Ahmet Cecen

Research Data Scientist

PhD

H1B

ExxonMobil Chemical
Baytown, Texas TX 77007 US

I have been doing research and heavily programming in the field of 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.


Toolsets

Machine Learning & Data Analysis
  • Image/Signal Processing
  • Convolutional Neural Networks
  • Natural Language Processing
MATLAB R Python
Web Development
  • Front End Design
  • Interactive Visualization
Plot.ly D3.js Three.js
Deployment & Automation
  • Software Prototyping
  • Hardware Prototyping
  • Lab/Workflow Automation
Batch Docker Azure
Database & Integration
  • API Engineering
  • Schema/Integration Design
HDF5 SQL MongoDB

Education

  • Georgia Institute of Technology

    Doctorate08/2013 - 08/2017

    Computational Science and Engineering

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

  • Drexel University

    Bachelors09/2009 - 08/2013

    Mechanical Engineering and Mechanics

    Graduation Project: Design and Manufacturing of an Automated Flow Capacitor Test Station


Work Experience

  • ExxonMobil

    Advanced Research Data Scientist

    09/2017 - Present

    • Data Scientist role working on Basic Chemicals and Polymers related problems.
  • Georgia Tech - MINED Group

    Graduate Research Assistant

    03/2010 - 08/2017

    • Design, implement and publish efficient machine learning and informatics tools for processing, statistical analysis and visualization of 2D/3D/4D image data.

Selected Publications

Material structure-property linkages using three-dimensional convolutional neural networks, Acta Materialia - 2018
Role of materials data science and informatics in accelerated materials innovation, MRS Bulletin - 2016
Versatile algorithms for the computation of 2-point spatial correlations in quantifying material structure, Integrating Materials and Manufacturing Innovation - 2016
Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters, Integrating Materials and Manufacturing Innovation - 2014

Community Impact

Google Scholar Citations
Citations 901
h-index 14
i10-index 14
MATLAB Central
ANSWERS151RANKof232,013
FILE EXCHANGE8693All-TimeDownloads
GitHub Language Usage

Awards

  • Air Force Research Lab in partnership with NIST and NSF

    Materials Science and Engineering Data Challenge - Runner Up05/2016

Projects

120 355

Collaborative blogging for the domain expert and the data scientist.

Javascript HTML/CSS MATLAB

Python XSLT Jekyll

Downloads: 10,000 (est.)

Polynomial regression with cross-validation on multidimensional data.

Machine learning tool for multiscale materials science.

Python Batch

Online demo written in R using Shiny framework.

R HTML/CSS

Computes 3D spatial statistics of gigantic datasets.