Dr. Johannes Otterbach is the CTO of nyonic — an AI startup at the cutting edge of Foundation and Large Language Models. He has decades of experience in the field of AI, big data and computational sciences, aquired through his work as VP of Machine Learning Research at Merantix Momentum, researcher at OpenAI, quantum engineer at Rigetti, data scientist at LendUp / Mission Lane and software engineer at Palantir.
He received his Ph.D. in Physics from the University of Kaiserslautern, Germany while studying light particle interactions in the context [Many-Body properties of Dark-State Polaritons] in the group of Prof. Dr. M. Fleischhauer. Subsequently, he held a postdoctoral fellowship at the Harvard Quantum Optics Center
More details can be found in the (more or less up-to-date) resume
M. Schambach, D. Paul, J. Otterbach
Scaling Experiments in Self-Supervised Cross-Table Representation Learning
Accepted at TRL @ NeurIPS 2023
arxiv:2309.17339, openreview
J. Siems, K. Ditschuneit, W. Ripken, A. Lindborg, M. Schambach, J. S. Otterbach, M. Genzel
Curve Your Enthusiasm: Concurvity Regularization in Differentiable Generalized Additive Models
Accepted at IMLH @ ICML 2023
arxiv:2305.11475, openreview
O. Kondrateva, S. Dietzel, M. Schambach, J. S. Otterbach & B. Scheuermann
Filling the Gap: Fault-Tolerant Updates of On-Satellite Neural Networks Using Vector Quantization
Accepted at IFIP Networking 2023
A. Koenig, M. Schambach & J. S. Otterbach
Uncovering the Inner Workings of STEGO for Safe Unsupervised Semantic Segmentation
Accepted at SAIAD @ ICCV 2023
arxiv:2304.07314
J. Siems, M. Schambach, S. Schulze & J. S. Otterbach
Interpretable Reinforcement Learning via Neural Additive Models for Inventory Management
Accepted at AI4ABM @ ICLR 2023 workshop
arxiv:2303.10382, openreview
K. Ditschuneit, A. Frenk, M. Frings, V. Rudel, S. Dietzel & J. S. Otterbach
NAM-CAM: Neural-Additive Models for Semi-Analytic Descriptions of CAM Simulations
Accepted at the FAIM 2023
Link to the pdf
LEAM:AI consortium
Große KI-Modelle für Deutschland (pdf in German only)
On behalf of the Federal Ministry for Economic Affairs and Climate Action (BMWK)
J. S. Otterbach, C. M. Wilson, M. P. da Silva, N. A. Tezak, G. E. Crooks
Using a quantum processor unit to preprocess data
Patent number: US-11551127-B1
M. J. Reagor, B. R. Johnson, M. P. da Silva. J. S. Otterbach, N. A. Tezak, C. T. Rigetti
Utilizing multiple quantum processor unit (QPU) instances
Patent number: US-11521103-B1
R. S. Smith, N. C. Rubin, J. S. Otterbach
Quantum state blockchain
Patent number: US-11477015-B1
W. Lötzsch, S. Ohler & J. S. Otterbach
Learning the Solution Operator of Boundary Value Problems using Graph Neural Networks
Accepted at the 2nd AI4Science Workshop at the 39th ICML 2022
arxiv:2206.14092, openreview
S. Ohler, D. S. Brady, W. Lötzsch, M. Fleischhauer & J. S. Otterbach
Towards Learning Self-Organized Criticality of Rydberg Atoms using Graph Neural Networks
Accepted at the 2nd AI4Science Workshop at the 39th ICML 2022
arxiv:2207.08927, openreview
K. Ditschuneit & J. S. Otterbach
Auto-Compressing Subset Pruning for Semantic Image Segmentation
Accepted at GCPR 2022
arxiv:2201.11103, accepted version
D. Sreenivasaiah, J. Otterbach & T. Wollmann
MEAL: Manifold Embedding-based Active Learning
2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2021, pp. 1029-1037, doi: 10.1109/ICCVW54120.2021.00120
arxiv:2106.11858
S. v. Baußnern, J. Otterbach, A. Loy, M. Salzmann & T. Wollmann
DAAIN: Detection of Anomalous and Adversarial Input using Normalizing Flows
arxiv:2105.14638
J. Otterbach & T. Wollmann
Chameleon: A Semi-AutoML framework targeting quick and scalable development and deployment of production-ready ML systems for SMEs
arxiv:2105.03669
J. Otterbach, J. Ward, M. P. da Silva, N. C. Rubin
Selecting parameters for a quantum approximate optimization algorithm (QAOA)
Patent number: US-10846366-B1
C. M. Wilson & Rigetti Computing
Quantum Kitchen Sinks: An algorithm for machine learning on near-term quantum computers
arxiv:1806.08321
M. Reagor & Rigetti Computing
Demonstration of Universal Parametric Entangling Gates on a Multi-Qubit Lattice
Science Advances, 4, eaao3603 (2018)
J. Otterbach & Rigetti Computing
Unsupervised Machine Learning on a Hybrid Quantum Computer
arxiv:1712.05771
Coverage:
MIT Technology Review: A Startup Uses Quantum Computing to Boost Machine Learning
Superposition.com: Rigetti Computing Advances Machine Learning on a Quantum Computer
EdgyLabs.com: Introducing: Quantum Machine Learning
quantamagazine.org: Job One for Quantum Computers: Boost Artificial Intelligence (reprint in Wired)
Q. Wang, J. Otterbach, S. F. Yelin
