Teaching

I love teaching. Part of this love stems from my deep appreciation of some of the best and most dedicated teachers that I have had the good fortune of being taught by.

There are two core components in my teaching philosophy:

1. teaching students to learn how to learn; and

2. encapsulating my lecture materials within active, discussion-oriented class meetings to maximize student recall.

The latter, especially, flows from my observation that by tying my lectures to students’ voices and ideas I build trust with them, which further facilitates learning and interactivity in class.

My teaching practice, especially for undergraduates, is inspired by the following practical pedagogical texts:

• Small Teaching by James Lang (2021; Wiley); and

• Uncommon Sense Teaching by Oakley, Rogowsky, & Sejnowski (2021; Penguin Random House).

Driven by my desire to emulate some of my most effective teachers, in 2024 I participated in the New York Academy of Sciences’ (NYAS) Scientist-in-Residence program. As part of this program, I partnered with a middleschool teacher in Brooklyn, NY, to help 7th graders get a basic grasp of how AI and neural networks relate to the nervous system of mammals. Together with the teacher, we helped students bring together their understanding of human anatomy and the nervous system to design a ‘robotic’ arm made with cardboard tubes and plastic straws. We followed this tutorial.

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Courses Taught

Here’s a list of courses that I teach, or have taught and served as a teaching assistant for.

• IS 467: Ethics and Policy for Data Science (Spring and Fall 2026 at UIUC)

  This large, required course for UIUC’s ‘X + DS’ program is meant to provide students a balance of depth and breadth of knowledge of the most salient ethical issues raised by data-driven systems like modern AI systems. Specifically, the learning objectives I designed for this course are for students to be able to:

  1. understand what is meant by data, data science, and datafication;
  2. identify sources of bias, uncertainty, and opacity in data-driven decision-making;
  3. apply ethical frameworks to analyze the impact of data-driven systems;
  4. identify multiple stakeholder perspectives on the societal implications of data science;
  5. examine the role of human values in shaping data science and AI; and,
  6. examine issues of privacy, responsibility, trustworthiness, and policy in relation to data science and AI.

• FIS 494: AI & Society: Ethics and Impact (Spring 2025 at Arizona State University).

  This course explored how to use artificial intelligence (AI) for socially responsible innovation, taking into consideration its societal implications, the needs of special populations, health-related applications, and adherence to ethical principles. With rising AI use and technical proficiency, the aim of this course it to equip students with the opportunity to further knowledge related to responsible innovation, principled innovation, and the use of AI technologies.

• I 303: Ethical Foundations for Informatics. (Spring 2022 at UT-Austin)

  This course, which was targeted towards freshmen and sophomores in the UT iSchool’s then new Informatics major, focused on providing students with a very broad overview of ethical theories and principles as applicable to informatics and information science. This broad perspective included ethical theories from both Western and Eastern traditions to help students appreciate how different cultural perspectives could enrich our vision for designing ethically sound information systems and processes.

  Student testimonial:

  “Mr. Verma was very understanding of students’ situations and was able to engage the class with discussion about the material. Overall, I enjoyed the course due to the interactions we had as a class with Mr. Verma going above and beyond as the instructor.”

• I 301: Introduction to Informatics. (Fall 2021 at UT-Austin)

  This course was a high-level overview of what the discipline of informatics is, and the various kinds of problems that informatics professionals (including information scientists) study. This course was also intended to be for freshmen and sophomores to help them get an overview of the various concentrations they could pick in later stages of their undergraduate study.

  Student testimonials:

  “This instructor is very patient and takes the time to listen to each student individually and makes each student feel heard.”

  “The instructor showed great dedication to the class and the students. He shared incredibly memorable and enriching insight that I feel I will take with me and really benefit from. The course felt important for both my college career in Informatics and my future as a whole.”

Courses Where I Served as a Teaching Assistant in Grad School

• INF 388L: Professional Experience and Project. (Summer 2018)

• INF 385L: Access and Reference in Libraries and Archives. (Summer 2018)