Mario Krenn

I am a research group leader of the Artificial Scientist Lab at the Max Planck Institute for the Science of Light (Theory Division).

I am excited about the potential of artificial intelligence-inspired and -augmented science, and how we can use algorithms in a more “creative” way. To make progress, I believe it will be important to learn what humans mean by crucial scientific concepts such as surprising, creativity, understanding or interest. I have created AIs for designing quantum experiments and hardware (several actually build in labs) and inspiring novel ideas for quantum technologies. (Part of this research has recently been summarized in a nice article in Scientific American and in a feature by the National Academy of Science). I also build autonomously semantic network from scientific publications, and use machine learning to predict and suggest personalized future research questions and ideas. In that sense, we use the machine as a source of inspiration to accelerate scientific progress. Ultimately, I want to create algorithms that help us to uncover the secrets of the Universe. More about the research philosophy and ideas can be read in this 2022 Nature Review Physics perspective.

More: e-Mail, Twitter, google scholar,arXiv, GitHub, youtube, full CV.

News

• 2024.03.27: Talk at ML for Quantum Physics workshop in Obergurgl, Austria – on artificial muses in science.
• 2024.03.18: Paper published in Phys. Rev. Research on an long-distance frustrated interference experiment.
• 2024.03.18: Talk at DPG Condensed Matter Section in Berlin on AI for designing physics experiments.
• 2024.03.15: Talk at DPG Atom, Molecules, Quantum Optics & Photonics in Freiburg on AI for designing physics experiments.
• 2024.03.05: New paper preprint on how to use virtual reality for understanding AI-generated ideas and designs in physics.
• 2024.02.28: Talk at Workshop for Epistemological Issues of Machine Learning in Science in Dortmund (Germany), about Artificial Muses in Physics.
• 2024.02.15: Paper on deep dreaming on quantum-graphs published in Machine Learning: Science & Technology.
• 2024.02.14: New paper preprint: Forecasting high-impact research topics via machine learning on evolving knowledge graphs. I believe this is a crucial step towards a big picture goal of artificial muses that can inspire new, interesting high-impact ideas.
• 2024.01.24: Talk at Workshop for Scientific Understand and AI in Leiden, Netherlands, about Artificial Muses in Physics.
• 2023.12.12: Paper on AI-driven design of 100 new (!) quantum experiments (for state generation, quantum communication, condensed matter physics, multi-photon control, etc) just published in Quantum, see the full PyTheus code package.
• 2023.12.08: New paper preprint: Digital Discovery of interferometric Gravitational Wave Detectors with Yehonathan Drori, Rana X Adhikari from Caltech and LIGO. Discovered 50 new detector designs (many are very exotic but superior) for supernovae, neutron star mergers, cosmological events, broad band detectors etc. See the Gravitational Wave Detector Zoo.
• 2023.12.07: Talk at the Google Research Science & AI Seminar on AI-driven discovery of new physics experiments, specifically new super-resolution microscopes.
• 2023.12.01: Talk at the Lingnan-Cambridge Workshop on AI in Science on concrete AI-inspired scientific discoveries in Cambridge, UK.
• 2023.12.01: Keynote talk at the Artificial Intelligence for Science of Science (AI4SciSci) workshop of IEEE ICDM 2023 [virtual], on predicting and suggesting future research directions with knowledge graphs and semantic networks (e.g. in quantum and AI).
• 2023.11.28: Participate in Panel Discussion about AI/GPT in Research & Higher Education at University of Vienna.
• 2023.11.15: Talk at MIT’s Optics and Quantum Electronics Seminar on AI-driven design of new (quantum) experiments.
• 2023.10.31: Our XLuminA paper on a JAX-based superfast simulator for automated discovery in superresolution microscopy got accepted as an Oral Presentation (with a score of 10/10: “10: Top 5% of accepted papers, seminal paper”) at NeurIPS AI4Science workshop.
• 2023.10.16: Paper published in Nature Machine Intelligence about the prediction of future research directions in Artificial Intelligence. This is a combination of all insights we gained in our Science4Cast AI-competition (within IEEE BigData 2021). See our GitHub for all details.
• 2023.10.13: New paper preprint, on AI-driven discovery for superresolution microscopy. XLuminA is an autodifferentiating discovery framework which can discover techniques involving STED microscopy and vector-beam based superresolution.
• 2023.10.12: Talk and panel discussion at National Academy of Science’s AI for Scientific Discovery workshop, Washington DC, USA.
• 2023.10.06: Talk about Artificial Muses in Quantum Physics at CIFAR Quantum Information Science Meeting, Banff (Calgary), Canada.
• 2023.10.03: Lecture about Artificial Muses in Physics at CIFAR Quantum Information Science Fall 2023 School, Banff (Calgary), Canada.
• 2023.09.22: Talk at IEEE Quantum Week: Quantum Algorithm Design Automation about AI-based design of quantum optics.
