Helgi I. Ingólfsson has a B.S. degree in Computer Science from the University of Iceland and a Ph.D. degree in Computational Biology and Medicine from Weill Cornell Medical College. He worked as a postdoctoral fellow at the University of Groningen and joined Lawrence Livermore National Laboratory (LLNL) as a Staff Scientist in 2016.

His research focuses on the organization of cellular membranes, lipid–protein interactions, and how small molecules alter membrane properties and protein function, with applications to membrane-mediated signaling and cancer biology. He combines experimental approaches with atomistic, coarse-grained, and multiscale molecular dynamics simulations to investigate these processes. His methodological contributions include developing the Martini coarse-grained force field, expanding its lipidome, building tools for realistic membrane simulations, and developing the Multiscale Machine-learned Modeling Infrastructure (MuMMI). His current work integrates machine learning with simulations to bridge length and time scales, generate protein conformations for pathway discovery, and automate large simulation campaigns. He also develops agentic workflows that connect scientific questions with simulation setup, execution, and analysis.

Postdoc, Biophysics / Molecular dynamics, 2011-2016, University of Groningen, Groningen, the Netherlands

Ph.D., Computational Biology and Medicine, 2010, Weill Cornell Medical College, New York, NY

M.S., Computer Science, 2005, Cornell University, Ithaca, NY

Software engineer, 2001-2003, deCODE Genetics, Reykjavik, Iceland

B.S., Computer Science, 2002, University of Iceland, Reykjavik, Iceland

For a full publication list see google Scholar profile.

Hsu T., K. Georgouli, M. Jones, … H.I. Ingólfsson and P.-T. Bremer. 2026. Guided protein structure generation for pathway discovery: a showcase for RAF dimerization. Machine Learning: Science and Technology, doi: 10.1088/2632-2153/aea04e.

Ai K., H. Miao, K. Tang, … H.I. Ingólfsson, … and S. Liu. 2026. SciVisAgentBench: A Benchmark for Evaluating Scientific Data Analysis and Visualization Agents. IEEE Transactions on Visualization and Computer Graphics (IEEE VIS 2026; accepted), preprint doi: 10.48550/arXiv.2603.29139.

Levine D.S., N. Liesen, L. Chua, … H.I. Ingólfsson, … and E.R. Antoniuk. 2025. The Open Polymers 2026 (OPoly26) Dataset and Evaluations. arXiv preprint (revised 2026), doi: 10.48550/arXiv.2512.23117.

Pedersen K.B., H.I. Ingólfsson, D.P. Ramirez-Echemendia, L. Borges-Araújo, … and S.J. Marrink. 2025. The Martini 3 Lipidome: Expanded and Refined Parameters Improve Lipid Phase Behavior. ACS Central Science, doi: 10.1021/acscentsci.5c00755.

Georgouli K., R.R. Stephany, J.O.B. Tempkin, … H.I. Ingólfsson and P.-T. Bremer. 2024. Generating Protein Structures for Pathway Discovery Using Deep Learning. Journal of Chemical Theory and Computation, 20:8795–8807, doi: 10.1021/acs.jctc.4c00816.

Bhatia H., F. Aydin, T.S. Carpenter, … H.I. Ingólfsson, … and F.H. Streitz. 2023. The confluence of machine learning and multiscale simulations. Current Opinion in Structural Biology, 80:102569, doi: 10.1016/j.sbi.2023.102569.

Stanton L.G., T. Oppelstrup, T.S. Carpenter, H.I. Ingólfsson, … and J.N. Glosli. 2023. Dynamic density functional theory of multicomponent cellular membranes. Physical Review Research, 5:013080, doi: 10.1103/PhysRevResearch.5.013080.

Bhatia H., J.J. Thiagarajan, R. Anirudh, … H.I. Ingólfsson, … and P.-T. Bremer. 2022. A biology-informed similarity metric for simulated patches of human cell membrane. Machine Learning: Science and Technology, 3:035010, doi: 10.1088/2632-2153/ac8523.

Bhatia H., J.J. Thiagarajan, R. Anirudh, T.S. Jayram, T. Oppelstrup, H.I. Ingólfsson, F.C. Lightstone, P.-T. Bremer. 2022. A Biology-Informed Similarity Metric for Simulated Patches of Human Cell Membrane. Machine Learning: Science and Technology, 3:2632-2153, doi: 10.1088/2632-2153/ac8523.

