Journal Publications

B. Hartl, M. Levin,
“What does evolution make? Learning in living lineages and machines”, Opinion doi.org/10.31219/osf.io/r8z7c (2025).

L. Pio-Lopez*, B. Hartl*, M. Levin, (* authors contributed equally)
“Aging as a Loss of Goal-Directedness: An Evolutionary Simulation”, DOI:10.20944/preprints202412.2354 (2024).

B. Hartl*, Y. Zhang*, H. Hazan*, M. Levin, (* authors contributed equally)
“Heuristically Adaptive Diffusion-Model Evolutionary Strategy”, arXiv.2411.13420 (2024).

Y. Zhang*, B. Hartl*, H. Hazan*, M. Levin, (* authors contributed equally)
“Diffusion Models are Evolutionary Algorithms”, in preceedings of the ICLR (2025), also see arXiv.2407.09438 (2024).
Posts: Paper Tweet, Gonzo ML@substack,
Audio: Discussion by Carlos E. Perez, papersread.ai, apple podcasts, open spotify.

B. Hartl, M. Levin, A. Zöttl
“Neuroevolution of Decentralized Decision-Making in N-Bead Swimmers Leads to Scalable and Robust Collective Locomotion”, arXiv:2407.09438, in revision (2024),

B. Hartl, S. Risi, M. Levin,
“Evolutionary Implications of Self-Assembling Cybernetic Materials with Collective Problem-Solving Intelligence at Multiple Scales”, Entropy 26(7), 532, (2024), OSF Preprint,

B. Hartl, M. Mihalkovič, L. Šamaj, M. Mazars, E. Trizac, and G. Kahl,
“Ordered ground state configurations of the asymmetric Wigner bilayer system – revisited: an unsupervised clustering algorithm analysis”, The Journal of Chemical Physics 159, 204112 (2023), arXiv:2211.04985,

B. Hartl, M. Hübl, G. Kahl, and A. Zöttl,
“Microswimmers learning chemotaxis with genetic algorithms”, The Proceedings of the National Academy of Sciences 118 (19) e2019683118 (2021)

B. Hartl, S. Sharma, O. Brügner, S.F.L. Mertens, M.Walter, and G. Kahl,
“Reliable Computational Prediction of the Supramolecular Ordering of Complex Molecules under Electrochemical Conditions”, The Journal of Chemical Theory and Computation 16 (8), 5227-5243 (2020)

D. O. Krimer*, B. Hartl*, F. Mintert, and S. Rotter, (* authors contributed equally)
“Optimal control of non-Markovian dynamics in a single-mode cavity strongly coupled to an inhomogeneously broadened spin ensemble”, Physical Review A 96, 043837 (2017)

D.O. Krimer, B. Hartl, and S. Rotter,
“Hybrid Quantum Systems with Collectively Coupled Spin States: Suppression of Decoherence through Spectral Hole Burning”, Physical Reveview Letters 115, 033601 (2015)

Conferences and Events

• 1 Talk, 2 Poster: Physics of Life Conference, Harrogate, UK (2025)
• 2 Posters: Liquid Matter Conference, Mainz, Germany (2024)
• 1 Talks (2 accepted), 2 Posters: European Colloid and Interface Society Conference, Copenhagen, Denmark (2024)
• Talk: Austrian-Slovenian HPC Meeting, Bad Aussee, Austria (2024)
• ICML, Honolulu, Hawaii, USA (2023)
• IJCAI-ECAI, Vienna, Austria (2022)
• Talk & Poster: 11th Liquid Matter Conference, Prague, Czech Republic (2020/2021)
• VDSP-ESI Winter School 2020 on Machine Learning in Physics, Vienna, Austria (2020)
• Talk: Kurt Gödel’s Legacy: Does the future lie in the past? Vienna, Austria (2019)
• Poster: 14th International Conference on Quasicrystals, Kranjska Gora, Slovenia (2019)
• Questract: Workshop for Machine Learning and Reverse Engineering for Soft Materials, Leiden, Netherlands (2018)
• Talk: European Colloid and Interface Society Conference, Ljubljana, Slovenia (2018)
• Talk: Interfacing Machine Learning and Experimental Methods, Graz, Austria (2018)
• Poster: From Electrons to Phase Transitions – A ViCoM Conference, Vienna, Austria (2018)
• Poster: 10th Liquid Matter Conference, Ljubljana, Slovenia (2017)

Media Outreach

• Sub auspiciis doctoral graduation, 26.01.2022 (TU Wien News, brief interview, ITP News, ORF)
• Wie man als Einzeller ans Ziel gelangt (Reaching your life goals as a single-celled organism), see entire media coverage
• Wie Moleküle Mosaike bilden (How molecules form mosaics)
• derStandard user-foto of the week #5: “Love breaks walls”
• DOC fellowship ceremony 2017

Other Literature

Some Good Reads

• Machine Learning – kurz & gut by Chi Nhan Nguyen and Oliver Zeigermann
• An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
• The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman
• Neural networks and deep learning by Michael Nielsen
• Generative Deep Learning by David Foster
• Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto
• A high-bias, low-variance introduction to Machine Learning for physicists by P. Mehta et al
• The Book of Why: The New Science of Cause and Effect by J. Pearl and D. Mackenzie
• Technological Approach to Mind Everywhere: An Experimentally-Grounded Framework for Understanding Diverse Bodies and Minds by M. Levin
• Darwin’s agential materials: evolutionary implications of multiscale competency in developmental biology by M. Levin

Blogs and Blog-Posts

• David Ha’s Blog - Great stuff about evolutionary strategies in AI
• DINO and PAWS - Advancing the state of the art in computer vision with self-supervised Transformers and 10x more efficient training
• Same or Different? The Question Flummoxes Neural Networks. in Quantamagazine
• The Computer Scientist Training AI to Think With Analogies in Quantamagazine
• Hopfield Networks is All You Need by the Sepp Hochreiter-group
• The Illustrated Transformer by Jay Alammar

Channels and Lectures

• Michael Levin’s Academic Content - Mindblowing stuff
• Yannic Kilcher’s Channel - Awesome communication of ML literature
• Machine Learning for Physicists by Florian Marquardt