Ninth Annual Texas A&M Research Computing Symposium

Last Update: May 14, 2026

Symposium Details

Dates: May 13-15, 2026
Location: Talks and Workshops - Texas A&M Innovative Learning Classroom Building (ILCB), College Station, TX (map)
Parking: Fee-based Parking is available at the Stallings Blvd Garage (SBG) adjacent to the ILCB building (map)
Questions? Call us at (979) 458-8414 or email [email protected]

Texas A&M's High Performance Research Computing is hosting a series of talks and Workshops May 13-15, 2026 to showcase the A&M community’s work in high-performance computing and data-intensive research.

The Symposium will feature a keynote presentation on the National Artificial Intelligence Research Resource (NAIRR) Pilot and a presentation for VISION, The Texas A&M University System DGX SuperPOD featuring 760 NVIDIA H200 GPUs.

Keynote Talk:

Katie Antypas, Senior Advisor for Cyberinfrastructure, National Science Foundation

Presentation on VISION, The Texas A&M University System DGX SuperPOD featuring 760 H200 GPUs

Banquet and Poster Session with a prize for best poster

Speakers

Dr. Brendan Roark, Associate Vice President for Research, Texas A&M University
Prof. Daniel Tabor, Chemistry, Texas A&M University
Prof. Wonmuk Hwang, Biomedical Engineering, Texas A&M University
Prof. Phanourios Tamamis, Chemical Engineering, Texas A&M University
Dr. Lars Koesterke, Texas Advanced Computing Center
Dr. Omar Sallam, Argonne National Laboratory
Dr. Alejandro Aviles Sanchez, Texas A&M University

Workshops on AI/ML, GPU Programming, Generative AI, and more

AI Fundamentals, Instructor: Dr. Yalong Pi, TAMIDS
Introduction to GPU programming with OpenMP Offloading, Instructor: Dr. Lars Koesterke, TACC
Generative AI for Protein Design: A Hands-On Introduction to RFdiffusion, Instructor: Dr. Dinesh Devarajan, HPRC
Setting up environments for AI research and Software Development, Instructor: Dr. Thang Ha, HPRC
AI/ML in the Life Sciences, Instructor: Dr. Wesley Brashear, HPRC

Keynote Talk

The National AI Research Resource (NAIRR) Pilot

Katie Antypas, Senior Advisor for Cyberinfrastructure, National Science Foundation (NSF)

Bio: Katie is the Senior Advisor for Cyberinfrastructure at NSF. Prior to joining NSF Katie was at the National Energy Research Scientific Computing (NERSC) Center at Lawrence Berkeley National Laboratory for 17 years in a variety of roles including NERSC Division Deputy, Project Director for NERSC's large scale High Performance Computing system acquisitions, Director of Hardware and Integration of the Exascale Computing Project, Data Department Head and User Services Group Lead. Before coming to NERSC in 2006, Katie worked at the Flash Center at the University of Chicago on the FLASH code, a highly scalable, parallel, adaptive mesh refinement astrophysics application. She has an M.S. in Computer Science from the University of Chicago and a bachelors in Physics from Wellesley College.

Workshops

AI Fundamentals

Friday, May 15, 9:00 AM - 5:00 PM

Instructor: Dr. Yalong Pi, Texas A&M Institute of Data Science

This full-day workshop covers the essential neural network fundamentals and tools to apply deep learning models through hands-on computer vision and natural language processing exercises. The HPC cluster will host and accelerate a set of Jupyter notebooks for image classification, transfer learning, natural language tokenization and embedding, and translation transformer. Both TensorFlow and PyTorch libraries will be used to enhance the understanding of AI and HPRC. A joint TAMIDS and HPRC certification will be provided at the end of the workshop.

• Develop an understanding of concepts of neural networks and machine learning.
• Apply simple convolutional neural network (CNN) models and evaluate their performance.
• Understand the mathematical nature of tokenization and embedding for large language models.
• Explain what attention mechanisms, transformer models, and generative pretrained transformers (GPT).

