Experts and Thought Leaders in Mathematics

Dr. KEHINDE ADEWALE ADESINA, Ph.D

Harlow
Assistant Professor in engineering (food, industrial and process) with quantitative and qualitative research in food processing, safefy, efficiency management, optimization and data analysis
Most Relevant Research Interests
Computational Mathematics
Applied Mathematics
Other Research Interests (17)
Artificial Intelligence
Software
Management Science and Operations Research
Safety, Risk, Reliability and Quality
Information Systems
And 12 more
About
Dr. KEHINDE ADEWALE ADESINA, Ph.D is a highly educated and experienced individual in the field of Industrial Engineering, Process, and Food Engineering. He obtained his PhD in Industrial Engineering from Eastern Mediterranean University in 2018, where he focused on research related to optimization and process improvement in manufacturing industries. Prior to his PhD, Dr. Adesina completed his Master's degree in Chemical Engineering from Obafemi Awolowo University in 2011 and his Bachelor's degree in Chemical Engineering from Ladoke Akintola University of Technology in 2003. Dr. Adesina has also gained valuable teaching experience as an Assistant Professor at Near East University Nicosia/KKTC and as a Lecturer at Rufus Giwa Polytechnic. He has taught a variety of courses related to industrial engineering, food engineering, chemical engineering, and research methodology. In addition to his teaching experience, Dr. Adesina has also worked as a Research Assistant at Eastern Mediterranean University, where he conducted research on various industrial engineering topics. Through his education and experience, Dr. Adesina has developed a strong understanding of industrial engineering principles and techniques, as well as a passion for research and teaching. He continues to contribute to the field through his academic work and is dedicated to helping students and industries improve their processes and operations.
Most Relevant Publications (2+)

27 total publications

A fuzzy rough copula Bayesian network model for solving complex hospital service quality assessment

Complex & Intelligent Systems / Mar 24, 2023

Li, H., Yazdi, M., Huang, H.-Z., Huang, C.-G., Peng, W., Nedjati, A., & Adesina, K. A. (2023). A fuzzy rough copula Bayesian network model for solving complex hospital service quality assessment. Complex & Intelligent Systems, 9(5), 5527–5553. https://doi.org/10.1007/s40747-023-01002-w

Modified variable return to scale back-propagation neural network robust parameter optimization procedure for multi-quality processes

Engineering Optimization / Nov 05, 2018

Daneshvar, S., & Adesina, K. A. (2018). Modified variable return to scale back-propagation neural network robust parameter optimization procedure for multi-quality processes. Engineering Optimization, 51(8), 1352–1369. https://doi.org/10.1080/0305215x.2018.1524463

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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
Applied Mathematics
Other Research Interests (20)
single-cell genomics
deep learning
complex network analysis
Genetics (clinical)
Genetics
And 15 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 (4+)

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

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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Michael Sebek

Boston, Massachusetts, United States of America
Northeastern University
Most Relevant Research Interests
Mathematical Physics
Applied Mathematics
Other Research Interests (5)
network science
food science
electrochemistry
nonlinear dynamics
Statistical and Nonlinear Physics
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+)

22 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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Tyler Ransom

Norman, Oklahoma, United States of America
Associate Professor of Economics at the University of Oklahoma
Most Relevant Research Interests
Applied Mathematics
Other Research Interests (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

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Jo Boaler

Palo Alto, California, United States of America
Professor of Mathematics Education, Stanford University
Most Relevant Research Interests
mathematics education
Applied Mathematics
Other Research Interests (28)
equity
gender
mindset
learning
Education
And 23 more
About
Dr Jo Boaler is a Professor of Mathematics Education at Stanford University, and the co-founder of youcubed. Her PhD won the national award for educational research in the UK and her book: Experiencing School Mathematics won the ‘Outstanding Book of the Year’ award for education in Britain. She is an elected fellow of the Royal Society of Arts (Great Britain), and a former president of the International Organization for Women and Mathematics Education (IOWME). She is the recipient of a National Science Foundation ‘Early Career Award’ and the NCSM Kay Gilliland Equity Award (2014). She is the author of nine books and numerous research articles. Her latest book is Mathematical Mindsets: Unleashing Students’ Potential through Creative Math, Inspiring Messages and Innovative Teaching (2016), and is published by Wiley. She serves as an advisor to several Silicon Valley companies.
Most Relevant Publications (3+)

