Work with thought leaders and academic experts in Numerical Analysis

Companies can benefit from working with Numerical Analysis experts in several ways. These researchers can help optimize processes, solve complex problems, and improve decision-making through mathematical modeling, algorithm development, and data analysis. They can also assist in developing and implementing numerical methods and algorithms for various applications, such as optimization, simulation, and machine learning. Additionally, their expertise can be valuable in areas like risk assessment, financial modeling, and predictive analytics. Collaborating with these experts can lead to improved efficiency, cost savings, and competitive advantage.

Researchers on NotedSource with backgrounds in Numerical Analysis include Hector Klie, Dmitry Batenkov, Ph.D., Denys Dutykh, Tim Leung, David Blanchett, Baidurya Bhattacharya, Oguzhan Kulekci, Dr. Abdussalam Elhanashi, Enrico Capobianco, Rameche Candane Somassoundirame, and Mohammad Vahab.

Hector Klie

Houston, Texas, United States of America

21 Years Experience

CEO @ DeepCast.ai | AI-driven Industrial Solutions, Technical Innovation

Education

Ph.D., Computational Science and Engineering / May, 1997

Houston, Texas, United States of America

Master of Arts, Computational and Applied Mathematics / May, 1995

Houston

Simón Bolívar University

Master of Science, Computer Science / May, 1991

Caracas

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 Publications (1+)

81 total publications

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.

New York City, New York, United States of America

15 Years Experience

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

Rehovot

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

Numerical Analysis

Other Research Expertise (30)

Applied Harmonic Analysis

Sparse Representations

Approximation Theory

Inverse Problems

Algebra and Number Theory

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 (2+)

50 total publications

Super-resolution of near-colliding point sources

Information and Inference: A Journal of the IMA / May 11, 2020

Batenkov, D., Goldman, G., & Yomdin, Y. (2020). Super-resolution of near-colliding point sources. Information and Inference: A Journal of the IMA, 10(2), 515–572. https://doi.org/10.1093/imaiai/iaaa005

The spectral properties of Vandermonde matrices with clustered nodes

Linear Algebra and its Applications / Jan 01, 2021

Batenkov, D., Diederichs, B., Goldman, G., & Yomdin, Y. (2021). The spectral properties of Vandermonde matrices with clustered nodes. Linear Algebra and Its Applications, 609, 37–72. https://doi.org/10.1016/j.laa.2020.08.034

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Denys Dutykh

16 Years Experience

Professional Applied Mathematician, Modeller, and Advisor

Education

École Normale Supérieure Paris-Saclay

PhD, Centre de Mathématiques et de Leurs Applications / December, 2007

Cachan

Experience

Centre National de la Recherche Scientifique

Research scientist / October, 2008 — August, 2022

Professional scientific research in the field of Applied Mathematics

Khalifa University of Science and Technology

Associate Professor / August, 2022 — Present

Professional research and educational activities in the field of Applied Mathematics

Most Relevant Research Expertise

Numerical Analysis

Other Research Expertise (50)

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

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

Conservative modified Serre–Green–Naghdi equations with improved dispersion characteristics

Communications in Nonlinear Science and Numerical Simulation / Apr 01, 2017

Clamond, D., Dutykh, D., & Mitsotakis, D. (2017). Conservative modified Serre–Green–Naghdi equations with improved dispersion characteristics. Communications in Nonlinear Science and Numerical Simulation, 45, 245–257. https://doi.org/10.1016/j.cnsns.2016.10.009

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

A spectral method for solving heat and moisture transfer through consolidated porous media

International Journal for Numerical Methods in Engineering / Dec 03, 2018

Gasparin, S., Dutykh, D., & Mendes, N. (2018). A spectral method for solving heat and moisture transfer through consolidated porous media. International Journal for Numerical Methods in Engineering, 117(11), 1143–1170. Portico. https://doi.org/10.1002/nme.5994

An efficient numerical model for liquid water uptake in porous material and its parameter estimation