• 2023.09.14: New paper preprint: Deep Quantum Graph Dreaming: Deciphering Neural Network Insights into Quantum Experiments. Trying to understand what strategies neural networks learn to manipulate quantum states.
• 2023.09.13: Talk at the European Optical Society Annual Meeting 2023, Dijon, France, in the Focused Session for AI and Photonics, on AI designed quantum experiments.
• 2023.07.25: New paper preprint: Experimental Solutions to the High-Dimensional Mean King’s Problem. Using our digital discovery framework PyTheus, we discover and generalize strategies for experimental blueprints of a quantum communication protocol called “The Mean King Problem”. In Collaboration with the experimental quantum optics group of Ebrahim Karimi in Ottawa.
• 2023.07.18: Talk in the Theoretical Physics Seminar at University of Augsburg, Germany, on AI as a muse for new ideas in physics.
• 2023.07.06: Talk about Scientific Understanding with AI at the Annual Conference of the British Society for the Philosophy of Science, Bristol, UK.
• 2023.07.01: Paper published in Digital Discovery on the recent code advanced of the code-base of the 100% robust molecular string representation SELFIES, which has been adopted in machine-learning for chemistry and material science since we published it 3 years ago.
• 2023.06.30: Lecture about AI as a Muse in Science at the summer school “Machine Learning Summer School on applications in Science” in Krakau, Poland. Video of lecture online.
• 2023.06.26: Talk and participation at panel discussion “AI for Science” at the amazing International Conference for Science of Science and Innovation, Chicago, USA.
• 2023.05.04: Talk at the Vienna Bio Center (Max Peruty Labs) about AI as an artificial muse for new ideas in Science.
• 2023.04.21: Talk at the Philosophy of Contemporary and Future Science group (Lingnan University, Hongkong) about scientific understanding with AI: video.
• 2023.04.10: Talk at the Machine Learning and (Quantum) Physics Workshop in Obergurgl, Austria on artificial muses.
• 2023.04.10: New paper preprint: Quantum interference between distant creation processes.
• 2023.04.07: The group of Jianwei Wang (Peking University) has implemented a very large scale quantum graph in integrated photonics, published today in Nature Photonics. The bridge between quantum optics and graph theory has been discovered by us over several years and has now become the standard representation for our Artificial-Intelligence-based Discovery algorithms. Jianwei’s implementation is a new framework for quantum information processing with photons, and also brings our and others (past and future) theory and AI results into the real world. Exciting — congratulations!
• 2023.03.17: Talk at Tampere University, Finland in Physics Colloquium, on AI-inspired science.
• 2023.03.17: Paper published in Nature Communications about a new multi-photon interference, derived from a graph-theoretical representation (discovered using AI).
• 2023.02.22: Our research covered by dutch news paper NRC: Will the next Einstein be an AI?
• 2023.02.08: New paper preprint on code-details of SELFIES library for AI in chemistry.
• 2023.01.27: Talk at ICFO (Barcelona, Spain) on an Artificial Muse for physics.
• 2023.01.19: Talk at aQa Leiden (Netherlands) University on an Artificial Muse for physics.
• 2023.01.16: New paper preprint: Roadmap on Structured Light (our contribution: Artificial intelligence for structured waves).
• 2023.01.13: Paper published in Optica on the observation of a new multi-photon quantum interference effect, first proposed using Graph Theory for quantum optics.
• 2023.01.11: Talk at the ELLIS unconference 2023 (La Palma, Spain) on AI as a source of inspiration in physics.
• 2023.01.03: Invited Perspective article on “Artificial intelligence and machine learning for quantum technologies” published in APS Phys.Rev.A.
• 2022.12.15: Talk at University of Stuttgart, Quantum Info & Technology on Computer-Designed Quantum Experiments.
• 2022.12.03: Participating at panel discussion on “Philosophy of Science in the AI Era” at the ML&Physical science workshop at NeurIPS 2022.
• 2022.11.23: Talk at the New Frontiers in Machine Learning and Quantum workshop in Watterloo, Canada on an Artificial Muse in Physics (video online).
• 2022.11.11: Talk at QTML 2022 (Naeples, Italy) about an Artificial Muse in Physics.
• !!! 2022.10.19: First two publications of my group — and they are HUGE. Release of PyTheus, a highly-efficient open-source discovery framework for quantum optics. We showcase PyTheus to discover 100 (!!) diverse new quantum experiments (state generation, quantum communication, quantum measurements, quantum gates, quantum simulation), and use it to extract a new very surprising scientific concept in experimental quantum optics.
• 2022.10.17: Paper published in Cell’s Patterns about a robust representation of molecules for AI in chemistry, with 30 co-authors from 14 countries.
• 2022.10.13: Paper published in Quantum about logical AI for desinging quantum experiments.
• 2022.10.11: Paper published in Nature Review Physics On Scientific Understanding with Artificial Intelligence. A wild view into the future of how AI could contribute to the essentail aims of science.