• American sign language image classification with Tensorflow
• Dog breed image classification and transfer learning with PyTorch
• Text tokenization and embedding
• Attention mechanism, transformer architecture, GPT, and beyond

• Current ACCESS ID
• Basic Python skills

Introduction to GPU programming with OpenMP Offloading

Friday, May 15, 9:00 AM - 12:00 PM

Instructors: Dr. Lars Koesterke, Texas Advanced Computing Center

This workshop introduces OpenMP GPU offloading through practical examples in C/C++ and Fortran. Participants will learn how target regions execute on accelerators, how data is mapped and moved between host and device, and how OpenMP constructs such as teams, distribute, and loop are used to organize work on GPUs. The course also covers common mistakes, basic verification techniques, and introductory asynchronous offloading concepts.

• understand how OpenMP target regions execute on GPUs
• use key constructs for offload, including data mapping and loop/work distribution
• manage device data movement and persistence efficiently
• verify offload behavior and avoid common mistakes

• OpenMP offloading basics: target, execution model, and host/device behavior
• Data mapping and movement: implicit/explicit mapping, target data, enter/exit/update
• Parallel work distribution on GPUs: teams, distribute, parallel for, loop
• Debugging and performance basics: verification, common pitfalls, and asynchronous offload

• Active ACCESS ID
• Basic knowledge of C/C++ or Fortran is preferred

• prior OpenMP experience is recommended
• basic understanding of GPU execution and host/device memory is helpful
• familiarity with Python is helpful for participants without a strong background in compiled languages. It can provide an accessible entry point, and Python is expected to be supported in a future OpenMP standard

Generative AI for Protein Design: A Hands-On Introduction to RFdiffusion

Friday, May 15, 9:00 AM - 12:00 PM

Instructor: Dr. Dinesh Devarajan, Texas A&M High Performance Research Computing

This workshop provides hands-on introduction to diffusion-based generative models for protein design, with a focus on RFdiffusion. Participants will learn how to run RFdiffusion inference workflows on HPRC clusters. The session will introduce the computational workflow, strategies for generative design runs on HPC infrastructure.

• Understand how diffusion models such as RFdiffusion enable de novo protein design
• Learn how RFdiffusion inference workflows are executed on HPC systems

Topics:

• Set up and run RFdiffusion inference through guided, hands-on exercises
• Modify RFdiffusion input parameters (motifs, length, symmetry, structural constraints) to guide protein backbone generation
• Interpret and visualize RFdiffusion outputs

• Current ACCESS ID
• Linux/Unix skills

Setting up environments for AI research and Software Development

Friday, May 15, 1:00 PM - 4:00 PM

Instructors: Dr. Thang Ha, Texas A&M High Performance Research Computing

Have you ever found a GitHub repo that has some features relevant to your research but you could not follow the instructions to make it work on HPRC Linux supercomputers? Maybe you tried to follow the instructions to set up a some Python/Conda environment for such a repo and ended up locked out of your account because you filled up your home storage quota limit? Ever tried to install an R library package or compile a source code in C/C++/Fortran and bumped into errors involving missing libraries that you don't have sudo permission to install? If you encounter any of these issues, this workshop is for you!

By learning to set up environments for Python/R scripts and C/C++/Fortran software compilation on HPC, participants will be able to install the libraries and packages required for their R and Python scripts (e.g. PyTorch), compile source codes into executable programs, and run these scripts and programs on TAMU HPC systems. These skills will be relevant for researchers in a variety of fields, including those involved with AI/ML, molecular dynamics simulations, and materials science.

• Set up a Python virtual environment containing PyTorch, which is a widely-used python package for many common AI/ML workloads, such as Large Language Models (LLMs), using HPRC's in-house Python virtual environment manager "ModuLair", which is based on Python venv.
• Set up a Conda/Mamba environment, in case a given Python environment requires Conda and/or is too complicated to set up using Python venv.
• Set up an environment for running R scripts using HPRC's in-house R project environment management and software module "R_tamu".
• Set up a software compilation environment using a FOSS (GNU) toolchain and/or an Intel toolchain, compile source code into executable programs, and write and submit a Slurm job script to run those compiled programs on HPRC.

• Current ACCESS ID

AI/ML in the Life Sciences

Friday, May 15, 1:00 PM - 4:00 PM

Instructor: Dr. Wesley Brashear, Texas A&M High Performance Research Computing

This workshop explores how AI/ML has become a transformative force in biological research with hands-on applications used in modern life science workflows, from protein structure prediction and variant calling to image-based analysis. Participants will gain practical experience running tools such as AlphaFold3, AlphaFast, and DeepVariant on high-performance computing (HPC) systems, while also learning how to build and interpret basic deep learning models.

