SPRING

February

• Programming for Beginners (with MATLAB)

  Programming for Beginners (with MATLAB) [BME 5096-04, PDRT 5095-01]

  Dates: February 2 & 3, 2026
  Time: 9 AM to 5 PM both days
  Location: G9.102

  This course is designed to break down the mystery of coding for the novice and to illustrate the basic thinking behind structuring a set of instructions to produce something intelligible. Students will learn how to write and read simple codes and how to evaluate the progression of a program sequence, both numerically as well as through graphical representations of intermediate and final results. As a final project, students will have a choice of programming a classic algorithm for data clustering or a classic algorithm for the simulation of biochemical reactions.

  Pre-requisite: Assigned YouTube tutorials (6 hours) + quiz.

  Please register using this form. Registration closes 12/29/2025, 5 p.m.

  Academic credit (1 credit hour) is available.
  UTSW PostDocs use PDRT 5095-01
  UTSW Grad Students use BME 5096-04

  Course Director: Srinivas Kota, Ph.D 
  Instructors: Dushyant Mehra, Ph.D., Armand Rathgeb, Khai Nguyen

• Single-Cell Analysis for Biologists: No Coding Required
  

  Single-Cell Analysis for Biologists: No Coding Required [BME 5096-05, PDRT 5095-02]

  Dates: February 12 & 13, 2026
  Time: 9 AM to 5 PM both days
  Location: ND11.218

  This two-day nanocourse introduces the core principles of single-cell analysis through a hands-on, no-coding workflow using our lab-developed platform. Participants will explore real datasets, interpret biological outcomes, and gain confidence in analyzing single-cell and spatial data — all without programming. The course emphasizes accessible tools and biological insight for students from biology and medical backgrounds. Biology comes first. Coding comes later — or never.

  Pre-requisite: None.

  Registration opens in early December.

  Academic credit (1 credit hour) is available.
  UTSW PostDocs use 5095-02
  UTSW Grad Students use BME 5096-05

  Course Director: Qiang Feng, Ph.D.
  Instructors: Jinming Gao, Ph.D., Baran Sumer, M.D., Alex Hunter, Animesha Krishnamurthy. 

• Ethics in AI: Scientific Writing
  

  Ethics in AI: Scientific Writing

  Dates: February 19 & 20, 2026
  Time: 2 to 4 PM both days
  Location: ND11.218

  This two-day nanocourse is a small workshop designed on the topic of AI detection and use in scientific writing and it's ethical implications. During this workshop, we will introduce AI basics, ethical best practices, and provide examples of the AI tools available for detecting use of AI in scientific writing as well as using AI responsibly for the same. This is a discussion-heavy workshop with case studies. During the registration, we will ask each participant to bring cases of interest as well as their own scientific writing material (grant/ manuscript, etc.) to discuss.
  This is the first of many other Ethics in AI workshops to come. We are curating them based upon feedback for the inaugural Ethics in AI symposium held on October 8, 2025.

  Pre-requisite: None.

  Registration opens in December.

  There is no academic credit for this workshop. A certificate of completion will be issued upon request to participants who complete the entire 4-hour nanocourse.

  Course Director: Prapti Mody, Ph.D.
  Instructors: Elizabeth Heitman, Ph.D., Frederick Grinnell, Ph.D., Darlene King, M.D., Lauren Sankary, M.A., J.D., Estefanie Garduno-Rapp, M.D., M.S.H.I., Yi Ren.

• Advanced Topics in Generative Modeling
  

  Advanced Topics in Generative Modeling [BME 5096-06, PDRT 5095-03]

  Dates: February 26 & 27, 2025
  Time: 9 AM to 5 PM
  Location: G9.102

  Autoregressive and diffusion models have been integrated into some of the forefront architectures used for text, image, video, and protein structure generation tasks. This two-day course will cover existing implementations of foundational architectures behind generative models used for image, video, text generation and how it can be applied for biomedical research tasks. This course will cover fast and scalable implementations of both continuous and discrete diffusion/flow models and methods for conditional generation/guidance at training and inference. We will discuss latest developments and implementations of diffusion and autoregressive models and discuss optimal use cases of both. We will cover biomedical applications of using these models for image/text generation, representation learning, style transfer, solving inverse problems, and protein structure prediction.

  Prerequisites: Participants should have familiarity with deep learning, ability to train models on GPUs, and ideally some understanding of generative modeling or have taken the diffusion models nanocourse but we will accept anyone that is interested in the course.

  Registration opens in December.

  Academic credit (1 credit hour) is available. 
  UTSW PostDocs use 5095-03
  UTSW Grad Students use BME 5096-06

  Course Director: Satwik Rajaram, Ph.D.
  Instructors: TBD

March

• Introduction to Python Software Development on GitHub

  Introduction to Python Software Development on GitHub [BME 5096-07, PDRT 5095-04]

  Dates: March 5 & 6, 2026 
  Time: 9 AM to 5 PM both days
  Location: G9.102

  In today's world of scientific research and development, the ability to effectively collaborate and develop software as a team is essential. This two-day introductory course is designed specifically for graduate students and postdoctoral researchers seeking to enhance their software development skills in Python and embrace modern continuous integration practices on GitHub. Participants will gain hands-on experience in using Git, pre-commit hooks, unit testing, managing dependencies, and ultimately maintaining stable code through detailed environment requirements. The skills gained in this course will enable participants to work efficiently as part of a team, ensuring the development of high-quality and maintainable software.

