Teaching

I was fortunate to have numerous influential and encouraging teachers growing up. I try to pay this forward by communicating a passion and enthusiasm for knowledge, not just knowledge itself. My teaching philosophy is built around the following pillars.
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Pillar 1: "What I cannot create, I do not understand." -- Richard Feynman.
Pillar 2: "Nothing great was ever achieved without enthusiasm." -- Ralph Waldo Emerson.
Pillar 3: "If you judge a fish by its ability to climb a tree..." -- (Attributed to) Albert Einstein.

While I like to avoid jargon, I like most ideas from active learning, constructivism, and Socratic teaching. I'm influenced​ by Scott Aaronson's teaching statement, in particular about modernizing the curriculum, raising the ceiling, and rewarding intellectual honesty.

Please read my teaching philosophy for a deeper explanation of these pillars, and see below for my teaching experience, sample evaluations, and other professional development.
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At the MSU Science Festival "Time for Quantum" booth, where we showed off superconducting magnets and programmed superconducting qubits.

Experience

Here's a short list of my teaching experience in reverse chronological order.
  • Teaching Assistant, CMSE 890: Quantum computing and quantum algorithms, Spring 2021. Course Instructor: Dr. Alexei Bazavov.
  • Chief Community Moderator,  Quantum machine learning, edX University of Toronto, 2019. Course instructor: Dr. Peter Wittek.
  • Teaching Assistant, CMSE 201: Introduction to computational modeling and data analysis, Michigan State University, Spring 2019. Course instructor: Dr. Devin Silvia.
  • Teaching Assistant, CMSE 202: Computational modeling: Tools and techniques, Michigan State University, Fall 2018. Course instructors: Dr. Devin Silvia and Dr. Pierson Guthrey.
  • Tutor and Academic Mentor, Academic Success Program and Summer Bridge Scholars Program, University of Michigan, Summer 2017.
  • Volunteer Tutor, Peace Neighborhood Center, Ann Arbor, MI, Fall 2016 and Spring 2017.
I also started and run the Quantum Information and Computation (QuIC) Seminar at MSU, which involves a fair deal of teaching.

Evaluations

The following anonymous quotes are from student feedback from CMSE 201 (Spring 2019) and CMSE 202 (Fall 2018). Quotes are verbatim responses from the open-ended question "What are the major strengths and/or weaknesses of your TA?"
  • "A major strength of Ryan’s is that he is particularly good at answering your question with the right kind of guidance and counter questioning to lead you to your answer. This means he doesn’t just tell you the direct answer of to what you ask him, this is a good thing, he guides you on how to answer the question on your own. This has done a lot of me, in particular. In the times that I have been to his office hours he has been very helpful and willing to give me his attention and effort, no matter how banal my question is."
  • "He’s really good at answering questions by asking questions and getting you to think about how the code works."
  • "I think Ryan was very helpful in guiding me to the right answer, without actually providing me with it. He pushed me the right way, while still allowing me to learn the concept."
  • "Super helpful and reasonable, goes out of way to help students learn and be successful in the class"
  • "Very helpful, many times explains things better than the professor, I really appreciate how he can explain things multiple ways until it is understood"
  • "He always wanted to make sure we understood the concept when helping rather than just give us the code, which helped a lot in the long run"
  • "Ryan has a lot of major strengths but the most import one is patience if you don't understand a concept the first time around. He encourages you to find the solution to the problem. Ryan has no weaknesses. He is a great TA!"
Additionally, the charts below are mid-semester evaluations from CMSE 202 in Fall 2018. From left to right, students were prompted with "Your TA explains material clearly," "Your TA answers questions well," and "Your TA's overall teaching is." In each chart, 1 = Not Very Effective and 5 = Very Effective.

Curriculum Development

I'm interested in more effective methods of teaching quantum information science, in particular quantum computing. Conventionally, quantum computing is taught in traditional-style lectures in physics or computer science departments. While this works for some students, I think a lot of work needs to be done in education research for quantum computing.

One way of transforming and modernizing the curriculum is quantum computer programming. We've come to a phase transition in the field where, for the first time, small quantum computers are able to be programmed over the cloud via quantum software platforms developed by industry and academia. Programming quantum algorithms is a definite way to grab students' attention, but even more importantly it has significant pedagogical power behind it. There's no better means of testing one's knowledge than trying to communicate it to a computer.

I think we could do well by incorporating modern programming and ideas of active learning into the classroom. To this end, I'm developing a course in this style for MSU's new CMSE department, which should be run for the first time in Spring 2020. One way I'm preparing for this is developing lecture notes for topics from the QuIC Seminar that I started and run at MSU. You can find lecture notes and Jupyter notebook tutorials for quantum algorithms on this page. I also developed materials for a week of quantum computing in an undergraduate programming course at MSU, which I implemented in Spring 2019 (pictured right), as a test-drive for the full course being developed. Please read the following article about this experience and check out the course materials I developed.I also created a lecture on the Deutsch-Jozsa algorithm for non-quantum information scientists  with all the great pedagogical tools I learned from Dr. Joyce Parker in ISE 870. See also the accompanying worksheet.
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Screenshot from a pre-class video lecture introducing qubits.
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Implementing the quantum computing unit in class. These "students" are actually the course instructor and TAs and gave consent to be photographed.

Professional Development

I actively make an effort to become a better educator. I am currently working towards a Certificate in College Teaching through the Graduate School at Michigan State University, and have taken/attended the following courses/workshops to gain experience and improve my teaching abilities. In 2019, I won the Future Academic Scholars in Teaching (FAST) Fellowship from Michigan State University to continue developing quantum computing curriculum at MSU.
Courses
  • ISE 870, Teaching College Science, Spring 2019, MSU.
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​​Workshops
  • Quantum computing bootcamp with Qiskit, Michigan State University, October 2019. Lead organizer.
  • Cirq Bootcamp, Google, Vencie, California, May 2019.
  • Schrodinger's Class, Institute for Quantum Computing, University of Waterloo, Waterloo, ON, Canada, October 2018.