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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Edoardo Airoldi

Professor of Statistics & Data Science Temple University & PI, Harvard University
Most Relevant Research Interests
Applied Mathematics
Computational Mathematics
Other Research Interests (42)
Statistics
Causal Inference
Network Science
Cell Biology
Molecular Biology
And 37 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 (6+)

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

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

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

New Haven, CT
Professor of Biostatistics and Ecology & Evolutionary Biology
Most Relevant Research Interests
Applied Mathematics
Computational Mathematics
Other Research Interests (51)
Evolutionary Genomics
Microbiology
Infectious Diseases
Genetics
Cell Biology
And 46 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 (6+)

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

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

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

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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Tim Osswald

Polymers Professor - University of Wisconsin
Most Relevant Research Interests
Applied Mathematics
Computational Mathematics
Computational Mechanics
Other Research Interests (42)
Polymer Engineering
Advanced Manufacturing
Composites
Additive Manufacturing
Materials Chemistry
And 37 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 (4+)

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

Novel modeling approach for fiber breakage during molding of long fiber-reinforced thermoplastics

Physics of Fluids / Jul 01, 2021

Bechara, A., Goris, S., Yanev, A., Brands, D., & Osswald, T. (2021). Novel modeling approach for fiber breakage during molding of long fiber-reinforced thermoplastics. Physics of Fluids, 33(7), 073318. https://doi.org/10.1063/5.0058693

Data enriched lubrication force modeling for a mechanistic fiber simulation of short fiber-reinforced thermoplastics

Physics of Fluids / May 01, 2021

Kugler, S. K., Bechara, A., Perez, H., Cruz, C., Kech, A., & Osswald, T. A. (2021). Data enriched lubrication force modeling for a mechanistic fiber simulation of short fiber-reinforced thermoplastics. Physics of Fluids, 33(5), 053107. https://doi.org/10.1063/5.0049641

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David Blanchett

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Laura Jackson Young

Associate Professor of Economics, Bentley University
Most Relevant Research Interests
Applied Mathematics
Other Research Interests (10)
Economics
Macroeconomics
Econometrics
Economics and Econometrics
Analysis
And 5 more
About
Laura Jackson Young graduated from the University of North Carolina at Chapel Hill in December 2014 with a PhD in Economics. She is now an Associate Professor of Economics at Bentley University.  ​ Founder and co-advisor for [WEB: Women in Economics at Bentley](https://webentley.weebly.com/). Faculty Research Director for the [Gloria Cordes Larson Center for Women and Business](https://www.bentley.edu/centers/center-for-women-and-business) **Research Interests:** Macroeconomics, Monetary and Fiscal Policy, Financial Economics, Business Cycles, Econometrics
Most Relevant Publications (3+)

21 total publications

Nonlinearities, smoothing and countercyclical monetary policy

Journal of Economic Dynamics and Control / Oct 01, 2018

Jackson, L. E., Owyang, M. T., & Soques, D. (2018). Nonlinearities, smoothing and countercyclical monetary policy. Journal of Economic Dynamics and Control, 95, 136–154. https://doi.org/10.1016/j.jedc.2018.08.007

International Stock Comovements with Endogenous Clusters

Journal of Economic Dynamics and Control / Jul 01, 2020

Coroneo, L., Jackson, L. E., & Owyang, M. T. (2020). International Stock Comovements with Endogenous Clusters. Journal of Economic Dynamics and Control, 116, 103904. https://doi.org/10.1016/j.jedc.2020.103904