Numerical Heat Transfer, Part A: Applications / Jan 17, 2019

Jumabekova, A., Berger, J., Dutykh, D., Le Meur, H., Foucquier, A., Pailha, M., & Ménézo, C. (2019). An efficient numerical model for liquid water uptake in porous material and its parameter estimation. Numerical Heat Transfer, Part A: Applications, 75(2), 110–136. https://doi.org/10.1080/10407782.2018.1562739

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

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

Non-dispersive conservative regularisation of nonlinear shallow water (and isentropic Euler equations)

Communications in Nonlinear Science and Numerical Simulation / Feb 01, 2018

Clamond, D., & Dutykh, D. (2018). Non-dispersive conservative regularisation of nonlinear shallow water (and isentropic Euler equations). Communications in Nonlinear Science and Numerical Simulation, 55, 237–247. https://doi.org/10.1016/j.cnsns.2017.07.011

Some special solutions to the Hyperbolic NLS equation

Communications in Nonlinear Science and Numerical Simulation / Apr 01, 2018

Vuillon, L., Dutykh, D., & Fedele, F. (2018). Some special solutions to the Hyperbolic NLS equation. Communications in Nonlinear Science and Numerical Simulation, 57, 202–220. https://doi.org/10.1016/j.cnsns.2017.09.018

Derivation of dissipative Boussinesq equations using the Dirichlet-to-Neumann operator approach

Mathematics and Computers in Simulation / Sep 01, 2016

Dutykh, D., & Goubet, O. (2016). Derivation of dissipative Boussinesq equations using the Dirichlet-to-Neumann operator approach. Mathematics and Computers in Simulation, 127, 80–93. https://doi.org/10.1016/j.matcom.2013.12.008

Energy equation for certain approximate models of long-wave hydrodynamics

Russian Journal of Numerical Analysis and Mathematical Modelling / Jan 01, 2014

Fedotova, Z. I., Khakimzyanov, G. S., & Dutykh, D. (2014). Energy equation for certain approximate models of long-wave hydrodynamics. Russian Journal of Numerical Analysis and Mathematical Modelling, 29(3). https://doi.org/10.1515/rnam-2014-0013

Simulation of surface waves generated by an underwater landslide in a bounded reservoir

Russian Journal of Numerical Analysis and Mathematical Modelling / Jan 01, 2012

Beizel, S. A., Chubarov, L. B., Dutykh, D., Khakimzyanov, G. S., & Shokina, N. Yu. (2012). Simulation of surface waves generated by an underwater landslide in a bounded reservoir. Russian Journal of Numerical Analysis and Mathematical Modelling, 27(6). https://doi.org/10.1515/rnam-2012-0031

Tsunami generation by dynamic displacement of sea bed due to dip-slip faulting

Mathematics and Computers in Simulation / Dec 01, 2009

Dutykh, D., & Dias, F. (2009). Tsunami generation by dynamic displacement of sea bed due to dip-slip faulting. Mathematics and Computers in Simulation, 80(4), 837–848. https://doi.org/10.1016/j.matcom.2009.08.036

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Tim Leung

15 Years Experience

Professor of Applied Mathematics, Computational Finance & Risk Management (CFRM) Program

Education

Princeton University

PhD, Operations Research & Financial Engineering

Princeton, New Jersey, United States of America

Cornell University

B.S., Operations Research & Industrial Engineering / May, 2003

Ithaca, New York, United States of America

Experience

University of Washington

Professor / 2016 — Present

Columbia University

Assistant Professor / 2011 — 2016

Johns Hopkins University

Assistant Professor / 2008 — 2011

Most Relevant Research Expertise

Numerical Analysis

Other Research Expertise (21)

Computational Finance

Risk Management

Portfolio Optimization

ETFs

Finance

And 16 more

About

Tim Leung is a professor of operations research and financial engineering at the University of Washington. He holds a PhD from Princeton University and a B.S. from Cornell University. He has held previous positions as an assistant professor at Columbia University and Johns Hopkins University.

