Interdisciplinary AI Journal Club

Welcome to the LSU AI Journal Club (AI JC), a vibrant interdisciplinary community where the Physics and Astronomy Department, the Department of Mathematics, and the Computer Science Department come together to explore the transformative potential of Artificial Intelligence in scientific data analysis. 

Whether you are a seasoned researcher or new to the field, AI JC provides a platform to explore the latest AI trends, discuss groundbreaking papers, and expand your knowledge through engaging discussions and presentations. Join us to learn, share, and contribute to the dynamic world of AI in science. 

 

Spring 2026 Hands-on collaborative ML data Project

This semester, we are trying a new format for the AI Journal Club. We have selected an overarching theme that bridges Mathematics, Physics and Biology—partial differential equations (PDEs) and machine learning. Together, we will explore how machine-learning techniques can be used to approach PDE-based problems, discussing both the theoretical foundations and practical strategies. Participants will work in interdisciplinary groups to apply these methods to real, scientifically relevant problems of shared interest.

Location

Nicholson Hall, Room 335

Access the live streaming on Zoom (zoom link, Meeting ID: 926 5063 9854)

Calendar 

All appointments for the Spring semester 2026 will be on Friday at 10:00-11:00 AM.

Click the semester you’d like to expand to view the expected schedule.

Date Activities Post Meeting Notes & Agenda
Jan 23 Intro to PDEs and ML Shawn introduced 3 main PDE-related problems that are currently being approached with machine learning: solving complex PDE parameters, fitting data given known PDEs plus additional uknown function, and interpolating parametric PDEs.
Jan 30 Discussion project 1: Astrophysics Presentation and discussion of the first proposed project: Solving PDE of particle transport in gamma-ray burst jets.New: Discussed material will be shared in this box (to get access reach out to Dr. M. Missiaggia)
Feb 6 Discussion project 2: Nuclear Physics TBD
Feb 13 Discussion project 3: Biological Radiation Physics TBD
Feb 20 TBD  
Feb 27 TBD  

 

Date Activities
Sep 11
  • Kick off / Intro
  • Selection of a Data Challenge / Project
  • Set-up of GitHub repo
Sep 25
  • Read the data and inspect the dataset
  • Discuss the initial NN design
Oct 9
Oct 23 Collaborative Session
  • Implemented the DataLoader class
Nov 6 Collaborative Session
  • Implemented the LeNet architecture
  • Defined the accuracy monitor (to monitor loss and accuracy through the epochs)
Nov 20 Collaborative Session
  • Final results reported in the repo's README
  • Discussed next semester's activity

 

Date Activities Discussion Topics
Jan 23 Kick off JC + Invited seminar Icebreaker / (tentative: Variational Autoencoders)
Feb 6 Regular AI-JC 
  • WAN Discretization of PDEs: Best Approximation, Stabilization, and Essential Boundary Conditions
  • Storm surge modeling in the AI era: Using LSTM-based machine learning for enhancing forecasting accuracy
  • Graph construction on complex spatiotemporal data for enhancing graph neural network-based approaches
Feb 20 Regular AI-JC / Invited seminar
  • Generative Modeling by Estimating Gradients of the Data Distribution
  • Efficiently Modeling Long Sequences with Structured State Spaces
Mar 6 Invited seminar Physics-informed NN (by Noah Thompson, LSU)
Mar 20 Regular AI-JC / Invited seminar
Apr 3 Invited seminar Machine Learning for Optical Communications and Technology (by Manon Bart Tulane University)
Apr 17 Invited seminar Seminar on Topological Deep Learning
May 1 Regular AI-JC / 

 

Date Activities Discussed Topics
Sept 25  Kick off JC  Icebreaker topics: why do we care about AI
Oct 9 Invited Seminar Presentation by James Ghawaly: An overview of ML techniques for data analysis
Oct 23 Regular AI-JC
Nov 6 Regular AI-JC
  • Topological Autoencoders
  • A Deep Learning Approach for Active Anomaly Detection of Extragalactic Transients 
Nov 20 Invited Seminars and Discussion
  • Presentation by Dr. Wang – Dep of Math LSU: KL Divergence insights
  • Presentation by Dr. Cibrario – University of Torino (Italy): A regression problem: dealing with angles
  • Statistical method scDEED for detecting dubious 2D single-cell embeddings and optimizing t-SNE and UMAP hyperparameters

 

 

The AI-JC Organizing Team

  • Michela Negro - Physics & Astronomy
  • Marta Missiaggia - Physics & Astronomy
  • James Ghawaly - Computer Science
  • Xiaoliang Wan - Mathematics
  • Shawn Walker - Mathematics
  • Maganizo Kapita - Mathematics (Grad Student)
  • Chakradhar Rangi - Physics & Astronomy (Grad Student)
  • Phong Dang - Physics & Astronomy (Grad Student)

Contact the mailing list

To post to the AI-JC list, send your message to ai-club@mail.cct.lsu.edu.

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