Jonathan E. Allen is a senior informatics researcher in the Global Security Computing Applications Division of the Computing Directorate at Lawrence Livermore National Laboratory (LLNL).  He leads a team focused on modeling and managing biomolecular systems data with applications to pathogen genome characterization and small molecule drug discovery.  He has extensive experience developing new software tools in metagenomics and machine learning for drug discovery, contributing to open source projects such as Livermore Metagenomics Analysis Toolkit (LMAT) and the ATOM Modeling Pipeline (AMPL).  Dr. Allen's recent work has earned him multiple LLNL Director’s Science and Technology’s Excellence in Publication Awards.

Ph.D. Computer Science, Johns Hopkins University, 2006

B.A. Computer Science, University of California, Santa Cruz 1996

https://scholar.google.com/citations?hl=en&user=DPUoo7AAAAAJ&view_op=list_works&sortby=pubdate

2022 LLNL Director's Science and Technology, Excellence in Publication

2021 LLNL Director's Science and Technology, Excellence in Publication

2020 LLNL Institutional Impact Award for COVID-19 therapeutic research

2016 Early and Mid-Career Recognition Program

2014 LLNL Gold Award: first transportable automated system for microbial forensic analysis

2009 LLNL Director's Science and Technology, Excellence in Publication

 

 

Selected general articles on related work:

·  5/20/2025: A guide to navigating AI chemistry hype – Chemical & Engineering News (ACS)

·  4/10/2025: Nucleotide database can improve microbe identification for science and medicine – Phys.org

·  2/25/2025: Big Ideas Lab Unlocks Secrets of Drug Discovery with Supercomputing and AI – HPCwire

·  8/9/2021: LLNL/ATOM Engagement with Purdue Introduces Students to Drug Design Modeling – HPCwire

·  11/13/2020: Building Safeguards for Genetically Engineered Microbes – Global Biodefense

·  3/14/2017: Reconstructing a Rabies Epidemic: Byte by Byte – YouTube

·  10/2016: High-Performance Computing Takes Aim at Cancer – Science & Technology Review (LLNL)

·  5/11/2015: Microbiomes raise privacy concerns – Nature News

·  10/2015: Two-Part Microbial Detection Enhances Bioidentification – Science & Technology Review (LLNL)

·  5/7/2014: First-of-a-kind supercomputer at Lawrence Livermore available for collaborative research – Phys.org

·  6/2009: Computational tools evolve to reveal patterns in flu data – Nature Medicine

·  3/28/2008: Distinguishing Artificial From Natural Is Possible, for Now – Wired

·  3/8/2008: On the trail of rogue genetically modified pathogens – Phys.org