  Prerequisites: Introduction to Python nanocourse in Fall 2025 or basic literacy/pre-reading about python & GitHub.

  Registration opens in January.

  Academic credit (1 credit hour) is available.
  UTSW PostDocs use PDRT 5095-04
  UTSW Grad Students use BME 5096-07

  Course Director: Kevin Dean, Ph.D.
  Instructors: Conor McFadden

• Neuroimaging & MRI: Processing & Analysis of Brain Data

  Neuroimaging & MRI: Processing & Analysis of Brain Data [BME 5096-08, PDRT 5095-05]

  Dates: March 12 & 13, 2026
  Time: 9 AM to 5 PM both days
  Location: G9.250A

  This nanocourse provides a comprehensive introduction to the processing and analysis of brain MRI data, with applications in neuroscience and biomedical research. Participants will explore key techniques for handling, preprocessing, and analyzing structural, diffusion weighted, and functional brain MRI datasets. The course will emphasize practical challenges in neuroimaging studies and demonstrate cutting-edge tools used in brain MRI research. Hands-on sessions will allow participants to work directly with brain MRI data, utilizing popular software like FSL, FreeSurfer, SPM, and ANTS.

  Prerequisites: Familiarity with brain scan datasets and image analysis as well as softwares for data analysis.

  Registration opens in January.

  Academic credit (1 credit hour) is available.
  UTSW PostDocs use PDRT 5095-05
  UTSW Grad Students use BME 5096-08

  Course Director: Jeon Lee, Ph.D.
  Instructors: Ahmed Shalaby, Ph.D. & Krishna Kanth Chitta, M.S.

• Single Cell Genomics - with Programming

  Single Cell Genomics - with Programming [BME 5096-09, PDRT 5095-06]

  Dates: March 19 & 20, 2026
  Time: 9 AM to 5 PM both days
  Location: G9.250A

  This course covers the basics of single-cell technologies and computational analysis. We will provide overviews and key algorithms for single-cell RNA-Seq, single-cell ATAC-Seq, and multiome analysis. This course includes hands-on practice to perform analyses from raw data to quality control, clustering, visualization, and trajectory inference. It also includes more advanced topics including multiome analysis, spatial transcriptomics, and single-cell perturbation.

  Prerequisites: Proficiency with R and Python.

  Registration opens in January.

  Academic credit (1 credit hour) is available.
  UTSW PostDocs use PDRT 5095-06
  UTSW Grad Students use BME 5096-09

  Course Director: Jeon Lee, Ph.D.
  Instructors: Jingxuan Chen, Ph.D., Jui Wan Loh, Ph.D., & Shao-Po Huang

April

• Introduction to Computational Neuroscience

  Introduction to Computational Neuroscience [BME 5096-10, PDRT 5095-07] 

  Dates: April 7 & 9, 2026 [please note that the days are non-consecutive] 
  Time: 9 AM to 5 PM all days
  Location: G9.250A

  This nanocourse provides an introduction to computational neuroscience. Topics cover single-neuron models, neural circuit models, neural coding theories, and perceptual computations. We will go through the concept and math of these models followed by coding exercises.

  Prerequisites: Familiarity with programming and machine learning is required.

  Registration opens in February.

  Academic credit (1 credit hour) is available.
  UTSW PostDocs use PDRT 5095-07
  UTSW Grad Students use BME 5096-10

  Course Director: Wenhao Zhang, Ph.D.
  Instructors: Eryn Sale

• Introduction to Linux

  Introduction to Linux [BME 5096-11, PDRT 5095-08]

  Dates: April 14 & 15, 2026
  Time: 9 AM to 5 PM both days
  Location: ND11.218

  Linux is a robust and versatile operating system favored by programmers and system administrators. Known for its stability and adaptability, it powers devices ranging from smartphones to supercomputers. Linux is particularly popular in academic and scientific fields due to its customizability and extensive suite of integrated tools. This two-day workshop welcomes beginners interested in learning Linux. It will introduce fundamental concepts to get you started on your Linux journey. This workshop lays the groundwork for anyone new to Linux. Those working in research, scientific computing, or computationally demanding fields will particularly benefit from its HPC emphasis.

  Prerequisites: none.

  Academic credit (1 credit hour) is available.
  UTSW PostDocs use PDRT 5095-08
  UTSW Grad Students use BME 5096-11

  Course Director: Liqiang Wang, M.S.
  Instructors: BioHPC staff

• Shiny Apps for InteractiveData Analysis and Sharing

  Shiny Apps for Interactive Data Analysis and Sharing [BME 5096-12, PDRT 5095-09]

  Dates: April 27 & 28, 2026
  Time: 9 AM to 5 PM both days
  Location: G9.102

  Shiny is a framework for developing interactive applications that run R code. These apps are broadly useful in biological data analysis and are particularly well suited for exploratory analysis of complex data, sharing datasets and workflows with non-coding users, and interactive teaching demonstrations. In this nanocourse, students will learn to quickly write simple Shiny apps and share them with users. Students will independently develop their own applications and present them at the end of the course. Basic R competency is required, because instruction will take place in R. Supplementary python examples will be provided.

  Prerequisites: Fluency in R programming language is required.

  Academic credit (1 credit hour) is available.
  UTSW PostDocs use PDRT 5095-09
  UTSW Grad Students use BME 5096-12

  Course Director: Scott Saunders, Ph.D.
  Instructors: TBD