Materials Design and Innovation MS

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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 - Introduction to 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 549LEC - Intro Partl Diff Equatns Credits: 3
MTH 731LEC - Functional Analysis Credits: 3
STA 504LEC - Introduction to Applied Statistics II Credits: 3
STA 515LEC - Distributn Free Inference Credits: 3
STA 517LEC - Categorical Data Analysis Credits: 3
STA 522LEC - Intro Theoretical Stat 2 Credits: 3
STA 551LEC - Stochastic Processes 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

Instruction Method

In Person (100 percent of courses offered in person)

Full/Part Time Options

Full Time
Part Time

Credits Required

30 Hours

Time-To-Degree

1.5 Years

Application Fee

$85

Admission Test **

GRE

Program Contact

School of Engineering and Applied Sciences
Office of Graduate Education
415 Bonner Hall
Buffalo, NY 14260
Email: gradeng@buffalo.edu

SED Statement

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

Online programs/courses may require students to come to campus on occasion. Time-to-degree and number of credit hours may vary based on full/part time status, degree, track and/or certification option chosen. Time-to-degree is based on calendar year(s). Contact the department for details.

** At least one of the admissions tests are required for admission. Test and score requirements/exceptions vary by program. Contact the department for details.