FALL

September

• Data Science using R

  Data Science using R [BME 5096 03, PDRT 5095 02]

  Dates: September 8 & 9, 2025
  Time: 9 AM to 5 PM both days
  Location: G9.102

  This course would benefit students who pursue advanced R programing techniques for data science. We will provide information about key elements for data science and machine learning, including how to properly preprocess data, how to select meaningful features from the data, how to identify data clusters, and how to build a predictive model. We will then cover statistical test basics and provides semi-hands-on sessions on how to utilize the statistics for biomarker discoveries.

  Please note that this IS NOT a course to learn R; rather it is aimed at teaching R users best practices to analyze data.

  Day 1: Data preprocessing, Feature selection/dimensionality reduction, Data clustering, Predictive models
  Day 2: Statistical test basics, Biomarker discovery I: metabolomics/proteomics data, Biomarker discovery II: RNA-seq data

  Prerequisite:Fluency with R programming language.

  Registration closed.

  Academic credit (1 credit hour) is available.
  UTSW PostDocs use PDRT 5095 02 SPECIAL TOPICS IN BIOINFORMATICS Data Science using R
  UTSW Grad Students use BME 5096 03 SPECIAL TOPICS Data Science using R

  Course Director: Jeon Lee, Ph.D
  Instructors: Jui Wan Loh, Ph.D.
  
  

• Deep Learning for Beginners

  Deep Learning for Beginners [BME 5096 04, PDRT 5095 03]

  Dates: September 15 & 16, 2025
  Time: 9 AM to 5 PM both days
  Location: G9.102

  This course is intended to provide a theoretical as well as practical introduction to Deep Learning. This is not a boiler plate presentation of Deep Learning as widely accessible through online courses. Instead, we hope attendees will take away a deeper understanding of the motivation of implementing neural networks for data modeling and the consequential complexities in formulating the underlying optimization problem. We will then make the critical step towards convolutional neural networks (CNNs), which permit a multiscale analysis of data. We will also offer a balanced discussion of the strengths and weaknesses of Deep Learning vis-à-vis conventional Machine Learning approaches. We will first introduce the intuition and computational underpinnings of Deep Learning, followed by hands on sessions, training attendees on practical approaches to implementing Deep Learning in Pytorch. The entire course revolves around the conceptually simple problem of two-class data classification. See syllabus for a preview of the course content. The course is targeted at biomedical researchers with no prior machine learning experience, yet a keen curiosity in the mathematical and computational of Deep Learning.

  Prerequisite: Competence in (python) programming is required.

  Course outcomes & objectives:
  1. Understand the core elements of data modeling with neural networks.
  2. Understand the power of learning convolution kernels for data modeling.
  3. Learn how to implement a deep learning pipeline in python.
  4. Understand why deep learning methods are able to perform so well and identify situations where they are likely to outperform (or underperform!) classical machine learning approaches.
  5. Gain a practical understanding of various choices in designing and validating a deep learning model.

  Registration closed.

  Academic credit (1 credit hour) is available.
  UTSW PostDocs use 5095 03 SPECIAL TOPICS IN BIOINFORMATICS Deep learning for beginners
  UTSW Grad Students use BME 5096 04 SPECIAL TOPICS Deep learning for beginners

  Course Director: Satwik Rajaram, Ph.D.
  Instructors: Thuong Nguyen, Ph.D., Aleksandra Nielsen

• Time Series Analysis

  Time Series Analysis [BME 5096 05, PDRT 5095 04]

  Dates: September 22 & 23, 2025
  Time: 9 AM to 5 PM
  Location: G9.102

  This course aims to promote understanding of time-series data and their processing/analysis methods. Starting with an introduction to techniques for time-series data processing, we will cover analysis, modeling, and various time-series data analysis techniques being used for neural spiking data.

  Day 1: Time-series signal processing (filtering, imputation, etc.), Feature extraction from time-series signals, Autocorrelation Function (ACF), AR modeling
  Day 2: Neural spiking data analysis (Spike train statistics, Reverse-correlation to estimate receptive fields, Poisson neuron model, Generalized linear model)

  Prerequisites: Familiarity with R and python is required.

