Teaching Experience
Teaching at Johns Hopkins
440.704: Physiology of the Central Nervous System (2002)
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.
440.800: Neuroscience A (2006, 2008)
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. In 2008, I was discussion group leader for 19 students with Eric Young . This course is organized by Jay Baraban in the department of Neuroscience.
080.621: Theoretical and Computational Neuroscience (2005, 2007)
This is a paper-reading course taught by Ernst Niebur at the Mind-Brain Institute , where selected students take turns teaching papers in computational neuroscience to the class. Unlike a journal club, we make a point of discussing each paper in full mathematical detail. In 2005, I presented a series of three lectures on reverse correlation methods, and in 2007 I presented two lectures on the statistics of the natural visual environment.
TopTeaching at Binghamton University
CS 140: Introduction to Programming (1994)
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.
TopFuture Course Development
Neural Computation
This will be an upper division course suitable for students in both the computational and cognitive sciences. We will discuss the theory of artificial neural networks and how they can be applied as models of sensory processing, motor control and memory. We will also discuss elements of statistical and machine learning in the context of supervised and unsupervised learning in neural networks.
Computational Neuroscience
This will be an upper division course suitable for students in both the computational and cognitive sciences. We will cover the application of mathematical modeling to neuroscience from the cellular to the systems levels, including Hodgkin-Huxley theory, neural networks, and statistical analysis of neural data.
Sensory Systems
This will be an upper division course suitable for students in both the computational and cognitive sciences. The focus will be unifying neurophysiology and pyschophysical results in an information-processing framework. In addition to presenting experimental data, we will consider computational theories of sensory processing, the statistics of natural sensory stimuli, and how neurally plausible systems can solve sensory problems.
Introduction to Neuroscience
This course will be appropriate for upper-division undergraduates majoring in Biology and Psychology, as well as students from the computational sciences interested in the brain. The first semester will cover cellular, molecular and medical neuroscience, including cellular neurophysiology, neuronal devlopment, sensory transduction and elements of neuroanatomy. The second semester will focus on systems, computational and cognitive neuroscience, emphasizing neurophysiology and behavioral results which help us to understand information processing in sensory, motor and memory systems.
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