MSE598DM/CSE598DM/ME598DM :: MatSE Illinois :: University of Illinois at Urbana-Champaign


Online discussion forum

This class uses the CampusWire System for announcements, updates, and all communication. Please visit this page to access it.

Excused Absences

Excused absences may be requested by filling out the Excused Absences form. For more information, please read the course syllabus.


Recordings will be posted under this link.

Week Tuesday Thursday Topic Instructor
1 1/18 1/20 Introduction, python basics, python notebook André Schleife
2 1/25 1/27 Python for data science I, II Luke Olson
3 2/1 2/3 Visualization Hands-on activity Matthew Turk
4 2/8 2/10 Intro to material science for non-experts I, II André Schleife
5 2/15 2/17 Generalized linear models (PDF), Bayesian models, Horseshoe Crab Data Bo Li
6 2/22 2/24 Band alignment I, II André Schleife
7 3/1 3/3 Defect ID using statistical learning methods Notes, Article Harley Johnson
8 3/8 3/10 Image processing for TEM: Handout, RR_2143 STEM 4.6 Mx HAADF_25.1pA_1024px_2.0us_Raw_Stack_16bit_TopRightQuarter.tif, RR_2143 STEM 4.6 Mx HAADF_25.1pA_1024px_2.0us_Raw_Stack_16bit.tif Pinshane Huang
9 3/15 3/17 Spring Break
10 3/22 3/24 4CeeD Handout I, 4CeeD Handout II, 4CeeD Notebook, 4CeeD Notebook PDF (Bracelet, Senselet) Klara Nahrstedt
11 3/29 3/31 Senselet Guide, Backend, Slides Klara Nahrstedt
12 4/5 4/7 Model development from first principles, The sometimes surprising behavior of magnetic spins on a complex surface: Visualizing Orbitals and managing data Lucas Wagner, Barbara Jones (IBM)
13 4/12 4/14 Uncertainty quantification Slides/Book Chapter/Exercise Dallas Trinkle
14 4/19 4/21 Accelerated discovery in chemistry: representation learning and recommender system (Paper I, Paper II, Slides), Semi-autonomous) experimental systems: Paper/Slides 1/Slides II Dmitry Zubarev (IBM), Elif Ertekin
15 4/26 4/28 Graphene ResQ (T) Neural network/(H)Bayesian search, Slides, Gaussian Process Slides Elif Ertekin
16 5/3 Q and A Johnson, Schleife, Huang, Turk, Li, and others

Course Description

This course is a multidisciplinary introduction to topics at the intersection of materials and data science. Corresponding to this, it brings in speakers and activities from 6 different departments (computer science, information science, statistics, physics, materials science, and mechanical engineering) to provide their own perspectives on this subject.


Course Grading


Each unit, noted below in the schedule, will involve hands-on sessions. You will turn in a PDF report on each of your hands on activities. The report should include a narrative about what you are doing and why. Learning how to do this is an important part of doing science. Please consult the course syllabus for details.