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USD 125 /hr

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Hire Dr. David M.

United States

USD 125 /hr

Data Scientist Designing and Improving Digital Twins with Data-Driven Methods

Profile Summary

Leader in physics-informed data-driven modeling using hybrid methods. Expert in Bayesian uncertainty quantification of system-level models. Domain expertise in electrochemistry and carbon capture. Projects delivered for Carrier Corporation, NASA, Lunexus (space startup) and the U.S. Department of Energy's Carbon Capture Simulation Initiative.

Subject Matter Expertise

• ★ Electrochemistry
• ★ Chemical Engineering
• ★ Bayesian Statistics
• ☆ Catalysis
• ☆ Mathematical Chemistry
• ☆ Surface Science
• ☆ Materials Science & Engineering
• ☆ Nanotechnology & Nanomaterials
• ☆ Inorganic Chemistry
• ☆ Physical Chemistry
• ☆ Surface Chemistry
• ☆ Metallurgy
• ☆ Ceramic Engineering
• ☆ Corrosion
• ☆ Fiber & Textile Engineering
• ☆ Carbon Materials
• ☆ Powder Metallurgy
• ☆ Refractory Materials
• ☆ Composite Materials
• ☆ Solid State Sciences
• ☆ Condensed Matter Physics
• ☆ Solid-State Chemistry
• ☆ Theoretical Chemistry
• ☆ Computational Chemistry
• ☆ Physical Organic Chemistry
• ☆ Engineering Education
• ☆ Engineering Physics
• ☆ Computer Science
• ☆ Parallel Computing
• ☆ Safety & Reliability Engineering
• ☆ Engineering Thermodynamics/Thermophysical Engineering
• ☆ Systems Engineering
• ☆ Chemical Manufacturing
• ☆ Process Chemistry
• ☆ Reactor Design
• ☆ Industrial Engineering
• ☆ Manufacturing
• ☆ Continuous Process Manufacturing
• ☆ Process Control
• ☆ Data Analysis
• ☆ Computer Simulation/Computational Modeling
• ☆ Algorithm Design
• ☆ Predictive Modeling & Analytics
• ☆ Data Processing
• ☆ Data Exploration
• ☆ Data Manipulation
• ☆ Machine Learning
• ☆ Neural Networks
• ☆ Artificial Intelligence
• ☆ Deep Learning
• ☆ Statistics
• ☆ Statistical Theory
• ☆ Applied Statistics
• ☆ Regression Analysis
• ☆ Multivariate Analysis
• ☆ Nonlinear Regression
• ☆ Correlation Analysis
• ☆ Statistical Computing
• ☆ Mathematical Statistics
• ☆ Survey Design
• ☆ Study Design
• ☆ Statistical Review
• ☆ Statistical Mechanics
• ☆ Thermodynamics
• ☆ Computational Physics
• ☆ Applied Mathematics
• ☆ Mathematical Models
• ☆ Mathematical Optimization
• ☆ Mathematics Education
• ☆ Mathematical Physics
• ☆ Differential Equations
• ☆ Chaos Theory
• ☆ Computational Mathematics
• ☆ Stochastic Processes
• ☆ Decision Science
• ☆ Production & Operations Management
• ☆ Python
• ☆ Matlab
• ☆ Excel
• ☆ Tensorflow
• ☆ LaTex
• ☆ R
• ☆ Scikit-learn

Services

• Writing
• Research
• Consulting
• Data & AI
• Product Development

Writing Technical Writing

Research Feasibility Study, Technology Scouting, Gray Literature Search, Scientific and Technical Research, Systematic Literature Review

Consulting Operations Consulting, Scientific and Technical Consulting, Manufacturing Consulting

Data & AI Predictive Modeling, Statistical Analysis, Algorithm Design-Non ML, Algorithm Design-ML, Data Processing, Data Insights

Product Development Formulation, Product Evaluation, Product Validation, Manufacturing, Prototyping, Reverse Engineering, Device Fabrication

Work Experience

Founder and Principal Developer

Harappa Modeling LLC

March 2024 - Present

Assistant Professor / Associate Professor

West Virginia University

August 2012 - Present

ORISE Postdoctoral Fellow

National Energy Technology Laboratory

July 2010 - August 2012

NSF Postdoctoral Fellow

Max Planck Institute for Solid State Research

January 2008 - July 2010

Education

Materials Science and Engineering

Georgia Institute of Technology

January 2005 - December 2007

Materials Science and Engineering

Georgia Institute of Technology

May 2002 - December 2004

Certifications

• Certification details not provided.

