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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, 11:00 a.m. to 12:00 p.m.
Location: 407A BAUM, 5607 Baum Blvd.
Prerequisites: None
Recitations: None
Expected class size: 35-50
BIOINF 2019
Biomedical Data Streaming (3 credits)
In this project, students and a faculty mentor will explore data streaming technologies to implement scalable and distributed biomedical data ecosystems. In particular, students and a faculty mentor will conduct a project to learn how biomedical data processing can be enhanced with processing power of modern data-streaming infrastructures to enable continuous biomedical data acquisition and analysis. Upon completion of this project, students will be able to understand major principles and trade-offs in design and development of a comprehensive biomedical data processing pipeline for data-intensive applications. Students will gain practical skills in selecting, applying, and developing data streaming solutions appropriate for specific data processing and data analysis tasks.
Instructor: Vladimir Zadorozhny, Ph.D.
Term: Spring
Days/Times: TBA
Location: TBD
Expected class size: 10-15
BIOINF 2032
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: Ye Ye, Ph.D.
Term: Spring
Days/Times: Fridays, 10:00 a.m. to 11:00 a.m.
Location: 536B BAUM, 5607 Baum Blvd.
BIOINF 2071
Foundations of Biomedical Informatics 2 (3 credits)
This course serves as an introduction to core methods and topics in biomedical informatics using the context of the Learning Health System (LHS). A LHS combines data and information managements, discovery, and application of discoveries to clinical and population health. Discussion of the challenges associated with the construction of a LHS will be used to contextualize and motivate content to be covered in the course (challenges and analysis and interpretation to create knowledge).
Instructor: Vanathi Gopalakrishnan, Ph.D.
Term: Spring
Days/Times: Mondays/Wednesdays 9:30 a.m. to 10:55 a.m.
Location: 407A BAUM, 5607 Baum Blvd.
Prerequisites: CS 1501 Algorithm Implementation and CS 2710 Foundations of Artificial Intelligence
Expected class size: 15
BIOINF 2118
Statistical Foundations of Biomedical Informatics (3 credits)
This is an introductory probability and statistics course intended primarily for biomedical informatics students. The first part of the course covers probability, including basic probability, random variables, univariate and multivariate distributions, transformations, expectation, numerical integration, and approximations. The second part of the course covers statistics, including study design, classical parametric inference, hypothesis testing, Bayesian inference, non-parametric methods, classification, ANOVA, and regression. We will use R for statistical computing and applications. Examples and applications will focus on biomedical informatics and related discipline.
Instructor: TBD
Term: Spring
Days/Times: TBD
Location: TBD
Expected class size: 10-15
BIOINF 2480 (1-6 credits)
Masters Thesis/Project Research
BIOINF 2990 (1-14 credits)
Masters Independent Study
BIOINF 2993 (1-9 credits)
Masters Directed Study
BIOINF 3990 (1-14 credits)
Doctoral Independent Study
BIOINF 3995 (1-9 credits)
Doctoral Directed Study
BIOINF 3998 (3 credits)
Doctoral Teaching Practicum
BIOINF 3999 (1-9 credits)
Doctoral Dissertation Research
NOTE: Students registering for Full-time Dissertation Study must register under the School of Medicine’s Course Number: FTDS 0000 (0 credits)
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