QCHALLenge
(Quantum-Classical Hybrid Optimization Algorithms for Logistics
and Production Line Management) is supported by the Federal
Ministry for Economic Affairs and Climate Action
with Partners
LMU, SAP, Siemens, BASF, BMW Group, and AQARIOS, 2023-2026.
Ko-Direktor der Arbeitsgruppe Technologische Wegbereiter und
Data Science der Plattform
Lernende Systeme (PLS) mit Förderung des Bundesministerium
für Bildung und Forschung (BMBF). 2022-
Invited presentation at Imperial College London in the series
"Imperial AI Talks", 2022.
QLindA
(Quantum Reinforcement Learning für industrielle Anwendungen) is
supported by the German Federal Ministry of Education and
Research with Partners Siemens AG, Fraunhofer IIS,
Ostbayerische Technische Hochschule Regensburg, and IQM Germany
GmbH, 2020-2024.
PI in ELISE (European Learning and Intelligent Systems
Excellence), a European Network of AI Excellence Centres, funded
under EU Horizon 2020, 2020-2024.
PyKEEN is our
new PyTorch-based library for knowledge graph embeddings (Project Page, Publication).
PyKEEN evolved out of a collaboration between the LMU, Uni Bonn
and TU Denmark. PyKEEN permits a comparative evaluation
of different embedding approaches, 2021.
PI in the Munich Center for Machine Learning (MCML) 2018-
Research
Interests
Our team has a
long tradition in machine learning for relational structured
domains. Currently our focus is on learning with (temporal)
knowledge graphs, where we also explore quantum computing
solutions. Our interest in cognitive
AI was the reason that we are increasingly exploring multimodal
data, such as texts, images, and videos. Foundation models are
becoming important for our work. Our
ultimate goal is to understand human level intelligence.
Medical Decision Support Systems, Precision Medicine
Reinforcement Learning and Multi-Agent Systems
Quantum Computing
Biography
Volker Tresp is a professor atLudwig Maximilian University of Munich(LMU). He received
his Diploma degree in physics from theUniversity of Göttingenin 1984 and M.Sc.,
M.Phil. and Ph.D. degrees fromYale
Universityin
1986 and 1989, respectively. During his Ph.D., he worked in
Yale’sImage Processing and Analysis Group(IPAG). In 1990, he
joinedSiemenswhere
he has been heading various research teams in machine learning.
In 1997, he became Siemens Inventor of the Year for his
innovations in neural networks research and in 2018 became
the first Siemens Distinguished Research Scientist. He
revolutionized steel processing by pioneering a novel Bayesian
neural network approach that cleverly integrated
real-world data with simulated data from a prior
solution.*** In 1994 he was a visiting scientist at theMassachusetts
Institute of Technologyin
theCenter for Biological and Computational
Learning, working with the
teams of Tomaso Poggio and Michael I. Jordan. He was co-editor
ofAdvances in Neural Information Processing
Systems 13. In 2011, he
was appointed professor in informatics at the LMU, where he
teaches a course on machine learning and where he is leading a
second research team. He is known for his work on Bayesian
machine learning, in particular theBayesian Committee Machineand his work onhierarchical learning with Gaussian
processes. TheIHRM, theSRM, SUNS, andRESCALare
milestones in representation learning for multi-relational
graphs. His team has been doing pioneering work on machine
learning with knowledge graphs, temporal knowledge graphs, and
scene graph analysis. The work on theTensor Brainreflects
his interest in mathematical models for cognition and
neuroscience. In 2020, he became a Fellow of theEuropean
Laboratory for Learning and Intelligent Systems(ELLIS). As
co-director (with Kristian Kersting and Paolo Frasconi), he
leads the ELLIS program "Semantic, Symbolic and Interpretable
Machine Learning".
***Renowned AI researcher, Geoff
Hinton, aptly termed this amalgamation of prior data as
"Priors without Prejudice." Subsequently,
Siemens engineers adeptly tackled the challenges of concept
drift and covariate shift, ensuring the model's adaptability to
changing conditions and environments. As a result, the project
achieved remarkable success, propelling the business unit to
become a global leader in the process industry. To this day, it
stands as one of the most significant achievements in machine
learning for the process industry. During the Golden Decade of the Conference
on Neural Information Processing Systems (then NIPS, now,
NeurIPS) from 1990-2000, I had 16 papers published in its
proceedings, more than any researcher working in Europe. It
was the most important machine learning conference of that
decade and is leading, even today. We were shallower, but
also deeper. Thanks to my co-authors: R. Hofmann, M.
Roescheisen, J. Hollatz, S. Ahmad, R. Neuneier, M.
Taniguchi, D. Ormoneit, H. G. Zimmermann, T. Briegel, J.
Hollmen