Most Relevant Publications (2+)

138 total publications

ESO Valuation with Job Termination Risk and Jumps in Stock Price

SIAM Journal on Financial Mathematics / Jan 01, 2015

Leung, T., & Wan, H. (2015). ESO Valuation with Job Termination Risk and Jumps in Stock Price. SIAM Journal on Financial Mathematics, 6(1), 487–516. https://doi.org/10.1137/130937949

Optimal Timing to Purchase Options

SIAM Journal on Financial Mathematics / Jan 01, 2011

Leung, T., & Ludkovski, M. (2011). Optimal Timing to Purchase Options. SIAM Journal on Financial Mathematics, 2(1), 768–793. https://doi.org/10.1137/100809386

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David Blanchett

11 Years Experience

Current Director - PGIM (Formerly Prudential Investment Management); Adjunct Professor of Finance

Education

Texas Tech University

Ph.D, Finance

Lubbock, Texas, United States of America

University of Chicago

MBA, Finance / 2010

Chicago, Illinois, United States of America

University of Kentucky

BBA, Finance

Lexington, Kentucky, United States of America

Experience

Prudential Investment Management

Managing Director / June, 2021 — Present

Morningstar

Head of Retirement Research / 2012 — 2021

Most Relevant Research Expertise

Numerical Analysis

Other Research Expertise (13)

Pharmacology (medical)

Life-span and Life-course Studies

Organizational Behavior and Human Resource Management

Geriatrics and Gerontology

Finance

And 8 more

About

David Blanchett, PhD, is Adjunct Professor of Wealth Management at The American College of Financial Services. A world-renowned thought leader in the fields of wealth management and retirement, Blanchett is a leading contributor to the Wealth Management Certified Professional® (WMCP®) designation program. In addition to his role with The College, Blanchett is the managing director and head of retirement research at QMA, a division of Prudential Financial, and formerly at Morningstar Investment Management, LLC. In his roles, Blanchett works to enhance consulting and investment services and conducts research primarily in the areas of financial planning, tax planning, annuities, and retirement.

Most Relevant Publications (1+)

82 total publications

Optimal Initiation of Guaranteed Lifelong Withdrawal Benefit with Dynamic Withdrawals

SIAM Journal on Financial Mathematics / Jan 01, 2017

Huang, Y. T., Zeng, P., & Kwok, Y. K. (2017). Optimal Initiation of Guaranteed Lifelong Withdrawal Benefit with Dynamic Withdrawals. SIAM Journal on Financial Mathematics, 8(1), 804–840. https://doi.org/10.1137/16m1089575

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Baidurya Bhattacharya

23 Years Experience

Computational mechanics, probabilistic risk analysis, statistical inference, Monte Carlo simulations

Education

Johns Hopkins University

PhD, Civil Engineering / January, 1997

Baltimore, Maryland, United States of America

Indian Institute of Technology Kharagpur

B.Tech (hons.), Civil Engineering / April, 1991

Kharagpur

Experience

University of Delaware

Visiting Professor / September, 2022 — Present

Assistant Professor / August, 2001 — February, 2006

Indian Institute of Technology Kharagpur

Professor / February, 2006 — Present

Most Relevant Research Expertise

Numerical Analysis

Other Research Expertise (43)