Interacting in-plane molecular dipoles in a zig-zag chain
Phys. Rev. A 96, 043615
S. Caldwell & Rigetti Computing
Parametrically-Activated Entangling Gates Using Transmon Qubits
arXiv:1706.06562
J. Otterbach and M. Lemeshko
Dissipative Preparation of Spatial Order in Rydberg-Dressed Bose-Einstein Condensates
Phys. Rev. Lett. 113, 070401
F. Bariani, J. Otterbach, H. Tan, P. Meystre
Single-atom quantum control of macroscopic mechanical oscillators
Phys. Rev. A 89, 011801(R) (2014)
J. Otterbach, M. Moos, D. Muth, M. Fleischhauer
Wigner Crystallization of Single Photons in Cold Rydberg Ensembles
Phys. Rev. Lett. 111, 113001 (2013)
E. G. Dalla Torre, J. Otterbach, E. Demler, V. Vuletic, M. D. Lukin
Dissipative Preparation of Spin Squeezed Atomic Ensembles in a Steady State
Phys. Rev. Lett. 110, 120402 (2013)
S. D. Bennett, N. Y. Yao, J. Otterbach, P. Zoller, P. Rabl, M. D. Lukin
Phonon-induced spin-spin interactions in diamond nanostructures: application to spin squeezing
Phys. Rev. Lett. 110, 156402 (2013)
J. Ruseckas, V. Kudriasov, G. Juzeliunas, R. G. Unanyan, J. Otterbach, M. Fleischhauer
Photonic band-gap properties for two-component slow light
Phys. Rev. A 83, 063811 (2011)
A. V. Gorshkov, J. Otterbach, M. Fleischhauer, T. Pohl, M. D. Lukin
Photon-Photon Interactions via Rydberg Blockade
Phys. Rev. Lett. 107, 133602 (2011)
D. Petrosyan, J. Otterbach, and M. Fleischhauer
Electromagnetically induced transparency with Rydberg atoms
Phys. Rev. Lett. 107, 213601 (2011)
J. Otterbach, J. Ruseckas, R. G. Unanyan, G. Juzeliunas, and M. Fleischhauer
Effective magnetic fields for stationary light
Phys. Rev. Lett. 104, 033903 (2010)
Coverage:
A. V. Gorshkov, J. Otterbach, E. Demler, M. Fleischhauer, and M. D. Lukin
Photonic Phase Gate via an Exchange of Fermionic Spin Waves in a Spin Chain
Phys. Rev. Lett. 105, 060502 (2010)
R. G. Unanyan, J. Otterbach, M. Fleischhauer, J. Ruseckas, V. Kudriasov, and G. Juzeliunas
Spinor Slow-Light and Dirac particles with variable mass
Phys. Rev. Lett. 105, 173603 (2010)
J. Otterbach, R. G. Unanyan, M. Fleischhauer
Confining stationary light: Dirac dynamics and Klein tunneling
Phys. Rev. Lett. 102, 063602 (2009)
R. G. Unanyan, J. Otterbach, M. Fleischhauer
Confinement Limit of Dirac particles in scalar 1D potentials
Phys. Rev. A 79, 044101 (2009)
F. E. Zimmer, J. Otterbach, R. G. Unanyan, B. W. Shore, M. Fleischhauer
Dark-State Polaritons for multi-component and stationary light fields
Phys. Rev. A 77, 063823 (2008)
M. Fleischhauer, J. Otterbach, R. G. Unanyan
Bose-Einstein condensation of stationary-light polaritons
Phys. Rev. Lett. 101, 163601 (2008)
Hannah Schwär (Capital Magazin, 2023)
KI - plötzlich sehen wir ganz schön alt aus
Link
Impact of LLMs On Energy & Sustainability (youtube), NLP + Climate Tech Event by briink, AI Campus Berlin
Hanna Sissmann @ eco Verband
Mit der LEAM-Initiative unterstützen wir Unternehmen, große KI-Sprachmodelle zu trainieren
Link (German), Link (English)
KI Park Tech Talk - GPT-3 and OpenAI Dall-e
youtube
GovTech Session: Countering Manipulation & Fake News - AI, the Silver Bullet?
youtube
Gradient Dissent — The Weights & Biases Podcast with Lukas Biewald
Unlocking ML for Traditional Companies
Link
Applying AI in the Wild (pdf), Zentrum fuer Optische Quantentechnologien (ZOQ), University of Hamburg, March 2021
Philipp Lorenz @ Stiftung Neue Verantwortung
Die 1-Milliarde-Dollar-Organisation, die ethische KI entwickelt
Transcript (German only)
Unsupervised Machine Learning on a Hybrid Quantum Computer (pdf, pptx), Bay Area Quantum Computing Meetup, YCombinator, February 2018
Quantum Cloud Computing (pdf, pptx), TU Kaiserslautern, Germany, January 2018
OQaml - A OCaml-based QASM, (pdf, pptx), Bay Area Quantum Computing Meetup, Rigetti, August 2017
The Curse Of Dimensionality - Visualizing High-Dimensional Datasets using t-SNE, MemSQL, June 2016
AI - En Route to Passing Turing's Test, TU Kaiserslautern, April 2016
Data Science - WTH?, TU Kaiserslautern, April 2016
In this section, I want to collect pieces of work of other scientist with some honorable mention of mine. All credit goes to the authors and I claim no contribution to their work. It is merely intended as a ongoing list of topics I have had exchanges with the authors.
P. Hacker, A. Engel, M. Mauer
Regulating ChatGPT and other Large Generative AI Models
arxiv:2302.02337
M. Kiefer-Emmanouilidis, R. Unanyan, M. Fleischhauer, and J. Sirker
Evidence for Unbounded Growth of the Number Entropy in Many-Body Localized Phases
Phys. Rev. Lett. 124, 243601 (2020)
P. Lorenz & K. Saslow (2019)
Demystifying AI & AI Companies — What foreign policy makers need to know about the global AI industry
pdf