• Learn how Artificial Intelligence and Machine Learning (AI/ML) have played a role in past biological research
• Learn how current AI/ML and Deep Learning technologies are being incorporated into modern workflows
• Learn how to access AI/ML-enabled tools on HPRC systems
• Learn how to run and interpret AlphaFold 3 and AlphaFast
• Learn how to run Google’s DeepVariant using NVIDIA’s GPU-accelerated Parabricks software suite
• Learn how to build basic Convolutional Neural Networks for image recognition and classification

• History of AI/ML in biological research
• Running AlphaFold3/AlphaFast on an HPC system
• Running DeepVariant on an HPC system
• Building CNN’s in Jupyter Lab on an HPC system

• Current ACCESS ID
• Familiarity with Python
• Download the AlphaFold3 model parameters file: https://github.com/google-deepmind/alphafold3 (Requests to download the parameters can take 1 or more business days)

Research Computing Symposium Full Schedule (All times are CT)

Last Update: May 14, 2026

Posters

Best Poster Winner: Molecular Dynamics Simulations in Co-Assembly: Designing Novel Cancer Drug Nanocarriers and Uncovering the Interaction of Aβ with δ-COP

Authors: Anastasia Vlachou¹, Phanourios Tamamis^1,2
Departments: ¹Artie McFerrin Department of Chemical Engineering, Texas A&M University; ²Materials Science and Engineering, Texas A&M University

Predicting E. coli Protein Interactions Using AlphaPulldown on an Academic HPC Cluster

Author(s): Sarah A. Vastani, Joseph F. Carr, Angela M. Mitchell
Department: Biology, Texas A&M University

Reduced Order Model for Airway Geometry for Patients with Respiratory Distress

Authors: Syed Anas Nisar, Debjyoti Banerjee
Department: Mechanical Engineering, Texas A&M University

From High-Dimensional Atlas to Therapeutic Lead: Data-Intensive Mapping of Clonal Expansion in Lung Cancer

Authors: Abraheem Khouqeer^1,2, Tommy Chong³, Daniyaal Qazi², Woo Yon Chung¹, Faith Willis¹, John Epps¹, Li Qiang¹
Departments: ¹Mays Business School, Texas A&M University; ²College of Medicine, Texas A&M University; ³Perelman School of Medicine, University of Pensylvania

Trade-offs between Dorsal gradient robustness and Cactus lifetime

Authors: Anuj Girish Pradhan¹, Dr. Gregory T. Reeves^1,2
Departments: ¹Artie McFerrin Department of Chemical Engineering, Texas A&M University; ²Interdisciplinary Graduate Program in Genetics and Genomics, Texas A&M University

AI-Driven Yield Prediction in Rice and Soybeans: Transfer Learning Applications in Louisiana Breeding Programs

Authors: Sri Varenya Mudumba, Md Nafiul Islam, Thanos Gentimis
Department: Soil and Crop Sciences, Texas A&M University

ML-guided peptide inhibits microbial growth and biofilm formation of pseudomonas aeruginosa

Authors: Samavath Mallawarachchi, Aadhil Haq, Maria King, Sandun Fernando
Department: Biological and Agricultural Engineering, Texas A&M University

AI-Powered Cow Tracking System in Dairy Cattle

Authors: R. Neupane¹, S. Rajanna¹, Bhuwan Shreshtha², Nelson Rodriguez², S. Paudyal¹
Departments: ¹Animal Science, Texas A&M University; ²Aurora Organic Dairy, Dublin, TX

Parametric Sensitivity Analysis on Full-Scale Precast Bridge Piers with Grouted Splice Sleeve Connectors under Sequential Vehicle Impact and Seismic Loading

Authors: Jinghui Jiang, Andrew D. Sorensen, Ph.D., M.B.A., P.E.
Department: Architectural Engineering, Texas A&M University

Determination of structural and compositional changes in Neurospora crassa ΔeL31 ribosomes relative to wild-type using high-resolution cryo-EM