computational materials science

probabilistic mechanics

Mechanical Engineering

Industrial and Manufacturing Engineering

Mechanics of Materials

And 38 more

About

Baidurya Bhattacharya is a highly accomplished and respected civil engineer with over 20 years of experience in the field. He was born in Kolkata, India and completed his B.Tech (hons.) in Civil Engineering from the prestigious Indian Institute of Technology Kharagpur in 1991. He then went on to pursue his PhD in Civil Engineering from Johns Hopkins University, which he completed in 1997. After completing his PhD, Bhattacharya started his academic career as a Visiting Professor at the University of Delaware. He then moved on to become an Assistant Professor at the same university, where he taught for several years and mentored numerous students. In 2005, he returned to his alma mater, Indian Institute of Technology Kharagpur, as a Professor in the Department of Civil Engineering. He has been a valuable member of the faculty and has made significant contributions to the department through his research and teaching. Bhattacharya's research interests lie in the areas of structural engineering, earthquake engineering, and soil dynamics. He has published numerous papers in reputable journals and has also presented his work at various international conferences. His research has been recognized and funded by prestigious organizations such as the National Science Foundation and the American Society of Civil Engineers. Aside from his academic career, Bhattacharya is also actively involved in consulting and has worked on various projects in collaboration with government agencies and private firms. He is known for his expertise and has received several awards and honors for his contributions to the field of civil engineering. Bhattacharya is a dedicated educator and mentor, and he continues to inspire and guide young engineers through his teaching and research. His passion for the field and his dedication to his students make him a highly respected figure in the academic community.

Most Relevant Publications (1+)

91 total publications

A Probabilistic Model of Flooding Loads on Transverse Watertight Bulkheads in the Event of Hull Damage

Journal of Ship Research / Mar 01, 2005

Bhattacharya, B., Basu, R., & Srinivasan, S. (2005). A Probabilistic Model of Flooding Loads on Transverse Watertight Bulkheads in the Event of Hull Damage. Journal of Ship Research, 49(01), 12–23. https://doi.org/10.5957/jsr.2005.49.1.12

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Oguzhan Kulekci

25 Years Experience

Algorithm Engineer, Security/Privacy Researcher, Combinatorial Problem Solver

Education

Sabancı University

Ph.D., Computer Science / July, 2006

Istanbul

Experience

Indiana University

Visiting Professor / January, 2022 — Present

Istanbul Teknik Üniversitesi

Professor / November, 2015 — Present

national research institute of electronics and cryptology

Chief Researcher / January, 2007 — March, 2014

Design, analysis, and implementation of cryptographic security and privacy algorithms

Senior Researcher / June, 2004 — May, 2007

Design, analysis, and implementation of cryptographic security and privacy algorithms

Researcher / June, 1999 — June, 2004

Design, analysis, and implementation of cryptographic security and privacy algorithms

Most Relevant Research Expertise

Numerical Analysis

Other Research Expertise (20)

About

My main expertise is in solving computational challenges with an innovative algorithm engineering approach. For more than two decades, I have been studying on such challenges originating from different fields mainly in cryptography and data security, natural language processing, information retrieval, computational biology, data compression and coding, massive data management, and most recently focusing on scalability and security aspects of ML/AI algorithms. I have been devising efficient innovative solutions and/or improving current state-of-art in terms of resource usage, e.g., time, memory, energy, communication costs. I would like to provide a summary of my previous achievements in engineering, research, and administration. Engineering Expertise: After spending around two years on programming point-of-sales devices and regular database programming, I have spent 10+ years in cryptography, where the main focus had been efficient implementation and cryptanalysis of the security&privacy algorithms and protocols both in hardware and software. During those years, despite gaining experience on how to develop programs that run fast and/or with small memory footprint, I had the chance to work with talented mathematicians and hardware engineers, that gave me the opportunity to widen my knowledge on different dimensions, including reverse engineering and FPGA/ASIC design. I also learned a lot on how to develop projects with a team of talent coming from different disciplines. I have observed, and today strongly believe, that theoretical knowledge is vital, but never enough to built efficient systems in practice. The platform that the solution will be executed on and the properties of the input data should always be considered for ground-breaking progress in practical performance. Theory without practice, or vice versa, is akin to trying to fly with one wing. In that sense, the development of the fastest pattern matching solutions and innovating patents that are licensed to companies have been exemplary outcomes of my perspective. Academic Expertise: Following my 15+ years in industry, I joined academia and have been serving as a professor of computer sci- ence. I succeeded to get several research grants and have been also serving in the committees of conferences. Actually, I started publishing in scientific venues when I was with the industry as well. I did my phd on natu- ral language processing, after which I got more engaged with combinatorial algorithms. I mostly published on data compression, combinatorial pattern matching and applications of them on computational biol- ogy/bioinformatics. Most recently, I have been studying scalablity and security aspects in ML/AI systems as well as in information retrieval. I have also experience in massive data management and analysis. I have been teaching courses on algorithms, security/privacy, and related topics. Administrative Expertise: After engineering cryptography for many years, I changed my focus to computational biology, particularly the genomics area. I have served as the deputy director of the National Institute of Genetics and Biotechnology of Turkey for two years, during which I was responsible for the establishment of the first high-throughput DNA sequencing facility of the country. That leadership equipped me with a unique experience of leading an interdisciplinary project with people from computing and life sciences disciplines. The establishment of the lab was supported with more than 2 million dollars grant by the government and was successfully completed in two years. Another leadership experience I had was being the program coordinator of the graduate programs in my university for more than four years. I was responsible by curriculum development and hiring new faculty. I have also served previously as principal investigator in research projects, lead research labs, and delivered project lead positions in industry projects.

