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.

Experts on NotedSource with backgrounds in computational mathematics include Emmanouil Mentzakis, Edoardo Airoldi, Tim Osswald, Jeffrey Townsend, Ariel Aptekmann, Ping Luo, Panos Ipeirotis, Dr. Abdussalam Elhanashi, Asst. Prof. Eng. Davide Verzotto, Ph.D., Ernesto Lowy, Jonathan Tamir, and Niko Popitsch.

Emmanouil Mentzakis

London

Edoardo Airoldi

Professor of Statistics & Data Science Temple University & PI, Harvard University
Most Relevant Research Interests
Computational Mathematics
Other Research Interests (63)
Statistics
Causal Inference
Network Science
Statistical Machine Learning
Computational Biology
And 58 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

Tim Osswald

Polymers Professor - University of Wisconsin
Most Relevant Research Interests
Computational Mathematics
Other Research Interests (64)
Polymer and Composites Engineering
Polymer Engineering
Advanced Manufacturing
Composites
Additive Manufacturing
And 59 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

Jeffrey Townsend

New Haven, CT
Professor of Biostatistics and Ecology & Evolutionary Biology
Most Relevant Research Interests
Computational Mathematics
Other Research Interests (78)
Statistics
cancer genetics
disease modeling
antibiotic resistance
Evolutionary Genomics
And 73 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

Asst. Prof. Eng. Davide Verzotto, Ph.D.

Assistant Professor of Computer Science, Centre for Higher Defence Studies (CASD) - School of Advanced Studies, Italian Defence General Staff, Rome
Most Relevant Research Interests
Computational Mathematics
Other Research Interests (22)
Algorithms & Data Structures
Bioinformatics & Computational Biology
Machine Learning
Scalable
Multidisciplinary
And 17 more
About
Algorithms & Information Intelligence, Pattern Discovery Bioinformatics & Computational Biology/Genomics Scalable Data Mining & Machine Learning
Most Relevant Publications (3+)

40 total publications

The Irredundant Class Method for Remote Homology Detection of Protein Sequences

Journal of Computational Biology / Dec 01, 2011

Comin, M., & Verzotto, D. (2011). The Irredundant Class Method for Remote Homology Detection of Protein Sequences. Journal of Computational Biology, 18(12), 1819–1829. https://doi.org/10.1089/cmb.2010.0171

Editorial: Special Issue on Algorithms for Sequence Analysis and Storage

Algorithms / Mar 25, 2014

Mäkinen, V. (2014). Editorial: Special Issue on Algorithms for Sequence Analysis and Storage. Algorithms, 7(1), 186–187. https://doi.org/10.3390/a7010186

Filtering Degenerate Patterns with Application to Protein Sequence Analysis

Algorithms / May 22, 2013

Comin, M., & Verzotto, D. (2013). Filtering Degenerate Patterns with Application to Protein Sequence Analysis. Algorithms, 6(2), 352–370. https://doi.org/10.3390/a6020352

Jonathan Tamir

Most Relevant Research Interests
Computational Mathematics
Other Research Interests (11)
Signal processing
machine learning
magnetic resonance imaging
biomedical imaging
clinical translation
And 6 more
Most Relevant Publications (1+)

5 total publications

Memory-Efficient Learning for Large-Scale Computational Imaging

IEEE Transactions on Computational Imaging / Jan 01, 2020

Kellman, M., Zhang, K., Markley, E., Tamir, J., Bostan, E., Lustig, M., & Waller, L. (2020). Memory-Efficient Learning for Large-Scale Computational Imaging. IEEE Transactions on Computational Imaging, 6, 1403–1414. https://doi.org/10.1109/tci.2020.3025735

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.