I love teaching. I went to graduate school to earn a PhD so that I can teach a wide variety of courses on the brain, cognition and computation at the college level. I am particularly interested in developing highly interdisciplinary courses which span neuroscience, psychology and computer science which will be accessible to students from a variety of backgrounds. I am also interested in developing clear and well-written textbooks for these interdisciplinary classes, and developing computer exercises and psychophysical experiments for the classroom.
Teaching Experience
Case Western
I was a teaching assitant for a graduate-level course taught by my postdoctoral advisor Mike Lewicki on Computational Perception. The course discusses information processing in the auditory and visual systems at the level of the current literature, and emphasizes the applicability of neuroscience to problems in artificial intelligence and machine perception. In addition to teaching one meeting of the class, I graded homeworks and projects and provided assistance to students during office hours. The class was comprised of about 12 graduate students and senior undergraduates from computer science, electrical engineering and related fields.
Johns Hopkins
This course is the basic Neuroscience course which is a part of the required first-year Medical School curriculum, covering all areas of Neuroscience from molecular biology to anatomy to systems neuroscience. In 2008, I was discussion group leader for a weekly discussion and problem-solving section of about 19 students with Eric Young. This course is organized by Jay Baraban in the department of Neuroscience.
This course is the basic Neuroscience course which is a part of the required first-year Medical School curriculum, covering all areas of Neuroscience from molecular biology to anatomy to systems neuroscience. In 2006, I was a discussion group leader for this course, leading a weekly discussion and problem-solving section of about 15 first-year medical students along with Mark Walker (now at Case Western) in the department of Neurology. This course is organized by Jay Baraban in the department of Neuroscience.
This is a paper-reading course taught by Ernst Niebur
at the Mind-Brain Institute . In 2005, I presented a series of three student guest lectures on reverse correlation
methods. An updated lecture on systems identification methods in neurophysiology is available.
lecture
This course is organized by Steve Hsiao of the Mind-Brain Institute and is part of the core curriculum for the Neuroscience PhD program. It is a lecture and paper reading course which includes material on sensory, motor and memory systems, as well as attention and consciousness. I was a teaching assistant for this course, and assisted students with understanding the material and preparing presentations.
Binghamton University
After graduating from high school, I was a teaching assistant for an introductory computer science course at Binghamton University by Stanley Reksc. I assisted the students in the course with their programming projects during a weekly computer lab.
Future Courses
This course will introduce the basic ideas of machine learning and neural computation, and will
be designed to be useful and accessible to upper-level undergraduate and beginning graduate students in computer science, cognitive science and
neuroscience alike. We will cover both supervised and unsupervised learning from a Bayesian perspective. In addition to covering the
theory, we will consider real-world applications of the techniques to both cognitive science and engineering.
syllabus
sample lecture
This will be an introductory level course on computational neuroscience and will cover in equal measure material the
celluar, network and information-processing levels of analysis. The main emphasis will be on providing young scientists from cognitive and
biological backgrounds with a set of quantitative neural modeling tools which will be useful in their future research. Computer laboratory exercises,
homework problems and projects will reinforce the lectures and help to make the ideas concrete.
syllabus
sample lecture
This course will focus on information processing in the visual, auditory and somatosensory systems, covering material in both
physiology and psychophysics. Central themes of the course will be formulating sensory representation as a computational problem and understanding the relationship between
neural coding, perception and the statistics of natural sensory environments. We will consider ideal observer analysis and signal detection theory and
emphasize the notion of developing quantitative models for psychophysical experiments.
syllabus
sample lecture