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 Ping Luo, PhD.Heydy Castillejos, Tyler Ransom, Sarah Hicks, Ph.D., Michael Sebek, Nicolangelo Iannella, Edoardo Airoldi, Hector Klie, Jeffrey Townsend, Ryan Howell, and Tim Osswald.

Ping Luo

Toronto, Ontario, Canada
Bioinformatics Specialist at Princess Margaret Cancer Centre with experience in deep learning
Most Relevant Research Expertise
Applied Mathematics
Other Research Expertise (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 (3+)

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

See Full Profile

PhD.Heydy Castillejos

Weston, Florida, United States of America
Research professor, Universidad Autónoma del Estado de Hidalgo
Most Relevant Research Expertise
Applied Mathematics
Other Research Expertise (10)
Image and signal processing
boimedical signals
segmentation
classification
CAD
And 5 more
About
Results-oriented professional with over 15 years of experience in research and teaching. Skilled in Python, MATLAB programming, image processing, and telecommunications. Demonstrated ability to work under pressure, manage time effectively, and solve complex problems. Successfully advised doctoral candidates on data analysis methods, authored multiple peer-reviewed journal articles, and secured funding for future research initiatives. Developed innovative curricula for advanced mathematics courses and utilized technology to enhance learning experiences. A fast learner with excellent written and verbal communication skills.
Most Relevant Publications (1+)

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

See Full Profile

Tyler Ransom

Norman, Oklahoma, United States of America
Associate Professor of Economics at the University of Oklahoma
Most Relevant Research Expertise
Applied Mathematics
Other Research Expertise (14)
Economics
Labor Economics
Economics of Education
Urban Economics
Applied Microeconomics
And 9 more
About
Tyler Ransom is an associate professor of economics at the University of Oklahoma. He received his Ph.D. in economics from Duke University in 2015. His research interests include labor economics, economics of education, urban economics, and machine learning applications. He has published papers in leading journals such as the Journal of Labor Economics, the Journal of Human Resources, and the Journal of Econometrics. He is also an associate editor of the Annals of Economics and Statistics and a research affiliate of IZA and GLO. He has taught courses on econometrics, data science, and economics of education at both undergraduate and graduate levels. He has received several awards and fellowships for his research and teaching, such as the OU Dodge Family College of Arts & Sciences Irene Rothbaum Outstanding Assistant Professor Award in 2022. He is proficient in various coding languages such as Matlab, Stata, R, Julia, Bash, Git, and LaTeX. He also has advanced language skills in Japanese.
Most Relevant Publications (1+)

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

See Full Profile

Sarah Hicks, Ph.D.

Cleveland, Ohio, United States of America
Independent Researcher of Electro-Optics of liquid crystal and polymer materials.
Most Relevant Research Expertise
Applied Mathematics
Other Research Expertise (6)
liquid crystals
polymers
electro-optics
displays
Condensed Matter Physics
And 1 more
About
Experienced senior-level scientist boasting a decade-long tenure in Research and Development within the realms of material science, optics, and manufacturing, coupled with a proven track record in product management. Known for rapid adaptability and an insatiable enthusiasm for mastering and integrating unfamiliar materials into project landscapes. Adept at fostering cross-departmental camaraderie among team members and management to drive project success and catalyze company growth. Core competencies include: ·       Seasoned in R&D of polymer and liquid crystal composite materials with a focus on applications across consumer electronics, construction, aerospace/defense, and life sciences sectors. ·       Proficient in Product Management and Business Development within the realm of Materials Science. ·       Actively engaged in professional conferences, presenting research findings, and interacting with customers at exhibitions. ·       Skilled in cultivating and sustaining client relationships and vendor networks through effective communication and collaborative engagement.
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

See Full Profile

Michael Sebek

Boston, Massachusetts, United States of America
Northeastern University
Most Relevant Research Expertise
Applied Mathematics
Other Research Expertise (6)
network science
food science
electrochemistry
nonlinear dynamics
Mathematical Physics
And 1 more
About
Michael Sebek is a highly educated and experienced chemist with a passion for research and teaching. He received his Bachelor of Science in Chemistry from Truman State University in 2012, where he conducted undergraduate research in the field of analytical chemistry. He then went on to earn his Masters and Ph.D. in Chemistry from Saint Louis University by 2017, where his research focused on the interplay between network science and electrochemistry. After completing his Ph.D., Michael continued his research as a Post-Doctoral Researcher at Northeastern University, where he works in food science, network medicine, and AI/ML. His work has been published in several peer-reviewed journals and has been presented at national and international conferences.
Most Relevant Publications (4+)

