TEACHING PHILOSOPHY

In my teaching, I endeavor towards eliciting a sense of wonder. I nudge conversations towards asking the biggest questions there are, and propose that it is possible not just to ask these grand questions, but to attempt to answer them as well! Who are we? What are our minds? What are thoughts and where do they come from? I believe the journey to finding these answers is beguiling. A delicious intellectual feast!

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"Communicating about the great ideas of science is one of my favorite things. Some ideas are so delightful, they are like a delicious intellectual feast!"

Professor Gaurav Suri

Courses Taught

PSY 891: Introduction To Machine learning

Often taught in the Spring Semester

The goal of this course is to understand artificial neural networks and how they learn. These concepts are extremely relevant to machine learning and artificial intelligence, but from my perspective, they are also at-least as crucially relevant to psychology. Our course, while very relevant to computer science, data science, mathematics, and philosophy, is above all else, a course in (computational) psychology. 

PSY 494: Introduction to COGNiTIVE PSYCHOLOGY

Often taught in the Spring Semester

In this Area-1 Course, we explore the nature of perception, attention, memory, thinking, decision making, and emotion and emotion regulation. Our emphasis is on discussing empirical phenomena that have illuminated the field.

PSY 490: Introduction to DAtA SCIENCE FOR PSYCHOLOGY MAJORS

Often taught in the Fall Semester

In this Area-1 Course, we use the statistical programming language R to explore how to draw insights from data. The course covers data visualization, statistical inferences, programming, and an introduction to machine learning.

PSY 498: Introduction to COGNITIVE NEUROSCIENCE

Often taught in the Fall Semester

In this Area-1 Course, we discuss the prospects and potential limitations of studying cognitive psychological processes from the integrated perspectives of cognitive psychology, neuropsychology, neural network modeling, and neuroscience