Courses

BIOINF 2010 Biomedical Informatics Colloquium (1 credit)

The course consists of weekly seminars that focus on current research topics in biomedical informatics and artificial intelligence. Guest lecturers are mostly from universities, but also from government laboratories and private companies. Attendance is required throughout graduate training, but only if the course is taken for credit. Grading is based on the extent of active participation (i.e., asking questions) throughout the semester.

• Instructor: Lujia Chen, PhD
• Terms: fall and spring
• Days/Times: Fridays, 11 a.m. to noon
• Location: 814 Murdoch
• Expected Class Size: 10-20

BIOINF 2016 Foundations of Translational Bioinformatics (3 credits)

The course goals are to gain familiarity with data produced with current biotechnologies—such as DNA arrays (e.g., SNP data), microarrays (transcriptional profiles), proteomics (mass spectrometry data), and epigenomics (methylation profiles)—and to understand what can be done with such data to infer relations between genome, epigenome and phenome to discover molecular mechanisms of diseases, identify biomarkers or discover novel therapies for diseases.

• Instructor: Madhavi Ganapathiraju, PhD
• Terms: spring, every odd year
• Days/Times: TBA
• Location: 814 Murdoch
• Expected Class Size: 5-10

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. More specifically, they will conduct a project to learn how biomedical data processing can be enhanced with the 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 the 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, PhD
• Terms: spring
• Days/Times: TBA
• Location: TBA
• Expected Class Size: 5-10

BIOINF 2032 Biomedical Informatics Journal Club (ISSP 2083) (1 credit)

This course consists of weekly meetings that focus on reviewing recent advances in biomedical informatics and artificial intelligence. Students will present classical and recent research articles, as well as develop skills in critically evaluating them, presenting them to their peers, and critiquing peer presentations.

• Instructor: Lujia Chen, PhD (fall), and Eric Strobl, MD, PhD (spring)
• Terms: fall and spring
• Days/Times: Fridays, 10 to 11 a.m.
• Location: 814 Murdoch
• Expected Class Size: 5-10

BIOINF 2061 Single-Cell and Spatial Genomics Data Analysis (3 credits)

Students will work on a wide range of projects centered on preprocessing, analysis, integration, interpretation, visualization, manipulation and design of single-cell and spatial genomics data and experiments in life sciences and biomedicine. Ideas for potential projects will be suggested, and students are also free to choose their own topics.  The class will meet twice a week (class and recitation). Class time will be used to introduce necessary concepts from biology and computational methodology. Recitation time will be used to discuss students' progress in their projects. For most projects, knowing R or Python will be necessary, and students should be ready to pick it up in the first few weeks of the seminar. Students may pair up to work on more ambitious projects or on complementary aspects of a project.

• Instructor: Hatice Osmanbeyoglu, PhD
• Terms: spring
• Days/Times: Tuesdays and Thursdays, 11 a.m. to 12:30 p.m.
• Location: 814 Murdoch
• Expected Class Size: 5-10

BIOINF 2062 Foundations of Algorithms for Biomedical Informatics (3 credits)

This course provides a comprehensive introduction to the design and analysis of algorithms and advanced data structures that underpin modern artificial intelligence, offering students a rigorous and practical foundation. Emphasis is placed on key computational principles, including algorithmic complexity, divide-and-conquer strategies, dynamic programming and graph-based methods. Homework assignments provide hands-on experience with applying algorithms.

• Instructor: Madhavi Ganapathiraju, PhD
• Terms: fall
• Days/Times: Tuesdays andThursdays, 1 to 2:30 p.m.
• Location: 814 Murdoch
• Expected Class Size: 5-10

BIOINF 2070 Foundations of Biomedical Informatics 1 (3 credits)

This course is part of a two-course series that comprehensively covers foundational methods and central themes in biomedical informatics and artificial intelligence (AI) through the integration of data and information management, knowledge discovery and the translation of insights into improved clinical and population health outcomes. Key topics include people, data, knowledge, and evaluation, as well as the management of biomedical data and processes for discovering, assessing and applying biomedical knowledge to support precision and population health. Through lectures and hands-on programming exercises, students develop practical skills in applying core biomedical informatics and AI concepts to real-world health and research applications, contextualized in a semester-long group project.

