Materials Design and Innovation MS

The Materials Design and Innovation MS program aims to equip individuals with comprehensive knowledge and expertise in both materials science and data science. By fostering an interdisciplinary approach, students gain a holistic perspective and acquire essential skills and tools to comprehend and apply computational techniques for modeling, simulation, and informatics. In parallel, the program emphasizes experimental-driven science to ensure students possess the capability to contribute to and expedite the progress of materials discovery and engineering design.

Required Core (24 credits)

MDI 501LEC - Introduction to Materials Design and Informatics Credits: 3
MDI 502LEC - Quant Structure-Property Relationship in Materials Credits: 3
MDI 503LEC - Thermodynamics and Molecular Structure of Materials Credits: 3
MDI 504LEC - Multivariate Statistics and Materials Informatics Credits: 3
MDI 505LEC - Computational Materials Chemistry and Physics Credits: 3
MDI 506LEC - Kinetics, Microstructure, and Defects Credits: 3
MDI 507LEC - Quantitative Methods in Materials Characterization Credits: 3
MDI 508LEC - Experimental Design for Materials Development Credits: 3

Electives & Culminating Experience (6 credits)

Electives (0-6 credits)

Number of elective credits may vary depending on choice made to complete the culminating experience. Be sure to complete enough elective credit to reach 30 minimum total credit hours.

Select from:

CE 530LEC - Molecular Modeling Credits: 3
CSE 555LEC - Intro Pattern Recognition Credits: 3
CSE 674LEC - Advanced Machine Learning Credits: 3
IE 576LEC - Applied Stochastic Proc Credits: 3
IE 671LEC - Nonlinear Programming Credits: 3
IE 675LEC - Game Theory Credits: 3
MAE 529LEC - Finite Element Structure Analysis Credits: 3
MAE 540LEC - Computational Fluid Dynamics 2 Credits: 3
MAE 541LEC - Topics Finite Elements Credits: 3
MTH 511LEC - Probability Theory Credits: 3
MTH 731LEC - Functional Analysis Credits: 3
STA 504LEC - Introduction to Applied Statistics II Credits: 3
STA 517LEC - Categorical Data Analysis Credits: 3
STA 522LEC - Intro Theoretical Stat 2 Credits: 3
STA 561LEC - Longitudinal Data Analysis Credits: 3
STA 581LEC - Multivar Data Analysis Credits: 3

Culminating Experience: Exam, Project or Thesis (0-6 credits)

MDI 700TUT - Project Guidance Credits: 1 - 6
MDI 701TUT - Thesis Guidance Credits: 1 - 6

Total Credit Hours: 30

3.0 Degree GPA required

Program Learning Outcomes (PLOs)

1) Students will have disciplinary knowledge in the areas of materials science and data science.

2) Students will have the awareness and ability to query and work with databases in materials science.

3) Students will acquire operational skills in statistics and data mining methods as applied to materials science.

4) Students will have the ability to use computational methods in the analysis of experimental and/or simulation data.

5) Students will develop relevant scientific literacy and have the ability to communicate in written and verbal formats.

SED Statement

This program is officially registered with the New York State Education Department (SED).