Fall 2016


Foundations of Clinical and Public Health Informatics (ISSP 2015/HRS 2429/INFSCI 2821) (3 credits)

A survey of fundamental concepts and activities on information technology applied to health care. Topics include computer-based medical records, knowledge-based systems, telehealth, decision theory and decision support, human-computer interfaces, systems integration, the digital library, bioinformatics, and educational applications.  Department-specific applications such as pathology, radiology, psychiatry and intensive care are also discussed.

Instructor: Rich Tsui, Ph.D.

Days/Times: Mondays and Wednesdays, 10:30 a.m. to 11:55 p.m.

Location: 407A BAUM, 5607 Baum Blvd.

Prerequisites: None

Recitations: None

Expected class size: 20-25


Biomedical Informatics Colloquium (Lecture Series) (This is not a formal course.)

This course meets once each week for one hour.  The current research of Biomedical Informatics faculty and senior fellows will be presented.

Instructor:  Various speakers

Days/Times:  Fridays, 9:00 a.m. to 10:00 a.m.

Location:  407A BAUM, 5607 Baum Blvd.

Prerequisites:  None

Recitations:  None

Expected class size:  35



Biomedical Informatics Journal Club (ISSP 2083) (1 credit)

Biomedical informatics is a broad field encompassing many different research domains. What all of the domains have in common is the need to review and publish scientific papers and to give talks that present research to different audiences. The aim of this journal club is to expose students to recent research in various topics of biomedical informatics and to teach students how to critique a research article, present research from a research study; and critique a verbal presentation of research.

Instructor:  Richard Boyce, Ph.D.

Term:  Fall

Days/Times:  Fridays, 10:00 a.m. to 11:00 a.m.

Location:  536B BAUM, 5607 Baum Blvd.

Expected class size:  35



Foundations of Bioinformatics (ISSP 2081) (3 credits) 

Provides an introduction to selected topics of bioinformatics also known as computational biology. In this course, the difficult computational problems involving different types of biological information are identified using case studies from current literature.  Emphasis is on genomic aspects of computational biology with some overview of proteomics and structural aspects.  The course is structured as a seminar course intending to draw students into participating in discussions related to both problems and existing solutions. 

Instructor:  Vanathi Gopalakrishnan, Ph.D.

Days/Times:  Mondays and Wednesdays 12:30 p.m. to 2:00 p.m.

Location:  407A BAUM, 5607 Baum Blvd.

Prerequisites:  An introductory biology course and an undergraduate mathematics course.

Recitations:  none

Expected class size: 15



Symbolic Methods in Artificial Intelligence (3 credits)

This course is designed for students who do not necessarily have a background in computer science and want to learn and apply methods in artificial intelligence to problems in biomedicine. The course will introduce and provide the foundations of artificial intelligence methods in logical knowledge representation and reasoning, biomedical ontologies and terminologies and information retrieval. Prerequisites for this course include introductory mathematics and programming.

Instructor:  Richard Boyce, PhD

Days/Times:  Tuesday/Thursdays 9:00 a.m.-10:30 a.m.

Location:  407A BAUM, 5607 Baum Blvd.

Prerequisites:  Introductory Mathematics and Programming

Recitations:  none

Expected class size:  15-20



Human Computer Interaction and Evaluation Methods (3 credits)

This course is designed to provide informatics students with the knowledge necessary to take an applied role in the design, implementation and evaluation of healthcare information systems. In this course, students will apply principles of usability and evaluation theory to informatics projects.  Topics include:  critical success factors, test plan development and user interface design.

Instructor:  Harry Hochheiser, PhD

Days/Times:  Monday/Wednesday 8:30 a.m.-9:55 a.m.

Location:  407A BAUM, 5607 Baum Blvd.

Prerequisites:  There are no prerequisites.

Recitations:  none

Expected class size:  15-20


Special Topic: Bayesian Statistics & Intermediate R

A 3-credit pass-fail course for intermediate-to-advanced students of informatics, computer science, and biostatistics. The focus is two-fold: developing strong understanding of important statistical learning methods and other computational techniques related to Bayesian ideas, and developing intermediate skills in R.

  •  Bayesian statistics: the math of Bayes theory, conjugate families, decision theory; computational methods including MCMC, EM algorithm, importance sampling, acceptance-rejection sampling, and multiple imputation.
  •  Intermediate-to-Advanced R: bioconductor and other packages; graphics; creating packages, documentation, reports; creating user interfaces with shiny; multiple-processor and distributed computing.
  •  Executing Bayes-related techniques in R using packages or de-novo implementation.

Instructor:  Roger Day, Sc.D.

Days/Times:  TBD.

Location:  407B BAUM, 5607 Baum Blvd.



Publication & Presentation in Biomedical Informatics (3 credits)

This course provides a practical overview of how to write a research manuscript and how to give a scientific talk. It is usually taken after completing the Project Course (BIOINF 2014). Students taking this course must have a completed research project that can be used to complete the course exercises. Each week, we will target a specific section of the manuscript or scientific talk. Didactic sessions describing common problems and approaches will alternate with student presentation and peer critique. The course also covers the details of the publication process. At the end of the course, a special presentation workshop gives students the opportunity to improve their talks using videotaping and debriefing methods. By the end of the course, students will have completed a research paper and a finalized colloquium presentation.

Instructor:  Rebecca Jacobson, M.D., M.S.

Days/Times:  Wednesdays from 10:00 a.m. – 12:55 p.m.

Location:  407B BAUM, 5607 Baum Blvd.

Prerequisite: Completed data collection for study in research project with approval of both research advisor and course instructor.

Recitations: None

Expected Class Size: 6