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

See Full Profile

Nicolangelo Iannella

Oslo
Senior Research fellow, The University of Oslo, Faculty of Mathematics and Natural Sciences
Most Relevant Research Expertise
Applied Mathematics
Other Research Expertise (18)
Neuromorphic circuits
Neural networks, Neural learning and applications
Theoretical and Mathematical neuroscience
Computational neuroscience
Artificial Intelligence
And 13 more
About
Following pre-doctoral studies in Mathematics and Theoretical Physics, I received a PhD in Computational Neuroscience from the University of Electro-Communications, Japan in 2009. From 2009, I was a Postdoctoral Researcher in RIKEN BSI. In 2010, I won the prestigious Australian Research Council (ARC) Australian Postdoctoral Award (APD) fellowship, based at the University of Adelaide from 2010–2014. In 2012 he completed a Graduate Certificate in Education (Higher Education) (GCEHE) from the University of Adelaide. From 2014–2017 he was an adjunct research fellow at the University of South Australia. From 2016–2018, he was a Cascade (Marie Curie) Research Fellow in Mathematical Sciences at the University of Nottingham. From 2018- a research fellow at the University of Oslo. His research interests include AI, Artificial and spiking neural networks and learning algorithms, synaptic plasticity, neuronal dynamics, and neuromorphic engineering. Dr. Iannella is a member of SFN and a Senior member of the IEEE.
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

See Full Profile

Edoardo Airoldi

Professor of Statistics & Data Science Temple University & PI, Harvard University
Most Relevant Research Expertise
Applied Mathematics
Other Research Expertise (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 (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

See Full Profile

Hector Klie

Houston, Texas, United States of America
CEO @ DeepCast.ai | AI-driven Industrial Solutions, Technical Innovation
Most Relevant Research Expertise
Applied Mathematics
Other Research Expertise (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

Numerical Comparisons of Path-Following Strategies for a Primal-Dual Interior-Point Method for Nonlinear Programming

Journal of Optimization Theory and Applications / Aug 01, 2002

Argáez, M., Tapia, R., & Velázquez, L. (2002). Numerical Comparisons of Path-Following Strategies for a Primal-Dual Interior-Point Method for Nonlinear Programming. Journal of Optimization Theory and Applications, 114(2), 255–272. https://doi.org/10.1023/a:1016047200413

A Parallel Stochastic Framework for Reservoir Characterization and History Matching

Journal of Applied Mathematics / Jan 01, 2011

Thomas, S. G., Klie, H. M., Rodriguez, A. A., & Wheeler, M. F. (2011). A Parallel Stochastic Framework for Reservoir Characterization and History Matching. Journal of Applied Mathematics, 2011, 1–19. https://doi.org/10.1155/2011/535484

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

See Full Profile

Jeffrey Townsend

New Haven, CT, Connecticut, United States of America
Professor of Biostatistics and Ecology & Evolutionary Biology
Most Relevant Research Expertise
Applied Mathematics
Other Research Expertise (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 (2+)

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

See Full Profile

Ryan Howell

San Francisco , California, United States of America
Professor of Psychology, Department of Psychology, San Francisco State University
Most Relevant Research Expertise
Applied Mathematics
Other Research Expertise (33)
Happiness
Psychiatry and Mental health
Clinical Psychology
History and Philosophy of Science
Applied Psychology
And 28 more
About
Dr. Ryan Howell is an Associate Professor at San Francisco State University. His research interests include the psychology of goals and how people pursue and achieve them. Dr. Howell received his PhD in Social/Personality Psychology from the University of California, Riverside in 2005.
Most Relevant Publications (1+)

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

See Full Profile

Tim Osswald

Polymers Professor - University of Wisconsin
Most Relevant Research Expertise
Applied Mathematics
Other Research Expertise (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 (2+)

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

See Full Profile

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.