• !!! 2022.10.04: Congratulations to my former PhD advisor Anton Zeilinger for winning the Nobel Prize in Physics 2022!
• 2022.10.04: New paper preprint on Predicting the Future of AI with AI.
• 2022.09.27: Talk at Galileo Galilei Institute (Florence) ML workshop on AI as an artificial Muse in Physics.
• 2022.09.22: Talk at ML for Natural Sciences workshop (University Hamburg) on AI as an artificial Muse in Physics.
• 2022.09.06: Talk at DPG Tagung on AI as an artificial Muse in Physics.
• 2022.08.09: New paper preprint on Artificial Intelligence in Quantum Technology.
• 2022.07.26: Paper published in Machine Learning: Science and Technology on a curious agent that explores the space of molecules.
• 2022.07.20: Work on interpretable VAE for Quantum Optics featured as Research Highlight in Nature Computational Science.
• 2022.07.06: Talk at University of Exeter (Quantum Non-Equilibrium Group) about AI-desinged Quantum Experiments.
• 2022.07.06: Talk at FAU Physics Colloquium about Artificial Scientists.
• 2022.06.24: Talk at ESA’s Advanced Concepts Team about Artificial Scientists.
• 2022.06.21: Paper published in Reviews of Modern Physics on Quantum indistinguishability by path identity and with undetected photons.
• 2022.06.16: Paper published in Nature Machine Intelligence on unsupervised learning of interpretable representations of quantum optics experiments.
• 2022.06.13: Talk at Bayer about Scientific Understanding from AI.
• 2022.06.06: Talk at meeting of Young German Physical Society about “Von Kuenstlicher Intelligenz zu Kuenstlichen Wissenschaftern”.
• 2022.05.26: Invited Talk at Photonics North 2022, Photonics&AI Session on Understanding through AI in Quantum Optics.
• 2022.05.22: Public Talk at “Lange Nacht der Forschung” about “Von Kuenstlicher Intelligenz zu Kuenstlichen Wissenschaftern“.
• !!! 2022.04.05: New paper preprint on Scientific Understanding with Artificial Intelligence. Perspective towards an essential aim of science – IMO crucial for the creation of a true Artificial Scientist.
• 2022.04.04: New paper preprint on SELFIES and the future of molecular string representations for AI in chemistry and material science (collaborations with 31 coauthors from 10 different countries).
• 2022.02.24: Paper published in Phys.Rev.Applied on the first experimental qutrit-GHZ state in a superconducting device.
• 2022.01.14: Talks of Science4Cast AI competition are online!
• 2021.12.23: New paper preprint on the observation of a non-local interference effect and its control with undetected photons. The interference effect was discovered using several years ago by AI, see a detailed cover about some background in Scientific American.
• 2021.12.20: Review on Quantum Indistinguishability by Path Identity accepted in Rev.Mod.Phys.
• 2021.12.17: Chaired the IEEE BigData Competition Science4Cast session, with so many amazing contributions and special talks! Talks on youtube soon.
• 2021.12.10: Talk at Perimeter ML seminar (video online!) on predicting the future of science.
• 2021.12.01: Talk at Quantum Theory group at FAU on (quantum-)computer-inspired quantum experiments.
• 2021.11.26: Paper published in Photonics on using Deep LSTM networks to predict complex quantum entanglement properties. Collaboration with group of Sepp Hochreiter, the inventor of LSTMs.
• 2021.11.11: Talk at the Bavarian Graduate School of Computational Engineering on predicting the future of science.
• 2021.11.10: Guest lecture at the University of Rochester on ML in chemistry.
• 2021.11.02: Talk at IBM Zurich on ML in chemistry.
• 2021.10.26: Print version of Scientific American’s cover of our work, see here.
• 2021.09.29: New paper preprint on the useage of logic and SAT solvers in the design of quantum experiments.
• 2021.09.07: New paper preprint on the internal worldview of deep generative models in quantum experiments.
• !!! 2021.09.01: Start of my research group at Max Planck Institute for the Science of Light (MPL, Theory Division).
• 2021.08.26: THESEUS paper published in PRX 🙂
• 2021.08.25: Annouced the Science4Cast Machine Learning competition (to predict future research in the field of ML&AI). Related to SemNet, but much larger.
• 2021.08.13: Mini-workshop on SELFIES (100% robust molecular string representation) celebrating our paper being the most downloaded and cited one in the journal MLST.
• 2021.07.07: Talk about computer-inspired quantum experiments and scientific understanding at Machine Learning for Quantum X workshop.
• 2021.07.02: Scientific American article about our work on computer-designed quantum experiments.
• 2021.07.01: Paper accepted in Phys.Rev.X on getting conceptual understanding though AI in quantum physics.
• 2021.06.30: Talk about computer-inspired quantum experiments and scientific understanding at Artificial Scientific Discovery workshop at MPL.
• 2021.06.09: Paper published in Machine Learning: Science&Technology on Deep Molecular Dreaming.
• 2021.05.31-2021.06.02: Co-Chair of AI session at Photonics North 2021, see poster.
• 2021.04.30: Talk at ETH Zurich about computer-inspired quantum experiments.