Most Relevant Publications (2+)

61 total publications

A Survey on Shortest Unique Substring Queries

Algorithms / Sep 06, 2020

Abedin, P., Külekci, M., & Thankachan, S. (2020). A Survey on Shortest Unique Substring Queries. Algorithms, 13(9), 224. https://doi.org/10.3390/a13090224

Applications of Non-Uniquely Decodable Codes to Privacy-Preserving High-Entropy Data Representation

Algorithms / Apr 17, 2019

Külekci, M. O., & Öztürk, Y. (2019). Applications of Non-Uniquely Decodable Codes to Privacy-Preserving High-Entropy Data Representation. Algorithms, 12(4), 78. https://doi.org/10.3390/a12040078

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Dr. Abdussalam Elhanashi

4 Years Experience

Researcher at University of Pisa

Education

University of Pisa

PhD, Department of Information Engineering / February, 2023

Pisa

University of Nicosia

MBA, Department of Business Management / March, 2018

Nicosia

University of Glasgow

Master of Science, Department of Electronics and Electrical Engineering / January, 2018

Glasgow

Experience

University of Pisa

Researcher / July, 2019 — Present

Most Relevant Research Expertise

Numerical Analysis

Other Research Expertise (20)

Machine learning Deep learning and Imaging processing IoT devices Object detection Embedded system

Information Systems

Electrical and Electronic Engineering

Computer Science Applications

Modeling and Simulation

And 15 more

About

Dr Abdussalam is a researcher at the Università di Pisa, Italia. He received M.Sc. degree in Electronic Engineering from the University of Glasgow in Scotland in 2018. He authored and co-authored several scientific articles in international conferences and journals . He is a member IET , and a member of IEEE. His current research interests are Deep learning, imaging processing, medical images, embedded systems, Power optimization management and IoT devices.

Most Relevant Publications (1+)

28 total publications

An IoT System for Social Distancing and Emergency Management in Smart Cities Using Multi-Sensor Data

Algorithms / Oct 07, 2020

Fedele, R., & Merenda, M. (2020). An IoT System for Social Distancing and Emergency Management in Smart Cities Using Multi-Sensor Data. Algorithms, 13(10), 254. https://doi.org/10.3390/a13100254

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Enrico Capobianco

25 Years Experience

Expertise in network science and special interest in cancer domain. Scientific Leader, Advisor. Quant, Computational & Digital Biomedical & Health research.

