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Ahmet Cecen
Research Data Scientist

Contact

ExxonMobil Chemical
Baytown, Texas TX 77007 US
(267) 586-4505

About

I have been doing research and heavily programming in the fields of computational materials science and materials informatics for the past 11 years. My current research focus is on leveraging statistical analysis and machine learning tools to solve big data analytics problems in materials science. My specific focus is on the extraction of meaningful structural information from scientific images obtained through micorscopy, tomography and spectroscopy techniques.


Work Experience

  • Present 09/2017

    ExxonMobil

    Advanced Research Data Scientist
  • 08/2017 03/2010

    Georgia Tech - MINED Group

    Graduate Research Assistant
  • 08/2013 03/2010

    Drexel University - ECSL Group

    Undergraduate Research Assistant

Education

  • 08/2017 08/2013

    Georgia Institute of Technology

    Doctorate

    Computational Science and Engineering

    Thesis: Calculation, Utilization, and Inference of Spatial Statistics in Practical Spatio-Temporal Data

  • 08/2013 09/2009

    Drexel University

    Bachelors

    Mechanical Engineering and Mechanics

    Graduation Project: Automated Electrochemical Flow Capacitor Test Station

Awards

  • 05/2016

    Air Force Research Lab in partnership with NIST and NSF

    Materials Science and Engineering Data Challenge - Runner Up

Toolsets

Mach. Learn. & Data Anlys.
Web Development
Deployment & Automation
Database Systems

Secondary Interests

Electronics
Hardware Prototyping Gadgetry Control Systems
3D Visualization
Paraview Blender ThreeJs

Languages

  • EnglishActive | Fluent
  • TurkishActive | Fluent
  • FrenchPassive | Intermediate
  • JapanesePassive | Beginner

Community Impact

Google Scholar Citations public

AllSince 2016
Citations901774
h-index1414
i10-index1414

GitHub Contribution Profile public

public








Journal Articles

1. A new framework for rotationally invariant two-point spatial correlations in microstructure datasets
2. Material structure-property linkages using three-dimensional convolutional neural networks
3. Calculation, utilization, and inference of spatial statistics in practical spatio-temporal data
4. Analytics on large microstructure datasets using two-point spatial correlations: Coarsening of dendritic structures
5. Process-structure linkages using a data science approach: application to simulated additive manufacturing data
6. Development of High Throughput Assays for Establishing Process-Structure-Property Linkages in Multiphase Polycrystalline Metals: Application to Dual-Phase Steels
7. Role of materials data science and informatics in accelerated materials innovation
8. Versatile algorithms for the computation of 2-point spatial correlations in quantifying material structure
9. Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters
10. Structure–property linkages using a data science approach: Application to a non-metallic inclusion/steel composite system
11. A data-driven approach to establishing microstructure–property relationships in porous transport layers of polymer electrolyte fuel cells
12. Resolving macro-and micro-porous layer interaction in polymer electrolyte fuel cells using focused ion beam and X-ray computed tomography
13. 3-D microstructure analysis of fuel cell materials: spatial distributions of tortuosity, void size and diffusivity
14. Selection of representative volume elements for pore-scale analysis of transport in fuel cell materials

Conference Abstracts & Presentations

1. A Generalized Statistical Microstructure Generation Framework
2. Formulation and calculation of rotationally invariant spatial correlations for microstructure datasets
3. Analytics on Large Microstructure Datasets Using Two Point Statistics: Application to Coarsening Dendritic Solid-Liquid Mixtures
4. Extraction of Process-Structure Linkages from Simulated Additive Manufacturing Microstructures Using a Data Science Approach
5. Analytics on Large Microstructure Datasets Using 2-pt Statistics: Application to Al-Cu Solidification Process
6. Data Science Enabled Fusion of Microstructure Images with Strain Fields Measured by DIC
7. Introduction to Materials Informatics with Open Source Tools
8. Microstructure Informatics for Mining Structure-Property-Processing Linkages from Large Datasets
9. Application of Statistical and Machine Learning Techniques for Correlating Properties to Composition and Manufacturing Processes of Steels
10. Multiscale Model for Non-Metallic Inclusions/Steel Composite System Using Data Science Enabled Structure-Property Linkages
11. Microstructure-driven analysis of two-phase transport in diffusion media of PEFCs”
12. Advanced microstructural analysis tools to quantify two-phase transport in gas diffusion layers of polymer electrolyte fuel cells
13. Characterization of two-phase flow properties of dual-layer PEFC gas diffusion electrodes via microstructure analysis and pore-scale modeling
14. Microstructure and two-phase flow analysis of gas diffusion electrodes of polymer electrolyte fuel cells
15. A Representative Volume Element Approach for Pore-Scale Modeling of Fuel Cell Materials
16. Microstructure Analysis Tools for Quantification of Key Structural Properties of Fuel Cell Materials
17. A new approach for microstructure characterization of porous fuel cell materials
18. Focused ion beam tomography of diffusion media for fuel cells
19. A new approach for quantification of morphology-property linkages In fuel cell materials: a case study for PEM fuel cells
20. Determination of Key Structural Parameters of Fuel Cell Porous Media through Microstructure Quantification
21. Microstructure characterization of micro-porous layer in PEM fuel cells