Debt and stabilization policy: Evidence from a Euro Area FAVAR

Journal of Economic Dynamics and Control / Aug 01, 2018

Jackson, L. E., Owyang, M. T., & Zubairy, S. (2018). Debt and stabilization policy: Evidence from a Euro Area FAVAR. Journal of Economic Dynamics and Control, 93, 67–91. https://doi.org/10.1016/j.jedc.2018.02.001

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Hector Klie

CEO @ DeepCast.ai | AI-driven Industrial Solutions, Technical Innovation
Most Relevant Research Interests
Computational Mathematics
Computational Mechanics
Applied Mathematics
Other Research Interests (21)
Artificial Intelligence
Machine Learning
Data Science
optimization
Computational Theory and Mathematics
And 16 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 (6+)

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

Stochastic collocation and mixed finite elements for flow in porous media

Computer Methods in Applied Mechanics and Engineering / Aug 01, 2008

Ganis, B., Klie, H., Wheeler, M. F., Wildey, T., Yotov, I., & Zhang, D. (2008). Stochastic collocation and mixed finite elements for flow in porous media. Computer Methods in Applied Mechanics and Engineering, 197(43–44), 3547–3559. https://doi.org/10.1016/j.cma.2008.03.025

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

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 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

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Anit Kumar Sahu

PhD from CMU working in ML/AI
Most Relevant Research Interests
Applied Mathematics
Other Research Interests (19)
Federated Learning
Stochastic Optimization
Data Selection
Electrical and Electronic Engineering
Signal Processing
And 14 more
About
Anit Kumar Sahu completed his PhD from Carnegie Mellon University in 2018, focusing on statistical machine learning and stochastic optimization. During his time at CMU, Anit worked on numerous research projects and published several papers in top-tier conferences and journals. After completing his PhD, Anit joined Amazon Services LLC as a Senior Applied Scientist. In this role, he is responsible for developing and implementing machine learning models and algorithms to enhance the performance of Amazon's services and products. Prior to joining Amazon, Anit worked as a Machine Learning Research Scientist at Bosch Center for Artificial Intelligence. He was actively involved in developing cutting-edge machine learning solutions for various industrial applications, including autonomous vehicles, smart homes, and industrial automation. Anit is a passionate and driven individual, constantly seeking new challenges and opportunities to further his knowledge and expertise in the field of electrical and computer engineering. With his strong educational background, extensive experience, and innovative mindset, he is a valuable asset to any organization.
Most Relevant Publications (3+)

62 total publications

Federated Learning: Challenges, Methods, and Future Directions

IEEE Signal Processing Magazine / May 01, 2020

Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated Learning: Challenges, Methods, and Future Directions. IEEE Signal Processing Magazine, 37(3), 50–60. https://doi.org/10.1109/msp.2020.2975749

Nonlinear Gradient Mappings and Stochastic Optimization: A General Framework with Applications to Heavy-Tail Noise

SIAM Journal on Optimization / May 16, 2023

Jakovetić, D., Bajović, D., Sahu, A. K., Kar, S., Milos̆ević, N., & Stamenković, D. (2023). Nonlinear Gradient Mappings and Stochastic Optimization: A General Framework with Applications to Heavy-Tail Noise. SIAM Journal on Optimization, 33(2), 394–423. https://doi.org/10.1137/21m145896x

Distributed Recursive Estimation under Heavy-Tail Communication Noise

SIAM Journal on Control and Optimization / Jun 20, 2023

Jakovetic, D., Vukovic, M., Bajovic, D., Sahu, A. K., & Kar, S. (2023). Distributed Recursive Estimation under Heavy-Tail Communication Noise. SIAM Journal on Control and Optimization, 61(3), 1582–1609. https://doi.org/10.1137/22m1477015

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