  Registration closed.

  Academic credit (1 credit hour) is available. Course numbers coming soon!
  UTSW PostDocs use 5095 04 SPECIAL TOPICS IN BIOINFORMATICS Time Series Analysis
  UTSW Grad Students use BME 5096 05 SPECIAL TOPICS Time Series Analysis

  Course Director: Jungsik Noh, Ph.D.
  Instructors: Jeon Lee, Ph.D., Wenhao Zhang, Ph.D., Srinivas Kota, Ph.D.

• Advanced Concepts of Deep Learning

  Advanced Concepts of Deep Learning

  Dates: September 18, 23, 25, and 30, 2025
  Time: 9 AM to 10:30 AM all four days
  Location: E2.502A

  This course will provide an introduction to key and emerging concepts and ideas in deep learning. We will introduce the design and principle behind recent advances in model architecture: transformers (including several efficient transformer designs), graph neural networks, and several other new architectures that utilize attention-like multiplicative updates. Then, we will cover mathematics and algorithms of generative probabilistic modeling with deep learning, including energy-based models, variational autoencoder, generative adversarial network, normalizing flow, neural ODE, and diffusion probabilistic models. Conceptual advances will be the focus of this nanocourse.

  Participants will be attending 4 key lectures with the class of BME 5317 Machine Learning course.
  There are no practicals or hands-on exercises beyond the content taught in the 4 lectures.

  Prerequisites: This course is advanced and requires knowledge of programming, machine learning, and deep learning.

  Registration closed.

  There is no academic credit for this nanocourse. A completion certificate can be issued upon request.

  Course Director: Albert Montillo, Ph.D.

  Instructors: Aixa Andrade-Hernandez

October

• Diffusion Models: from Image to Biological Sequence Generation

  Diffusion Models [BME 5096 06, PDRT 5095 07]

  Dates: October 13 and 16, 2025 [please note that dates are non-consecutive]
  Time: 9 AM to 5 PM both days
  Location: G9.250A

  Diffusion generative models such as Stable Diffusion have achieved remarkable results in generating images, videos, and so on. This two-day course explores the key principles behind diffusion models. During the first part of course, participants will gain a theoretical understanding behind the original diffusion models and become familiar with score-based generative stochastic differential equation models. Students will also learn about diffusion guidance and how to guide diffusion models for conditional image generation, style transfer, and image processing/reconstruction tasks. Participants will have an opportunity to implement the first diffusion model and generate images. During the second part, we will depart from image generation and will venture to biological sequence generation by studying several state-of-art diffusion models. As a result of the course, participants will learn how to implement diffusion models and how to generate various data modalities including images and DNA sequences.

  Prerequisites: This course requires basic knowledge of deep learning and the ability to develop and train own models using PyTorch on GPU.

  Please register using this form. Registration closes 9/5/2025, 5 PM.

  Academic credit (1 credit hour) is available.
  UTSW PostDocs use PDRT 5095 07 SPECIAL TOPICS IN BIOINFORMATICS Diffusion Models
  UTSW Grad Students use BME 5096 10 SPECIAL TOPICS Diffusion Models: From Image to biological Sequence Generation

  Course Director: Satwik Rajaram, Ph.D.
  Instructors: Pavel Avdeyev, Ph.D. and Dushyant Mehra, Ph.D.

• Introduction to Linux

  Introduction to Linux [BME 5096 07, PDRT 5095 08]

  Dates: October 20 & 21, 2025
  Time: 9 AM to 5 PM both days
  Location: G9.102

  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.

  Please register using this form. Registration closes 9/8/2025, 5 PM.

  Academic credit (1 credit hour) is available.
  UTSW PostDocs use PDRT 5095 08 SPECIAL TOPICS IN BIOINFORMATICS Introduction to Linux
  UTSW Grad Students use BME 5096 07 SPECIAL TOPICS Introduction to Linux

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

• Introduction to Python

  Introduction to Python [BME 5096 08, PDRT 5143 01]

  Dates: October 21 & 22, 2025
  Time: 9 AM to 5 PM both days
  Location: ND11.218

  This two-day intensive course is designed to introduce Python programming to graduate students and postdocs in biomedical fields. The course aims to provide a solid foundation in Python, emphasizing practical applications relevant to research. Participants will learn about Python's structures, flow control, data handling, basic analysis techniques, and how to write clean, reusable code.
  Course Objectives:
  By the end of this course, participants will be able to…

  • Understand and implement basic Python syntax and programming concepts.

