Hector Klie, Dmitry Batenkov, Ph.D., Edoardo Airoldi, Ariel Aptekmann, and Abbas Alameer.
Siddharth Maddali
Computational physicist with a specialization in X-ray and optical imaging and microscopy for condensed matter and materials systems.
Education
Carnegie Mellon University
PhD, Physics / May, 2016
Carnegie Mellon University
MS, Physics / May, 2011
Indian Institute of Technology Madras
M.Sc, Physics / May, 2009
Experience
KLA (United States)
Research Scientist / November, 2022 — Present
Sensitivity enhancement of optical inspection of semiconductor wafers
Argonne National Laboratory
Staff Scientist / October, 2019 — September, 2022
(1) Imaging: Inverse problems for 3D nanoscale materials imaging using coherent X-ray probes. (2) Time-resolved studies: Signal processing methods for XPCS at free electron laser facilities. (3) Experiments: POCs & demonstrations for the above at APS/future APS-U instruments. (4) Fundraising: Research grants (LDRD, DoE), APS, ESRF user-time proposals. (5) Dissemination/Outreach: Publications, peer review, editorship, conferences, tech reports. (6) Mentoring/Organization: Postdocs, students (unofficial), workshop planning/chairing.
Post-doctoral researcher / January, 2017 — September, 2019
National Energy Technology Laboratory
Postdoctoral Fellow / May, 2016 — November, 2016
Machine learning -driven materials discovery of steel alloys for optimized power plant components
Most Relevant Research Expertise
Other Research Expertise (21)
About
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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Ping Luo
Assistant Professor at Algoma University
Education
University of Saskatchewan
Ph.D., Biomedical Engineering / September, 2019
Beijing Institute of Technology
M.Eng., Biomedical Engineering / June, 2015
Hunan University
B.Eng., Computer Science / June, 2010
Experience
Princess Margaret Cancer Centre
Postdoctoral Researcher / November, 2019 — Present
I work in Dr. Trevor Pugh's lab and design cancer diagnosis and treatment strategies by analyze cell-free DNA and single cell sequencing data
Princess Margaret Cancer Centre
Bioinformatics Specialist / September, 2023 — Present
I work in Dr. Tak Mak's lab and study tumor immunology using single cell and TCR sequencing data.
Most Relevant Research Expertise
Other Research Expertise (21)
About
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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Nicolangelo Iannella
Senior Research fellow, The University of Oslo, Faculty of Mathematics and Natural Sciences
Education
University of Adelaide
Graduate Certificate in Education (Higher Education) , School of Electrical & Electronic engineering / December, 2012
Denki Tsushin Daigaku
PhD (Eng), Information and Communications Engineering / March, 2009
Experience
University of Oslo
Postdoctoral Fellow / July, 2018 — Present
Most Relevant Research Expertise
Other Research Expertise (18)
About
Most Relevant Publications (1+)
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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Jeffrey Townsend
Professor of Biostatistics and Ecology & Evolutionary Biology
Education
Harvard University
Ph.D., Organismic and Evolutionary Biology / May, 2002
Brown University
Sc.B., Biology / May, 1994
Experience
Yale University
Professor / July, 2018 — Present
Elihu Professor of Biostatistics / July, 2018 — Present
Elihu Associate Professor of Biostatistics / July, 2017 — June, 2018
Associate Professor / July, 2013 — June, 2018
Associate Professor / July, 2013 — June, 2017
Assistant Professor / July, 2006 — June, 2013
University of Connecticut
Assistant Professor / August, 2004 — May, 2006
St. Ann's School
Teacher / September, 1994 — June, 1997
Most Relevant Research Expertise
Other Research Expertise (52)
About
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
Education
University of Aberdeen
PhD, Health Economics Research Unit
Experience
University of Southampton
Lecturer in Economics / September, 2012 — August, 2014
Associate Professor in Economics / September, 2014 — June, 2022
Professor in Economics / July, 2022 — January, 2023
NHS England
Analytical Lead / October, 2019 — July, 2020
Led the update of the CCGs need-based funding allocation formula.
City University of London
Professor / February, 2023 — Present
Head of Department of Economics / February, 2023 — Present
Most Relevant Research Expertise
Other Research Expertise (21)
About
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
Education
University of Illinois at Urbana-Champaign
PhD, Mechanical Engineering / January, 1987
South Dakota School of Mines and Technology
M.S., Mechanical Engineering / May, 1982
South Dakota School of Mines and Technology
B.S., Mechanical Engineering / May, 1981
Experience
University of Wisconsin Madison
Professor / August, 1989 — Present
Rheinisch Westfalische Technische Hochschule Aachen
Humboldt Fellow / February, 1987 — June, 1989
Most Relevant Research Expertise
Other Research Expertise (44)
About
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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Hector Klie
CEO @ DeepCast.ai | AI-driven Industrial Solutions, Technical Innovation
Education
Ph.D., Computational Science and Engineering / May, 1997
Master of Arts, Computational and Applied Mathematics / May, 1995
Simón Bolívar University
Master of Science, Computer Science / May, 1991
Experience
DeepCast, LLC
CEO / May, 2017 — Present
ConocoPhillips Company
Staff Data Scientist / March, 2008 — April, 2016
Sanchez Oil and Gas
Director of Enterprise Data Solutions / March, 2016 — March, 2017
Design corporate data science platform, lead R&D in machine learning and AI to generate highly predictive models for field applications
Most Relevant Research Expertise
Other Research Expertise (23)
About
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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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.
Education
Weizmann Institute of Science
Ph.D., Applied Mathematics / January, 2014
Experience
Tel Aviv University
Assistant Professor / July, 2019 — Present
Producing high-impact research in inverse problems, super-resolution, numerical analysis, signal processing, physics-informed machine learning, computational harmonic analysis, optimization, atmospheric remote sensing • Advised 4 postdocs, 2 PhD, 4 M.Sc. students and 3 undergraduates • Developed and taught an advanced graduate class on Inverse Problems and Super-Resolution
Most Relevant Research Expertise
Other Research Expertise (30)
About
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
Education
Università Bocconi
B.Sc., Institute for Quantitative Methods
Carnegie Mellon University
Ph.D., School of Computer Science
Experience
Harvard University
Most Relevant Research Expertise
Other Research Expertise (43)
About
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
Education
University of Buenos Aires
PhD, Biological Chemistry, Bioinformatics / February, 2018
Rutgers, The State University of New Jersey
Postdoc, Computational Biology / March, 2021
Experience
Hackensack Meridian Hospital Center for Discovery and Innovation
Bioinformatician / March, 2021 — Present
Most Relevant Research Expertise
Other Research Expertise (21)
About
Most Relevant Publications (1+)
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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Abbas Alameer
Assistant Professor of Bioinformatics at Kuwait University
Education
University of Leicester
PhD in Bioinformatics and Molecular Modelling, Department of Molecular and Cell Biology / January, 2014
University of Leeds
MRes in Bioinformatics and Computational Biology / December, 2006
Experience
Kuwait University
Assistant Professor of Bioinformatics / February, 2014 — Present
Most Relevant Research Expertise
Other Research Expertise (10)
About
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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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.