Education

Stanford University

Post-doctoral Fellowship, AI, Machine & Statistical Learning, Neural Networks / 1998

Stanford, California, United States of America

University of Padua

PhD, Statistical Sciences / 1995

Padova

Experience

The Jackson Laboratory

Associate Director, Computational Systems / 2018 — 2024

Most Relevant Research Expertise

Numerical Analysis

Other Research Expertise (34)

Networks

Machine Learning

Big Data

Systems Biology & Medicine

Statistics

And 29 more

About

Enrico Capobianco is a highly experienced and accomplished expert in the fields of artificial intelligence, machine learning, and statistical learning. He holds a Post-doctoral Fellowship in AI, Machine & Statistical Learning, Neural Networks from Stanford University, which he completed in 1998. Prior to that, he received his PhD in Statistical Sciences from the University of Padua in 1995. With over 25 years of experience, Capobianco has held various positions in academia, research, and industry. Most recently, he served as the Associate Director of Computational Systems at The Jackson Laboratory, a leading non-profit research institute focused on genetics and genomics. In this role, he oversaw the development and implementation of computational systems and tools for genetic and genomic research. Throughout his career, Capobianco has published numerous articles and book chapters on topics such as machine learning, artificial intelligence, and computational biology. He has also been a keynote speaker at various international conferences and has received numerous awards and grants for his research. In addition to his professional achievements, Capobianco is known for his collaborative and innovative approach to problem-solving. He is constantly seeking new ways to apply advanced computational techniques to solve complex problems in various industries, from healthcare to finance. Overall, Enrico Capobianco is a highly respected and sought-after expert in the fields of AI, machine learning, and statistical learning. His education and experience have equipped him with the knowledge and skills to make significant contributions to the advancement of these fields.

Most Relevant Publications (1+)

94 total publications

Entropy embedding and fluctuation analysis in genomic manifolds

Communications in Nonlinear Science and Numerical Simulation / Jun 01, 2009

Capobianco, E. (2009). Entropy embedding and fluctuation analysis in genomic manifolds. Communications in Nonlinear Science and Numerical Simulation, 14(6), 2602–2618. https://doi.org/10.1016/j.cnsns.2008.09.015

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Rameche Candane Somassoundirame

18 Years Experience

A seasoned mechanical engineer with an in-depth knowledge of computational methods in engineering (CFD, heat transfer, FEA, Structural and Optimization studies).

Education

Indian Institute of Technology Madras

Doctor of Philosophy, Department of Mechanical Engineering / November, 2006

Chennai

Pondicherry Engineering College

Master of Technology in Energy Technology, Mechanical Engineering / April, 2003

Pillaichavady

Pondicherry Engineering College

Bachelor of Technology, Mechanical Engineering / May, 2000

Pillaichavady

Experience

Johns Hopkins University

Associate Research Scientist / October, 2020 — October, 2021

TechnipFMC

Multi-Physics Specialist Engineer / January, 2014 — April, 2020

FMC Technologies (Norway)

Senior System Engineer / August, 2012 — June, 2014

Most Relevant Research Expertise

Numerical Analysis

Other Research Expertise (19)

About

Rameche Candane Somassoundirame is a highly educated and experienced mechanical engineer with a strong background in energy technology and multi-physics. He obtained his Doctor of Philosophy from the prestigious Indian Institute of Technology Madras in 2006, followed by a Master of Technology and Bachelor of Technology from Pondicherry Engineering College in 2003 and 2000 respectively. With over a decade of experience, Rameche has worked in various roles in different countries. He has held positions at renowned institutions such as Johns Hopkins University, TechnipFMC, FMC Technologies, Norsk Hydro ASA, WindSim, Universite de La Rochelle, and the University of New Brunswick. He has also worked as an IP Research Professional at GE India Technology Centre Pvt Ltd. Rameche's expertise lies in the fields of computational fluid dynamics (CFD), multi-physics simulations, and energy technology. He has published several papers in international journals and conferences and has received numerous awards and recognitions for his work. In addition to his technical skills, Rameche is known for his strong leadership and project management abilities. He has led and managed teams on various projects and has a track record of delivering successful results within tight deadlines. Rameche's passion for engineering and his continuous pursuit of knowledge make him a valuable asset in any organization.