  • Manage project dependencies and create reproducible Python environments.

  • Apply Python data structures effectively in solving real-world problems.

  • Utilize Python for data manipulation, basic statistical analysis, and visualization.

  • Write reusable and efficient code using object-oriented programming principles. Explore parallel processing techniques to optimize performance for larger datasets.

  Prerequisites: None.

  Please register using this form. Registration closes 9/8/2025, 5 PM.

  Academic credit (1 credit hour) is available.
  UTSW PostDocs use PDRT 5143 01 BIOINFORMATICS Python Level I
  UTSW Grad Students use BME 5096 08 SPECIAL TOPICS Introduction to Python

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

• Large Language Models in Action: A Practical Introduction

  LLMs in Action: A Practical Introduction [BME 5096 09, PDRT 5095 06]

  This is a two-day intensive course exploring the world of Large Language Models (LLMs) in healthcare and biomedical research. Designed for beginners, this hands-on program covers LLM fundamentals, practical applications, ethical considerations, and the landscape of different LLM options. Participants will learn to leverage LLMs for a range of tasks (e.g., document analysis, assessment, clinical decision support). The course will delve into comparing various foundation models, discussing the pros and cons of open-source versus proprietary LLMs, and guiding participants in choosing the right tools for their needs. Through interactive sessions, attendees will develop the skills to effectively and responsibly use LLMs in their respective fields.

  Prerequisite:Literacy in basics of machine learning.

  Please register using this form. Registration closes on 9/15/2025, 5 PM.

  Academic credit (1 credit hour) is available.
  UTSW PostDocs use PDRT 5095 06 SPECIAL TOPICS IN BIOINFORMATICS LLMs in Action - A Practical Introduction
  UTSW Grad Students use BME 5096 09 SPECIAL TOPICS LLMs in Action - A Practical Introduction

  Course Director: Andrew Jamieson, Ph.D.
  Instructors: TBA

November

• Science Communication

  Science Communication

  Dates: November 6 & 7, 2025
  Time: 9 AM to 5 PM both days
  Location: ND11.218

  This nanocourse will be taught in collaboration with the Teaching & Science Communication Club (TaSC) at UTSW as a two-day hands-on workshop. The workshop will begin with fundamentals of science communication, best practices for effective communication, and themes underpinning different communication formats. Participants are required to bring their own project like a graphical abstract, presentation, poster, talk, manuscript, grant proposal, 3-minute thesis, etc. During the workshop, you will put into practice the learned principles to edit and refine your specific projects into final versions. We will provide real-time insights into improving your scientific communication with detailed feedback as well as teach you how to repurpose existing material into different modalities; for example: converting a PPT to a chalk talk or poster.

  Prerequisites:None.

  Registration opens in September 2025.

  There is no academic credit for this nanocourse. A completion certificate can be issued upon request.

  Course Director: Stuart Ravnik, Ph.D.
  Instructors: Sarah Jobbins, Innesa Leonovich, Brandon Smith, Prapti Mody, Ph.D.

SUMMER

7/14-7/15: Scientific Reproducibility with Containers
8/4-8/5: Shiny Apps for Interactive Data Analysis and Sharing
8/6: Scalable Data Analysis with Dask
8/15: Basic Optics for Microscopy

SPRING

2/5-2/6: Programming for Beginners using MATLAB
2/20-2/21: Single cell Genomics
2/24-2/25: Introduction to Linux
3/5-3/6: Introduction to Python Software Development on GitHub
3/10-3/11: Multiplexed NGS Assays & Analysis: from FASTQ to fitness
4/1-4/3: Introduction to Computational Neuroscience
4/4: Neuroimaging & MRI: Processing & Analysis of Brain Data
4/16-4/18: Theory of Variational Methods
4/22-4/23: LLMs in Action: A Practical Introduction