• 2021.04.28: Paper published in Quantum Science&Technology on designing quantum optics using quantum computers.
• 2021.04.23: Talk at 4th University of Florida Drug Discovery Symposium on SELFIES and ML in chemistry.
• 2021.04.20: Paper published in Chemical Science on extremely efficient generative model for chemistry.
• 2021.04.13: New paper preprint on first high-dimensional GHZ in superconducting device, on IBM’s quantum computer.
• 2021.03.28: New paper preprint on experimental observation of new quantum interference effect (discovered in 2017 using graph theory).
• 2021.03.19: Uploaded Video of SELFIES talk.
• 2021.03.04: Talk at Carnegie Mellon University, Scientific Machine Learning Webinar Series, on SELFIES and ML in chemistry.
• 2021.02.23: Talk at Max Planck Research Group Symposium about Computer-Inspired Quantum Research.
• 2021.02.17: Talk at Max Planck Institute for the Science of Light (MPL in Erlangen) about Computer-Inspired Quantum Research.
• 2021.02.02: New paper Data-Driven Strategies for Accelerated Materials Design in ACS Accounts of Chemical Research.
• 2021.01.11: New extensive blog post on SELFIES, a 100% robust graph representation.
• 2021.01.07: New paper preprint, a review of quantum indistinguishability by path identity.
• 2020.12.25: New youtube video on our Conjecture in Graph Theory.
• 2020.12.21: New paper preprint on Curiosity-driven exploration in deep molecular reinforcement learning.
• 2020.12.17: New paper preprint on Deep Molecular Dreaming.
• 2020.12.16: Misc – The Vienna Technical Museum has realized a suggestion by me and built a real-world Neural Network for the public to understand basic concepts in Machine Learning.
• 2020.12.15: New paper preprint on surprisingly efficient, combinatorial exploration in the molecular universe with SELFIES.
• 2020.11.29: New youtube video (my talk of Quantum2020) on conceptual understanding from inverse-design for quantum experiments.
• 2020.11.24: Talk at Q-Turn 2020 on conceptual understanding from inverse-design for quantum experiments.
• 2020.11.20: View&Perspective on paper using graph theory for designing efficient GHZ experiments in Frontiers of Physics.
• 2020.11.18: Talk in QML Semniar at National University of Singapour on predicting and suggesting new research in quantum physics.
• 2020.11.12: Talk in Quantum Nanoscience Seminar at TU Delft on Computer-Inspired Quantum Research.
• 2020.11.10: Talk at QTML 2020 (conceptual understanding from inverse-design for quantum experiments).
• 2020.10.28: Paper published in Machine Learning: Science and Technology on SELFIES!
• 2020.10.19: Talk at Quantum2020 on conceptual understanding from inverse-design for quantum experiments, and IOP Quantum2020, International Quantum Technology Emerging Researcher Award, Highly Commended, “in recognition of significant achievement and exceptional promise for future contributions to the field of quantum science and technology.“.
• 2020.10.27: New paper preprint on machine-learning inspired scientific intuition.
• 2020.10.09: Misc – Elon Musk liked this tweet.
• 2020.10.01: Paper published in PNAS of first Experimental Entanglement by Path Identity.
• 2020.09.29: Paper published in Nature Review Physics on Computer-inspired quantum experiments
• 2020.09.19: Talk at NetSci MMXX, Machine Learning In Network Science Symposium on predicting and suggesting new research directions in quantum physics.
• 2020.07.27: Paper published in PRL on computer-inspired quantum gate concepts.
• 2020.06.26: Misc – The International Astronomical Union (IAU) decided to rename a number of moon craters because of my tipps. See Philip Ball’s article and the coverage in the Nature Podcast.
• 2020.06.25: Paper published in Nature Review Physics on high-dimensional quantum entanglement.
• 2020.06.30: Talk at A.I. Socratic Circles on conceptual understanding from inverse-design for quantum experiments.
• 2020.06.25: Talk at Photonics Online Meetup #POM20Ju on conceptual understanding from inverse-design for quantum experiments.
• 2020.06.19: Talk at Max Planck Institute for the Science of Light (Erlangen) on conceptual understanding from inverse-design for quantum experiments.
• 2020.06.04: New paper preprint on quantum-computer designed quantum hardware.
• 2020.05.26-28: Co-Chair of AI session at Photonics North 2020, see poster.
• 2020.05.18: Paper published in Physica Scripta on open physics questions in future. Our contribution: automation of physics; my A.I. Melvin is official co-author.
• 2020.05.13: New paper preprint on conceptual understanding from inverse-design for quantum experiments.
• 2020.04.23: Paper published in ICLR on Genetic Algorithm+Deep Learning for molecules.
• 2020.04.23: Talk at A.I. Socratic Circles on SELFIES.
• 2020.04.13: Paper published in Optics Express on Light Propagation in Turbulence.
• 2020.03.13: Paper published in PRA on quantum experiments and hypergraphs.
• 2020.02.29: New paper preprint on Computer-inspired quantum experiments.
• 2020.01.14: Paper published in PNAS on predicting and suggestion future of quantum physics.
• 2020.01.08: News&Views in APS’s Physics on Scientific Insights from Neural Nets.