• Instructor: Rafael Ceschin, PhD, and Olga Kravchenko, PhD
• Terms: fall
• Days/Times: Tuesdays and Thursdays, 9:00 to 10:30 a.m.
• Location: 814 Murdoch
• Expected Class Size: 5-10

BIOINF 2071 Foundations of Biomedical Informatics 2 (3 credits)

This course is part of a two-course series that comprehensively covers foundational methods and central themes in biomedical informatics and artificial intelligence (AI) through the integration of data and information management, knowledge discovery, and the translation of insights into improved clinical and population health outcomes. Key topics include people, data, knowledge, evaluation, the management of biomedical data, and processes for discovering, assessing, and applying biomedical knowledge to support precision and population health. Through lectures and hands-on programming exercises, students develop practical skills in applying core biomedical informatics and AI concepts to real-world health and research applications, contextualized in a semester-long group project.

• Instructor: Xinghua Lu, MD, PhD
• Terms: spring
• Days/Times: Mondays and Wednesdays, 9:30 to 10:55 a.m.
• Location: 814 Murdoch
• Prerequisites: BIOINF 2062 Foundations of Algorithms or equivalent
• Expected Class Size: 5-10

BBIOINF 2105 Artificial Intelligence for Biomedical Informatics (3 credits)

This course offers a thorough introduction to the fundamental concepts and methods of modern artificial intelligence (AI), providing students with a rigorous and practical foundation. The focus is on symbolic methods, machine learning, Bayesian learning, deep learning and generative AI. Although topics covered have broader application beyond biomedicine, relevant biomedical problems and applications are integrated throughout the course. Homework assignments provide hands-on experience with applying AI methods to biomedical applications.

• Instructor: Richard Boyce, PhD
• Terms: fall
• Days/Times: Mondays and Wednesdays, 9 to 10:30 a.m.
• Location: 814 Murdoch
• Expected Class Size: 10-15

BIOINF 2125 Informatics and Industry (1 credit)

This class will be held once a week for one hour. The focus of the class is to provide an opportunity for students to interact with leading industry representatives and to learn techniques and tools that would enable them to market their skills in non-academic environments. We will invite speakers from various local, regional, national and international relationships that we have established.

• Instructor: TBA
• Terms: spring, every odd year
• Days/Times: TBA
• Location: 814 Murdoch
• Expected Class Size: 5-10

BIOINF 2132 Special Topic Seminar in Medical Informatics (1-3 credits)

This course is designed for faculty to offer small groups of students a study course on a topic of mutual interest and concern in the faculty member’s area of expertise.

• Instructor: Department of Biomedical Informatics faculty (will vary)
• Terms: TBA
• Days/times: TBA
• Location: 814 Murdoch
• Expected Class Size: 5-10

This course could be offered in any given term; check with Toni Porterfield (tls18@pitt.edu)

BIOINF 2134 Publication and Presentation in Biomedical Informatics (3 credits)

This course provides a practical overview of writing a research manuscript and delivering a scientific talk. Students must have a research project for the course exercises. Each week focuses on a specific manuscript or presentation section, with peer critiques of early drafts addressing both the overall structure of research papers and the construction of sentences and paragraphs. Didactic sessions will discuss reviewing background literature, publication ethics, authorship and other related topics. The course also covers the details of the publication process and provides an introduction to grant and proposal writing. By the end of the course, students will have completed a research paper and a finalized presentation.

• Instructor: Harry Hochheiser, PhD
• Terms: fall
• Days/Times: Mondays and Wednesdays from 1 to 2:30 p.m.
• Location: 814 Murdoch
• Prerequisite: research project in progress with approval of both research advisor and course instructor.
• Expected Class Size: 5-10

BIOINF 2480 Masters Thesis/Project Research (1-6 credits)

• Terms: fall, spring and summer

BIOINF 2990 Masters Independent Study (1-6 credits)

• Terms: fall, spring and summer

BIOINF 2993 Masters Directed Study (1-6 credits)

• Terms: fall, spring and summer

BIOINF 3990 Doctoral Independent Study (1-6 credits)

• Terms: fall, spring and summer

BIOINF 3995 Doctoral Directed Study (1-6 credits)

• Terms: fall, spring and summer

BIOINF 3998 Doctoral Teaching Practicum (3 credits)

• Terms: fall, spring and summer

BIOINF 3999 Doctoral Dissertation Research 1-(18 credits)

• Terms: fall, spring and summer

Students registering for full-time dissertation study must register under the School of Medicine’s course number: FTDS 0000 (0 credits).