Overview

This workshop will explore Artificial Intelligence and Statistical Learning algorithms and techniques for biological applications. The objective of this workshop is to bring together LANL scientists from the various divisions to present the latest AI-driven biology LANL research efforts and to identify challenges and future directions in applying AI in biological research.

This is an open in-person workshop; no sign-up is required. We will also send the weblink to those who wish to connect remotely closer to the workshop date.

Dates: August 26th and August 27th

Location: CNLS conference room, LANL

Sponsor: Center for Nonlinear Studies (CNLS)

Email: Email ai-bio@lanl.gov to get a Microsoft Teams weblink (different for each day) if you wish to connect remotely.

Topics

Tentative schedule

Abstracts are here.

Day 1 (August 26th), Morning Session

Time Speaker Title
8.50 - 9.00 Intro  
9.00 - 9.30 Nick Generous AI and Biological Risk
9.30 - 10.00 Carrie Manore Using a hybrid approach of AI and mechanistic models to predict climate change impact on infectious diseases
10.00 - 10.30 Morgan Gorris Projecting the geographical distribution of mosquitoes in response to climate change
10.30 - 11.00 Lauren VanDervort Statistical Bias and Predictive Modeling
11.00 - 11.30 Discussion Session  

Day 1 (August 26th), Afternoon Session

Time Speaker Title
1.00 - 1.30 Sandrasegaram Gnanakaran Prediction of virus-host protein-protein interactions
1.30 - 2.00 Bin Hu Learning the language of proteins and predicting the impact of mutations
2.00 - 2.30 Blake Hovde NLP trained on biological literature, for protein and plasmid analysis
2.30 - 3.00 Jason Gans A cross-validation framework for data clustering by protein sequence similarity
3.00 - 3.30 Michael Wall Some Novel Applications for Molecular-Dynamics Simulations of Biomolecules
3.30 - 4.00 Discussion Session  

Day 2 (August 27th), Morning Session

Time Speaker Title
9.00 - 9.30 Manish Bhattarai DNA Breathing Integration with Deep learning Foundational Model Advances
9.30 - 10.00 Juston Moore Local Latent Space Bayesian Optimization for Drug Discovery
10.00 - 10.30 Shounak Banerjee Learning Growth Curves: Algae Crops vs Harmful Algae Blooms
10.30 - 11.00 Sayera Dhaubhadel Suicide veteran predictions using transfer learning
11.00 - 11.30 Discussion Session  

Day 2 (August 27th), Afternoon Session

Time Speaker Title
1.00 - 1.30 Sara del Valle Harnessing Large-Scale Unstructured Data to Understand Public Perception
1.30 - 2.00 Casey Gibson Improving ML based infectious disease forecasting through mechanistic constraints
2.00 - 2.30 Yen Ting Lin Bayesian inference for SARS-CoV-2 pandemic
2.30 - 3.00 Ruian Ke A graph theory and machine learning approaches for identifying rapidly expanding SARS-CoV-2 lineages using early genetic data
3.00 - 3.30 Qianying Lin Model-based Deep Learning Inferential Framework for Genomic Reassortment
3.30 - 4.00 Discussion Session  
4.00 - 4.15 Closing Remarks  

Organizers