Publications

JOURNAL ARTICLE

San Dinh, Claudemi A. Nascimento, David S. Mebane, Fernando V. Lima (2026). A Framework for Implementation of Dynamic Discrepancy Reduced-Order Modeling in Advanced Process Control . Industrial & Engineering Chemistry Research.

Claudemi A. Nascimento, San Dinh, David S. Mebane, Fernando V. Lima (2025). Embedding Dynamic Microkinetic Modeling Information Into Reduced‐Order Models Using Gaussian Processes . AIChE Journal.

David S. Mebane, Roksana Jackowska, Michael W. Fouts, Ferran Brosa Planella, Emma Kendrick, Mohit R. Mehta, John W. Lawson (2025). Initial Proof-of-Concept for an Accelerated Galvanostatic Intermittent Titration Technique via Embedded Machine Learning . Journal of The Electrochemical Society.

David Mebane, Karen Swider-Lyons, Liang Du, Shawn Litster, Dustan Skidmore (2024). Machine Learning in Fuel Cell Stacks, Systems and Modeling . ECS Meeting Abstracts.

Kyle Hayes, Yu Zhou, Michael W. Fouts, Ali Baheri, David S. Mebane (2024). Forward variable selection enables fast and accurate dynamic system identification with Karhunen-Loève decomposed Gaussian processes . PLOS ONE.

David Mebane, Hasti Vahidi, Alejandro Mejia, Shengquan Xuan, Angel Cassiadoro, Abdnego Abdi, William J Bowman (2023). Which Interfaces Matter Most? Variability in Grain Boundary Defect Chemistry and Conductivity in a Concentrated Solid Electrolyte . ECS Meeting Abstracts.

David S. Mebane (2023). Bayesian calibration of electrical conductivity relaxation and isotope exchange-secondary ion mass spectrometry experiments . Journal of Electroceramics.

Pedram Tavadze, Reese Boucher, Guillermo Avendaño-Franco, Keenan X. Kocan, Sobhit Singh, Viviana Dovale-Farelo, Wilfredo Ibarra-Hernández, Matthew B. Johnson, David S. Mebane, Aldo H. Romero (2021). Exploring DFT+U parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling . npj Computational Materials.

Xiaorui Tong, William J. Bowman, Alejandro Mejia-Giraldo, Peter A. Crozier, David S. Mebane(2020). New Data-Driven Interacting-Defect Model Describing Nanoscopic Grain Boundary Compositions in Ceramics . The Journal of Physical Chemistry C. 124. (43). p. 23619--23625. American Chemical Society ({ACS})

Xiaorui Tong, David S. Mebane, Roger A. De Souza (2020). Analyzing the grain‐boundary resistance of oxide‐ion conducting electrolytes: Poisson‐Cahn vs Poisson‐Boltzmann theories . Journal of the American Ceramic Society.

Kuijun Li, Priyadarshi Mahapatra, K. Sham Bhat, David C. Miller, David S. Mebane(2017). Multi-scale modeling of an amine sorbent fluidized bed adsorber with dynamic discrepancy reduced modeling . Reaction Chemistry & Engineering. 2. (4). p. 550--560. Royal Society of Chemistry ({RSC})

Giuseppe F. Brunello, William K. Epting, Juwana de Silva, Paul A. Salvador, Shawn Litster, Harry O. Finklea, Yueh-Lin Lee, Kirk R. Gerdes, David S. Mebane(2017). Quantitative interpretation of impedance spectroscopy data on porous LSM electrodes using X-ray computed tomography and Bayesian model-based analysis . Physical Chemistry Chemical Physics. 19. (37). p. 25334--25345. Royal Society of Chemistry ({RSC})

PREPRINT

David Mebane, xiaorui tong, William Bowman, Alejandro Mejia-Giraldo, Peter Crozier (2019). A New Data-Driven Interacting-Defect Model Describing Nanoscopic Grain Boundary Compositions in Ceramics .

REPORT

David Mebane, Adekola Lawal, Pieter Schmal, Alfredo Ramos, Alejandro Cano, Debangsu Bhattacharyya, Nikolaos Sahinidis, Ananya Chowdhury, Xiaohui Liu, Stefan Bellinghausen (2017). Evaluation and demonstration of commercialization potential of CCSI tools within gPROMS advanced simulation platform .