Professional Experience

• since 09.2021: Independent Research Group Leader (Artificial Scientist Lab) at Max Planck Institute for the Science of Light (MPL, Theory Division), Erlangen, Germany.
• 01.2019-08.2021: FWF Erwin Schrödinger Postdoctoral Fellow (group of Alan Aspuru-Guzik)
• University of Toronto (Department of Chemistry & Computer Science), Canada.
  • Vector Institute for Artificial Intelligence, Toronto, Canada.
  • (05.-09.2021: Institute for Advanced Research in Artificial Intelligence (IARAI), Vienna, Austria)
• 12.2017-12.2018: Postdoctoral Fellow (group of Anton Zeilinger)
  • University of Vienna (Faculty of Physics), Austria.
  • Institute for Quantum Optics and Quantum Information (IQOQI) Vienna, Austria.
• 10.2012 – 11.2017: Graduate research assistant (group of Anton Zeilinger)
  • University of Vienna (Faculty of Physics), Austria.
  • Institute for Quantum Optics and Quantum Information (IQOQI) Vienna, Austria.

Education

• 2012-11.2017: PhD in Physics at University of Vienna in the group of Anton Zeilinger:
  • Quantum experiments with spatial modes of photons in large real and Hilbert spaces.
  • Finished with distinction
• 2009-09.2012: Master studies at Vienna’s University of Technology.
  • Master thesis in the group of Anton Zeilinger.
  • Investigation of complex spatial mode structures of photons.
  • Finished with distinction.
• 2006-06.2009: Bachelor studies at Vienna’s University of Technology.
  • Bachelor thesis in the group of Manfried Faber.
  • SU(3)-Soliton-Fields on a Lattice