Fall 2013, Fall 2012, Fall 2011, Fall 2010
Healthcare is facing a digital revolution from data collection to its application in decision-making. The Affordable Care Act has a major provision for electronic health records or HER. The EHR will not only reduce the paperwork and administrative effort, but it will also lead to a reduction in costs, reduction in errors, improved and standardized data, and the meaningful use of such data will improve the quality of care (preventative medicine). With the availability of the digitized data comes the opportunity of novel large-scale analytics towards prospective healthcare—a personalized assessment of ones's health, along with a sundry of recommended lifestyle changes. Meaningful use of the electronic health care data is to not only take a giant leap towards personalized and prospective health care, but also reduce the healthcare costs by designing a better disease management strategy, leading to lifestyle adjustments and pre-emptive measures. Personalized medicine integrates genetic, genomic, and clinical information to predict a person's likelihood of developing a disease, its onset course, and potential treatment plans. The course will bring together the intersection of medicine and computational thinking for the grand challenge problems in healthcare. The course will capture the intersection of clinical informatics and public health informatics. It will not cover bioinformatics.
Data Mining/Data Science
Fall 2014, Spring 2013, Spring 2012, Spring 2010
Data mining uses methods from multiple fields including but not limited to: machine learning, pattern recognition, databases, probability and statistics, information theory and visualization. The focus of this course will primarily be the machine learning component, with relevant inclusions and references from probability, statistics, pattern recognition, and information theory. The course will give students an opportunity to implement and experiment with some of the concepts, and also apply them to the real world data sets. It will also touch upon some of the advances in related fields such as web mining, intrusion detection, bioinformatics, etc. In addition, we will discuss the role of data mining in the society.
Fundamentals of Computing I
Fall 2010, Fall 2009, 2008, 2007
This is the first part of a two-course introduction-to-computing sequence, intended primarily for computer science and computer engineering majors. It introduces fundamental concepts and principles of computer science, from formulating a problem and analyzing it conceptually, to designing, implementing, and testing a program on a computer. Using data and procedural abstractions as basic design principles for programs, students learn to define basic data structures, such as lists and trees, and to apply various algorithms for operating on them. The course also introduces object-oriented and parallel programming methods.
Networks are a pervasive and ubiquitous phenomenon --- online social networks (facebook, myspace, etc.) or biological networks or economic networks or search and information networks on the WWW. We will introduce basic concepts in network science, discuss metrics and models, use software analysis tools to experiment with a wide variety of real-world network data, and study applications to a number of areas. We will analyze several datasets, and focus on a number of applications, including blogging, marketing, tagging, financial networks, wikipedia, facebook, citation networks, co-authorship, etc. We will also study how information, fads, and political movements (recent political climate will provide us with sufficient fodder) spread via the networks. The students will develop a comprehensive understanding of networks, including their sensitivities and vulnerabilities, and be able to design practical, and sound solutions for real-world problems.