Teaching and Outreach
Students in the S4E lab learn to compute, reason about materials, and check numerical results against physics before trusting them. The courses below are taught at Johns Hopkins University. Workshops, outreach programs, and research mentoring bring these approaches to students and researchers outside the classroom.
Courses
Students write scientific code in Python and use it to model materials. The course begins with the UNIX shell, Git, LaTeX, and Make, then develops numerical methods from their mathematical foundations. Topics include integration of equations of motion, relaxation methods for fields, eigenvalue problems, random numbers, and Monte Carlo sampling. Students apply these methods to density functional theory, high-throughput materials databases, molecular dynamics, and machine learning. The final unit introduces autonomous experimentation using the LEGOLAS low-cost autonomous laboratory kit, with results from each experiment used to select the next.
There are no written examinations. Each of the seven assignments consists of a working program and a report typeset in LaTeX and maintained in a GitHub repository. The semester ends with a group project presented in class. Students implement the numerical methods themselves rather than relying on libraries that already solve the problem, and they check their results against analytic limits, conservation laws, or convergence tests. The density functional theory, high-throughput modeling, and molecular dynamics assignments run on the university's research computing cluster. The course enrolls 15 to 17 students each fall, mostly graduate students in materials science and related departments, and often has a waiting list. Across course evaluations, students rate its intellectual challenge 4.6 out of 5.
An introductory programming course for engineering students covering algorithms, control structures, functions, files, testing, and debugging, with problems drawn from engineering and science. Prof. Oses teaches two sections each spring, with 20 to 30 students in total.
Undergraduate and graduate students carry out research in the group for course credit during the academic year and summer. Information on joining the group is on the Jobs page.
From course evaluations and student surveys
“This course is definitely heavy on coding, but even heavier on the satisfying feeling gained by understanding a topic on a deeper level.”
Course evaluation, Materials Modeling, fall 2025
“The homeworks are fantastic. I learned so much in this class by doing the homeworks. They were very challenging and pushed me out of my comfort zone but allowed me to learn so many new skills and forced me to think.”
Course evaluation, Materials Modeling, fall 2024
“He's helpful, but will never simply give a student the answer; he'll force them to think critically about the error and manually debug it themselves, since that's a more valuable skill than any one technique. He's patient and adept at explaining new concepts.”
Enrolled Student Survey, Johns Hopkins University, 2023
“Dr. Oses clearly puts a great deal of time into his students and provides a great deal of personalized feedback.”
Course evaluation, Gateway Computing: Python, spring 2025
“Challenging yet immensely supportive.”
Enrolled Student Survey, Johns Hopkins University, 2025
Guest lectures
- 2026Guest lecture on applications of thermodynamics to materials, EN.510.603 Phase Transformations (T. Curk), Johns Hopkins University
- 2023Guest lecture, EN.510.422/622 Micro and Nano Structured Materials and Devices (A. S. Hall), Johns Hopkins University
Workshops and schools
The group organizes hands-on workshops at Johns Hopkins University on data-driven materials modeling, quantum computing, and translating energy research into applications. Recordings and notebooks from past events are on the Workshops page. Prof. Oses has also co-organized schools on high-throughput computational materials science at Carnegie Mellon University, the University of Pennsylvania, Texas A&M University, TU Dresden, and the University of Rwanda, and has presented at the University of Maryland and NIST Machine Learning for Materials Research bootcamps since 2018.
Outreach and mentoring
Research in the group. Since 2022, the group has hosted nine undergraduate researchers, six high school researchers, five visiting students, and four master's students, in addition to its PhD students, postdoctoral associates, and research scientists. Undergraduates join through Johns Hopkins research credit and programs such as Research on Sustainable Energy Technology and Systems (ROSETAS), a National Science Foundation Research Experience for Undergraduates site hosted by ROSEI, and four undergraduate researchers are co-authors on group publications.
High school research. Since 2023, the group has mentored high school students through programs such as the Ingenuity Project Research Practicum at Baltimore Polytechnic Institute and the Johns Hopkins Engineering Innovation Research Program. Students build and program the LEGOLAS low-cost autonomous laboratory, a LEGO-based system that runs experiments and uses machine learning to select the next one, as well as work on other autonomous-laboratory projects. Each cohort helps train the students who follow, and the same kit, developed at the University of Maryland and NIST, is used in the final unit of the Materials Modeling course.
Society of Hispanic Professional Engineers. Prof. Oses is faculty mentor for the Johns Hopkins University chapter.