Ingólfsson H.I., C. Neale, T.S. Carpenter, R. Shrestha, … and F.H. Streitz. 2022. Machine learning–driven multiscale modeling reveals lipid-dependent dynamics of RAS signaling proteins. Proceedings of the National Academy of Sciences, 119, doi: 10.1073/pnas.2113297119.

Bhatia H., T.S. Carpenter, H.I. Ingólfsson, G. Dharuman, … and P.-T. Bremer. 2021. Machine-learning-based dynamic-importance sampling for adaptive multiscale simulations. Nature Machine Intelligence, 3:401–409, doi: 10.1038/s42256-021-00327-w.

Ahn D.H., N. Bass, A. Chu, … H.I. Ingólfsson, … and M. Taufer. 2020. Flux: Overcoming scheduling challenges for exascale workflows. Future Generation Computer Systems, 110:202–213, doi: 10.1016/j.future.2020.04.006.

Zhang X., S. Sundram, T. Oppelstrup, … H.I. Ingólfsson, … and J.N. Glosli. 2020. ddcMD: A fully GPU-accelerated molecular dynamics program for the Martini force field. The Journal of Chemical Physics, 153:045103, doi: 10.1063/5.0014500.

Di Natale F., H. Bhatia, T.S. Carpenter, C. Neale, … and H.I. Ingólfsson. 2019. A Massively Parallel Infrastructure for Adaptive Multiscale Simulations: Modeling RAS Initiation Pathway for Cancer. In Supercomputing ’19: The International Conference for High Performance Computing, Networking, Storage, and Analysis, doi: 10.1145/3295500.3356197.

Marrink S.J., V. Corradi, P.C.T. Souza, H.I. Ingólfsson, … and M.S.P. Sansom. 2019. Computational Modeling of Realistic Cell Membranes. Chemical Reviews, 119:6184–6226, doi: 10.1021/acs.chemrev.8b00460.

Melo M.N., C. Arnarez, H. Sikkema, N. Kumar, … and H.I. Ingólfsson. 2017. High-throughput simulations reveal membrane-mediated effects of alcohols on MscL gating. Journal of the American Chemical Society, 139:2664–2671, doi: 10.1021/jacs.6b11091.

Ingólfsson H.I., T.S. Carpenter, H. Bhatia, P.-T. Bremer, … and F.C. Lightstone. 2017. Computational Lipidomics of the Neuronal Plasma Membrane. Biophysical Journal, 113:2271–2280, doi: 10.1016/j.bpj.2017.10.017.

Ingólfsson H.I., C. Arnarez, X. Periole and S.J. Marrink. 2016. Computational ‘microscopy’ of cellular membranes. Journal of Cell Science, 129:257–268, doi: 10.1242/jcs.176040.

Wassenaar T.A., H.I. Ingólfsson, R.A. Böckmann, D.P. Tieleman and S.J. Marrink. 2015. Computational Lipidomics with insane: A Versatile Tool for Generating Custom Membranes for Molecular Simulations. Journal of Chemical Theory and Computation, 11:2144–2155, doi: 10.1021/acs.jctc.5b00209.

Ingólfsson H.I., M.N. Melo, F.J. van Eerden, C. Arnarez, … and S.J. Marrink. 2014. Lipid Organization of the Plasma Membrane. Journal of the American Chemical Society, 136:14554–14559, doi: 10.1021/ja507832e.

Ingólfsson H.I., P. Thakur, K.F. Herold, E.A. Hobart, … and O.S. Andersen. 2014. Phytochemicals Perturb Membranes and Promiscuously Alter Protein Function. ACS Chemical Biology, 9:1788–1798, doi: 10.1021/cb500086e.

Ingólfsson H.I., C.A. Lopez, J.J. Uusitalo, D.H. de Jong, … and S.J. Marrink. 2014. The power of coarse graining in biomolecular simulations. WIREs Computational Molecular Science, 4:225–248, doi: 10.1002/wcms.1169.

Ingólfsson H.I. and O.S. Andersen. 2011. Alcohol effects on lipid bilayer properties. Biophysical Journal, 101:847–855, doi: 10.1016/j.bpj.2011.07.013.

Ingólfsson H.I. and O.S. Andersen. 2010. Screening for small molecules’ bilayer-modifying potential using a gramicidin-based fluorescence assay. Assay and Drug Development Technologies, 8:427–436, doi: 10.1089/adt.2009.0250.