Teaching


2016 CS-GSS Teaching Award

 
Fall 2019
206 Introduction to Discrete Structures II
 
Provides the background in combinatorics, probability theory and graph theory required in design and analysis of algorithms, in system analysis, and in other areas of computer science.

Syllabus
 
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Spring 2019

525/425 Brain-inspired Computing - NEW Course

Syllabus

The course provides an overview of the fundamental concepts and current trends in neuro-mimetic and neuro-inspired Computing with a focus on designing neuromorphic networks for vision and movement.

Course Objectives:

1. To explore how computation in the human brain can be effectively modeled across different levels of abstraction (from a single neuron to neuronal networks and systems)

2. To introduce a computational formalization of brain function based on the model of neuron as a Spike Processing Machine – Spike Neural Networks (SNNs);

3. To employ neuro-mimetic or neuro-inspired SNNs and tackle a CS problem in a term-wide project.

To have access to the course material, please login to sakai.

 
Fall 2018
 
On sabbatical
 
Spring 2018

673 Data-driven and Neurorehabilitation Robotics

To have access to the course material, please login to sakai.
 
Fall 2017

525/443 Brain-inspired Computing - NEW Course

Syllabus

The course provides an overview of the fundamental concepts and current trends in neuro-mimetic and neuro-inspired Computing with a focus on designing neuromorphic networks for vision and movement.

Course Objectives:

1. To explore how computation in the human brain can be effectively modeled across different levels of abstraction (from a single neuron to neuronal networks and systems)

2. To introduce a computational formalization of brain function based on the model of neuron as a Spike Processing Machine – Spike Neural Networks (SNNs);

3. To employ neuro-mimetic or neuro-inspired SNNs and tackle a CS problem in a term-wide project.

To have access to the course material, please login to sakai.

Spring 2017

525/443 Brain-inspired Computing - NEW Course


The course provides an overview of the fundamental concepts and current trends in neuro-mimetic and neuro-inspired Computing with a focus on designing neuromorphic networks for vision and movement.

Course Objectives:

1. To explore how computation in the human brain can be effectively modeled across different levels of abstraction (from a single neuron to neuronal networks and systems)

2. To introduce a computational formalization of brain function based on the model of neuron as a Spike Processing Machine – Spike Neural Networks (SNNs);

3. To employ neuro-mimetic or neuro-inspired SNNs and tackle a CS problem in a term-wide project.

To have access to the course material, please login to sakai.

Fall 2016

107 Computing for Math and the Sciences


This course introduces the student to computers, programming, and some of the key ideas on which the field of computer science is based. The primary vehicle for doing so is the computer language MATLAB. The use of a program like MAPLE to manipulate symbolic equations is also covered.

Topics: Structure of Computers, MATLAB basics, Computation and decision-making, Iteration, Recursion, Applications, Efficiency Analysis. MAPLE, Program Proving

To have access to the course material, please login to sakai.

Spring 2016

443/674 Integration of Computer and Brain Sciences


In this course, students have the opportunity to discover, and understand

1) how the human brain computes to achieve intelligent behavior and

2) how this knowledge guides the development of new computational algorithms that

  • mimic the neural activity
  • simulate a brain disease
  • harness the learning ability of the brain (neuro-plasticity) and apply therapy

We cover the entire range of computational methods for modeling the brain activity from the micro (neural) to the macro (behavioral) level (without this coverage to be complete or dense).

Course syllabus.

To have access to the course material, please login to sakai.

Fall 2015

443/674 Integration of Computer and Brain Sciences


In this course, students have the opportunity to discover, and understand

1) how the human brain computes to achieve intelligent behavior and

2) how this knowledge guides the development of new computational algorithms that

  • mimic the neural activity
  • simulate a brain disease
  • harness the learning ability of the brain (neuro-plasticity) and apply therapy

We cover the entire range of computational methods for modeling the brain activity from the micro (neural) to the macro (behavioral) level (without this coverage to be complete or dense).

Course syllabus.

To have access to the course material, please login to sakai.