Authors: Sourima Banerjee¹, Joon Young Shin², Jirapat Thongchol², Gaya Yadav³, Teresa Lamb¹, Junjie Zhang², Deb Bell-Pedersen¹, Matthew S Sachs¹
Departments: ¹Biology, Texas A&M University; ²Biochemistry & Biophysics, Texas A&M University; ³Laboratory of Biomolecular Structure & Dynamics, Texas A&M University

Solid Electrolyte Interphase Formation between a Li-Metal Anode and an Ionic Liquid Electrolyte Using an Ab Initio Molecular Dynamics Approach

Authors: Hafsa Ashraf¹, Luis A. Selis², Jorge M. Seminario^1,2,3
Departments: ¹Chemical Engineering, Texas A&M University; ²Electrical and Computer Engineering, Texas A&M University; ³Materials Science and Engineering, Texas A&M University

A Digital Twin Model for South Texas Ecotourism Center Management

Authors: Bryn Shellenback¹, Amir Hossein Khazaei¹, Xuecong Fan²
Departments: ¹Performance, Visualization, & Fine Arts, Texas A&M University; ²Hopitality, Hotel Management and Tourism, Texas A&M University

Integrating AlphaFold and protein-protein docking to predict VLDLR usage by Madariaga virus and North American EEEV

Authors: Aadhil Haq¹, Samavath Mallawarachchi¹, Tereza Magalhaes², Sandun Fernando¹
Departments: ¹Biological and Agricultural Engineering, Texas A&M University; ²Entomology, Texas A&M University

Kinetics of vacancy-assisted reversible phase transition in monolayer MoTe₂

Authors: Fei Shuang, Daniel Ocampo, Reza Namakian, Arman Ghasemi, Poulumi Dey, Wei Gao
Department: Mechanical Engineering, Texas A&M University

A Vision-Language AI Framework for Quantitative and Standards-Aligned Bridge Corrosion Assessment

Authors: Yao-Teng Hu¹, Jian Tao², Arash Noshadravan¹
Departments: ¹Zachry Department of Civil and Environmental Engineering, Texas A&M University; ²Performance, Visualization & Fine Arts, Texas A&M University

Molecular Dynamics Insights into MPER-TM and Lipid Interactions of HIV-1 Broadly Neutralization Antibodies

Authors: Yue Qui¹, Kemin Tan², Yong Do Kwon³, Thomas Walz⁴, Ellis L. Reinherz^5,6, Mikyung Kim^5,7, Wonmuk Hwang^{1,8,9,10
Departments: 1Biomedical Engineering, Texas A&M University; 2Structural Biology Center, X-ray Science Division, Argonne National Laboratory; 3Vaccine Research center, National Institute of Allergy and Infectious Diseses, National Institutes of Health; 4Laboratory of Molecular Electron Microscopy, The Rockefeller University; 5Laboratory of Immunobiology, Medical Oncology, Dana-Farber Cancer Institute; 6Medicine, Harvard Medical School; 7Dermatology, Harvard Medical School; 8Materials Science and Engineering, Texas A&M University; 9Physics & Astronomy, Texas A&M University; 10Center for AI and Natural Sciences, Korea Institute for Advanced Study}

Learning to Reflect: Hierarchical Multi-Agent RL for CSI-Free mmWave Beam-Focusing

Authors: Hieu Le, Mostafa Ibrahim, Oguz Bedir, Jian Tao, Sabit Ekin
Departments: Electrical & Computer Engineering, Texas A&M University

The Impact of Selection and Drift on De Novo Gene Evolution and Retention

Authors: Catherine Chaison¹, Adekola Owoyemi², Matthew Marano³, Claudio Casola^2,3,4
Departments: ¹biochemistry & Biophysics, Texas A&M Univesity; ²Ecology and Conservation Biology, Texas A&M University;³Interdisciplinary Doctoral Program in Ecology and Evolutionary Biology, Texas A&M University; ⁴Interdisciplinary Graduate Program in Genetics & Genomics, Texas A&M University

Machine Learning Integrated in Wavelet Shrinkage

Authors: Vijini Lakmini¹, Dixon Vimalajeewa², Brani Vidakovic¹
Departments: ¹Statistics, Texas A&M University;²Statistics, University of Nebraska

Texas A&M Research Computing Symposium is supported in part by NSF award #2112356, ACSS: ACES - Accelerating Computing for Emerging Sciences and NSF award #2430335.