Jan-Peter Calliess

Categories: 31 May 2016

Mini Bio

Since November 2014 I have been a research associate at the Engineering Department at the University of Cambridge.
Predominantly working at the intersection of machine learning and control, I am a joint member of both the Machine Learning Group and the Control Group.

I am working within the Autonomous and Intelligent Systems Partnership (AISP) and am grateful of having received funding from EPSRC and Schlumberger. Furthermore, I am working in the area of computational mechanism design and am grateful for an award in support of this work from EPSRC-NCSML.

Between 2011 and 2014, I was a DPhil student in the

Machine Learning Research Group at the University of Oxford. Supervised by Stephen Roberts and Mike Osborne, I was funded via an EPSRC studentship
and part of the Orchid project. My thesis covered a variety of AI-related areas including machine learning,
control and multi-agent coordination.

Before coming to Oxford I worked as a freelance consultant for
IOSB, Fraunhofer Gesellschaft and start-up M2M as well as for
ISAS, Karlsruhe Institute of Technology (2009, 2010). Furthermore, I have been a member
of the German Airforce where I trained to become an officer over many years
since 2003.

I completed my undergraduate studies (Diplom) at the University of Karlsruhe
in Germany in computer science, graduating with a Diplom (equiv. of B.Sc. + M.Sc.) in
2008. The university has meanwhile merged with Forschungszentrum Karlsruhe
and has become Karlsruhe Institute of Technology.

While being a student at Karlsruhe I stayed twice
at SCS, Carnegie Mellon University (2006, 2007) working
with Tanja Schultz and Geoff Gordon as a visiting researcher on
projects related to brainwave recognition and multi-agent coordination,
respectively. Both my undergraduate theses evolved from the work I did there.


Contact information

Jan-Peter Calliess, DPhil
Research Associate

Computational and Biological Learning 

and Control Group
Department of Engineering,
University of Cambridge
Trumpington Street
United Kingdom
jpc73 “at” cam “dot” ac “dot” uk

Supervisors: Jan Maciejowski, Carl Edward Rasmussen


My research interests include topics in machine learning, computational mechanism design, multi-agent coordination, probabilistic inference, control and dynamic systems.

PAPERS  (updates to follow soon)


    • J. Calliess, Lipschitz Optimisation for Lipschitz Interpolation. To appear in Proc. of the ACC, 2017.
    • J. Calliess, N. Korda, G. J. Gordon. A Distributed Mechanism for Multi-Agent Convex Optimisation and Coordination with No-Regret Learners, Workshop on Learning, Inference and Control of Multi-Agent Systems, NIPS, 2016.
    • J. Calliess. Bayesian Lipschitz Constant Estimation and Quadrature, Workshop on Probabilistic Integration, NIPS, 2015.
    • J. Calliess, M. Osborne and S. J. Roberts. Conservative collision prediction and avoidance for
      stochastic trajectories in continuous time and space. Proc. Autonomous Agents and Multi-agent Systems (AAMAS), 2014.
    • J. Calliess A. Papachristodoulou and S. J. Roberts. Stochastic processes and feedback-linearisation for online identification and Bayesian adaptive control of fully-actuated mechanical systems, WS- Advances in Machine Learning for Sensorimotor Control, NIPS, 2013.  (Also submitted to Arxiv)
    • J. Calliess, M. Osborne and S. J. Roberts. Nonlinear adaptive hybrid control by combining Gaussian process system identification with classical control laws, WS- Novel Methods for Learning and Optimization of Control Policies and Trajectories for Robotics, ICRA, 2013.
    • J. Calliess and S. J. Roberts. Multi-agent planning with mixed-integer programming and adaptive interaction constraint generation. (Extended Abstract), Symposium on Combinatorial Search (SOCS), 2013.
    • J. Calliess, M. Osborne and S. J. Roberts. Towards auction-based multi-agent collision-avoidance under continuous stochastic dynamics. Presented at workshop: Markets, Mechanisms, and Multi-Agent Models — Examining the Interaction of Machine Learning and Economics, (ICML 2012).
    • D. Lyons, J. Calliess and U. Hanebeck. Chance-constrained Model Predictive Control for Multi-Agent Systems. Proc. of the American Control Conference (ACC 2012).
    • J. Calliess, D. Lyons and U. Hanebeck. Lazy auctions for multi-robot collision avoidance and motion control under uncertainty. LNAI 7068, Springer, 2011.
    • J. Calliess, M. Mai, S. Pfeiffer. On the Computational Benefit of Tensor
      Separation for High-Dimensional Discrete Convolutions. Multidimensional Systems and Signal Processing, Springer, 2010.
    • J. Calliess. On Fixed Convex Combinations of No-Regret
      Learners. 6th International Conference on Machine Learning and Data Mining
      in Pattern Recognition. In LNAI 5632, Springer, 2009.
    • S. Pfeiffer, M. Mai, W. Globcke, J. Calliess. On
      generalized separation and the speed-up of local operators on
      multi-dimensional signals. 6th International Workshop on
      Multidimensional (nD) Systems (NDS ’09). (IEEE-XPLORE).
    • A. Porbadnigk, M. Wester, J. Calliess, T. Schultz. EEG-based Speech Recognition – Impact of Temporal Effects. International Conference on Bio-inspired Systems and Signal Processing, Biosignals 2009.
    • J. Calliess and G. J. Gordon. No-regret Learning and a
      Mechanism for Distributed Multiagent Planning. Proc. of the 7th International
      Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2008.


  • J. Calliess, Lazily Adapted Constant Kinky Inference for Nonparametric Regression and Model-Reference Adaptive Control, arXiv:1701.00178, 2016.
  • J. Calliess. Conservative decision-making and inference in uncertain dynamical systems. DPhil thesis. University of Oxford, 2014.
  • J. Calliess, M. Osborne and S. J. Roberts. Towards optimization-based multi-agent collision-avoidance under continuous stochastic dynamics. Presented at AAAI-2012, Workshop on Multiagent Pathfinding, Toronto, Canada, 2012.
  • J. Calliess, D. Lyons and U. Hanebeck. Lazy auctions for
    multi-robot collision avoidance and motion control under
    uncertainty. Technical Report. No: PARG-11-01. University of Oxford.
    2011. (Extended version of workshop publication above).
  • J.-P. Calliess, On Fixed Convex Combinations of No-Regret
    Learners. Technical Report. Machine Learning Dept., Carnegie Mellon
    University, 2008.
  • J.-P. Calliess, and G. J. Gordon. No-Regret Learning and a
    Mechanism for Distributed Multi-agent Planning. Technical Report.
    Machine Learning Dept., Carnegie Mellon University, 2008.
    (Long version of conference publication
  • J.-P. Calliess. Diplomarbeit. No-regret Learning and 
    Market-based Multiagent Planning. IES, Fakultaet fuer Informatik, Universitaet Karlsruhe. September 2007

Working papers and work under review

  • J. Calliess et. al.. Lazily Adapted Kinky Inference and Online Control. Under Review , 2016.
  • D. Limon, J. Calliess and  J. Maciejowski. Robust Data-Based Model-Predictive Control. Under Review, 2017.
  • J. Calliess, On a class of consistent learning methods on translation-invariant groups endowed with pseudo-metrics. Working paper , 2016.
  • J.Calliess, Nathan Kordan and Geoffrey Gordon. No-regret learning and a mechanism for distributed convex optimisation and coordination. Under Review. , 2016.


Reviewer for Multidimensional Systems and Signals, ICRA, Automatica, CDC, IROS, ACC, Transactions on Automatic Control, NIPS and ICML.