Computational Informatics

Adaptive Robust Optimization Model

M.Eng. Project Website

Big data is arguably a major focus in the next round of the transformation of information technology in industry. With the recent development of the Internet of Things, wireless sensors, clouds, mobile devices, e-commerce, and smart manufacturing, the amount of data collected and stored has grown exponentially. Manufacturing process operation databases are massive because of the use of process operation and control computers and information systems. The diversity of process measurement technologies from conventional process sensors to images, videos, and indirect measurement technologies has compounded the variety, volume, and complexity of process big data. The Computer Informatics focus path lies at the interface between process engineering and data science (or informatics). It teaches students data analytics skills to derive knowledge and information from mass process data, as well as decision-support tools for solving real operation problems in the era of big data. Two courses (“CHEME 6800: Computational Optimization” and “CHEME 6880: Industrial Big Data Analytics and Machine Learning”) are specially designed and developed for this focus path and train students with relevant methodologies and skills.

Decision Making Under Multi-Scale Uncertainty

Computational Informatics Curriculum

Fall Course

Course NameCredit

Spring Course

Course NameCredit
CHEME 5500Software Carpentry or equivalent programming proficiency course3 (core course requirement)2-3CHEME 6880        Industrial Big Data Analytics and Machine Learning2
CHEME 5740Probability, statistics, and data analysis for physical sciences (core course requirement) or equivalent52-3 Required CI focus area elective73
CHEME 6800      Computational Optimization or equivalent4 (core course requirement)3-4 Required CI focus area elective73
 Required CI Focus Area Elective73 Free Elective83
Business Elective Can be taken fall or spring2-3Business ElectiveCan be taken fall or spring2-3
CHEME 5650CI-related M.Eng. Project (can be taken spring semester)3-6CHEME 5650CI-related M.Eng. Project (can be taken spring semester)3-6
 Total13-19 Total14-18

The focus in Computational Informatics is designed to train students in data science skills and data analytics in the context of problems of interest to chemical engineering.1,2 This sample curriculum satisfies the chemical engineering M.Eng. requirements and the specialization requirements. 

Computational Informatics

1. Minimum of 30 total credits

2. At least 12 of these credits must be earned in courses listed as chemical engineering courses. This does not include credits earned for the M. Eng. project (more than three credits).

3. Approved courses to satisfy the programming proficiency requirement are:

  • Any CS course (but this will not count towards the 30-credit M. Eng. requirement unless the course is at the 4000-or above level)
  • Demonstration of programming proficiency from a prior course. Note that prior courses based on Matlab will not satisfy this requirement. Courses in modern languages (e.g., Python or Julia) will be allowed, as will courses in traditional languages such as Java, C, C++, etc.

4. Approved optimization courses include:

  • SYSEN 6800 Computational Optimization (fall) 4 credits
  • ORIE 5300 Optimization I (fall) 4 credits
  • ORIE 5310 Optimization II (spring) 4 credits

5. Approved probability and statistics courses include:

  • BIOMG 8340 Quantitative Biology for Molecular Biology & Genetics (spring) 2 credits
  • BTRY 6010 Statistical Methods I (fall) 4 credits
  • CEE 3200 Engineering Computation (spring) 4 credits/or ENGRD 2700 Basic Engineering Probability and Statistics (fall/spring) 3 credits (cannot be counted towards the 30-credit M.Eng. requirement)

6. Approved machine learning courses include:

  • CS 4780 Machine Learning for Intelligent Systems (spring) 4 credits
  • CS 4786 Machine Learning for Data Science (fall) 4 credits
  • ORIE 4740 Statistical Data Mining (spring) 4 credits
  • STSC 4740 Data Mining and Machine Learning (fall) 4 credits
  • CHEME 55XX Applied Machine Learning and Process Data Analytics [not offered AY17-18]

7. Required CI Focus Area Electives: Students may choose a topic within Computational Informatics (CI) on which to focus their three required electives, or they may choose to take these three electives broadly among categories.  These focus sub-specialization topics are:

  • Computational Biology, Chemistry, or Bioinformatics
  • Process Systems and Data Analytics (i.e., optimization and statistical/machine learning for design, control and operations of chemical/energy processes)
  • Physical Analytics (i.e., high performance computing applications in science and engineering)

  Approved Computational Biology, Chemistry, and Bioinformatics Courses:

  • BEE 4600 Deterministic & Stochastic Modeling in Biological Engineering () 3 credits
  • BIOMG 3340 Computer Graphics and Molecular Biology (fall) 1 credit
  • BIOMG 6300 Math
  • MAE 6210 Adv. Math. Modeling-Biological Fluid Dynamics (spring) 3 credits
  • CHEME 7770 Adv. Biomolecular Engineering (spring) 3 credits
  • BTRY 6840 Computational Genetics and Genomics (fall) 4 credits
  • CHEME 5440 Systems Biology in Biotechnology and Medicine (spring) 3 credits
  • CHEME 7740/CHEME 4810/CHEME 5810 Computational Methods in Chemistry (spring) 4 credits

  Approved Process Systems and Data Analytics:

  • CEE 5970 Risk Analysis and Management (spring) 3 credits
  • CS 6780 Applied Machine Learning (spring) 4 credits
  • CHEME 4700 Process Control Strategies (spring) 3 credits
  • MATH 7740 Statistical Learning Theory (spring) 4 credits
  • ORIE 4740 Statistical Data Mining (spring) 4 credits
  • ORIE 4741 Learning with Big Messy Data (fall) 4 credits
  • ORIE 6127 Computational Issues in Large Scale Data-Driven Models (fall) 3 credits
  • ORIE 6300 Mathematical Programming I (fall) 4 credits
  • ORIE 6741 Bayesian Machine Learning (fall) 4 credits
  • STSCI 4780 Bayesian Data Analysis: Principles and Practice (spring) 4 credits
  • SYSEN 5100 Model Based Systems Engineering (fall) 4 credits
  • SYSEN 5200 Systems Analysis Behavior and Optimization (spring) 3 credits

  Approved Physical Analytics Courses:

  • CEE 6300 Computational Sim. of Flow and Transport in the Environment (spring) 3 credits
  • CHEME 7740/CHEM 4810 Principles of Molecular Simulation (spring) 3 credits
  • MAE 5230 Intermediate Fluid Dynamics with CFD (spring) 4 credits
  • MAE 5700 Finite Element Analysis for Mechanical & Aero. Design (fall) 4 credits
  • MAE 7150 Atomistic modeling of materials (fall) 4 credits
  • MAE 7840 Bayesian Scientific Computing (spring) 4 credits
  • MSE 5715 Engr. Quantum Mechanics (spring) 3 credits
  • MSE 5720 Computational Materials Science (fall) 3 credits
  • MSE 6400 Computational Combustion (spring) 4 credits

8. Free elective(s) will be a requirement if the 30-credit criterion is not met.  It can be fulfilled from any other relevant area with the consent of the M.Eng. Director.

9. Suitable business requirement courses include:

  • AEM 4120 Comp. Methods for Management and Econ. (fall) 3 credits
  • AEM 4190 Strategic Thinking (spring) 3 credits
  • AEM 4380 Entrepreneurial Strategy for Technology Ventures (spring) 2 credits
  • AEM 6061 Risk, Simulation, and Monte Carlo Methods (spring) 4 credits
  • CHEME 5720 Managing New Business Development (fall) 3 credits
  • CEE 5900 Project Management (spring) 3 credits
  • Any other course with the approval of the M.Eng. Director