Most Relevant Publications (3+)

16 total publications

Modeling particulate removal in plate-plate and wire-plate electrostatic precipitators

The International Journal of Multiphysics / Jun 01, 2014

Ramechecandane, S., Beghein, C., & Eswari, N. (2014). Modeling particulate removal in plate-plate and wire-plate electrostatic precipitators. The International Journal of Multiphysics, 8(2), 145–168. https://doi.org/10.1260/1750-9548.8.2.145

Numerical analysis of a divergent duct with high enthalpy transonic cross injection

The International Journal of Multiphysics / Mar 01, 2012

Ramechecandane, S., Balaji, C., & Venkateshan, S. (2012). Numerical analysis of a divergent duct with high enthalpy transonic cross injection. The International Journal of Multiphysics, 6(1), 17–28. https://doi.org/10.1260/1750-9548.6.1.17

Modelling Particulate Removal in Tubular Wet Electrostatic Precipitators Using a Modified Drift Flux Model

The International Journal of Multiphysics / Sep 01, 2011

Ramechecandane, S., & Beghein, C. (2011). Modelling Particulate Removal in Tubular Wet Electrostatic Precipitators Using a Modified Drift Flux Model. The International Journal of Multiphysics, 5(3), 243–266. https://doi.org/10.1260/1750-9548.5.3.243

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Mohammad Vahab

1 Years Experience

Lecturer at University of New South Wales (UNSW)

Education

Sharif University of Technology

Ph.D, Civil engineering / May, 2015

Tehran

Experience

TTW

Senior Soil Structure Analyst / June, 2023 — Present

Most Relevant Research Expertise

Numerical Analysis

Other Research Expertise (23)

Scientific Machine Learning

Computational Geomechanics

Fracture

Hydraulic Fracturing

Modeling and Simulation

And 18 more

About

I am an experienced researcher and chartered engineer in numerical simulation of geotechnal and structural engineering applications.

Most Relevant Publications (1+)

34 total publications

An enriched FEM technique for modeling hydraulically driven cohesive fracture propagation in impermeable media with frictional natural faults: Numerical and experimental investigations

International Journal for Numerical Methods in Engineering / Jun 01, 2015

Khoei, A. R., Hirmand, M., Vahab, M., & Bazargan, M. (2015). An enriched FEM technique for modeling hydraulically driven cohesive fracture propagation in impermeable media with frictional natural faults: Numerical and experimental investigations. International Journal for Numerical Methods in Engineering, 104(6), 439–468. Portico. https://doi.org/10.1002/nme.4944

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Example Numerical Analysis projects

How can companies collaborate more effectively with researchers, experts, and thought leaders to make progress on Numerical Analysis?

Optimizing Supply Chain Operations

A Numerical Analysis expert can develop mathematical models and algorithms to optimize supply chain operations, considering factors like demand forecasting, inventory management, and transportation logistics. This can lead to improved efficiency, reduced costs, and better customer satisfaction.

Improving Drug Formulation

Collaborating with a Numerical Analysis researcher can help pharmaceutical companies optimize drug formulation processes. By using mathematical modeling and simulation techniques, they can identify the optimal combination of ingredients, dosage, and delivery methods, leading to improved drug efficacy and reduced development time.

Enhancing Energy Efficiency

Numerical Analysis experts can assist energy companies in optimizing energy production and consumption processes. By developing mathematical models and algorithms, they can identify energy-saving opportunities, optimize resource allocation, and improve overall energy efficiency.

Predictive Maintenance in Manufacturing

Working with a Numerical Analysis researcher, manufacturing companies can develop predictive maintenance models. By analyzing historical data and using machine learning algorithms, they can predict equipment failures, schedule maintenance activities, and minimize downtime, resulting in cost savings and improved productivity.

Risk Assessment in Finance

Collaborating with a Numerical Analysis expert can help financial institutions assess and manage risks. By developing mathematical models and algorithms, they can analyze market trends, evaluate investment portfolios, and quantify risk exposures, enabling informed decision-making and risk mitigation strategies.