Volker Tresp

Professor
Ludwig Maximilian University of Munich

Distinguished Research Scientist
Siemens

E-mail (email): volker.tresp at lmu dot de

Prospective students: As LMU professor, I can supervise Ph.D. students.   Opening: We are looking for a PhD student for a publicly funded project involving Deep Learning, Reinforcement Learning, and Quantum Computing. We are looking for students who are looking for master thesis projects and who have a strong mathematical background! We are looking for a PhD student


Current Research Interests:



News | Research InterestsBiography  |  Students | Past Students | Awards and Honors | Tutorials | SoftwarePapers


News



Yesterday's News



Research Interests

My current research interests focus on Statistical Relational Learning, which combines machine learning with relational data models and first-order logic and enables machine learning in knowledge bases. An aspect of particular interest is that machine learning tasks such as classification and object recognition can be supported by rich background knowledge. 

More research interests: 

Biography

Volker Tresp is a professor at Ludwig Maximilian University of Munich (LMU) and Distinguished Research Scientist at Siemens Research. He received his Diploma degree in physics from the University of Göttingen in 1984 and M.Sc., M.Phil. and Ph.D. degrees from Yale University in 1986 and 1989, respectively. During his Ph.D., he worked in Yale’s Image Processing and Analysis Group (IPAG). In 1990, he joined Siemens where he has been heading various research teams in machine learning. In 1994 he was a visiting scientist at the Massachusetts Institute of Technology in the Center for Biological and Computational Learning, working with the teams of Tomaso Poggio and Michael I. Jordan. He was co-editor of Advances in Neural Information Processing Systems (NIPS) 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 and representation learning for multi-relational graphs.   In 2020, he became a Fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS). As co-director, he leads the ELLIS program "Semantic, Symbolic and Interpretable Machine Learning".



Students

Former Ph.D. Students

Selected Former Master Students, Interns and Visiting Students and Postdocs


Awards and Honors:


Tutorials



Software


Books

Papers

2022
2021
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009 2008 2007 2006 2005  2004
2003
2002

2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991 1990