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
Cambridge
CB2 1PZ
United Kingdom
jpc73 “at” cam “dot” ac “dot” uk

Supervisors: Jan Maciejowski, Carl Edward Rasmussen



RESEARCH INTERESTS

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)

Publications

    • 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.

MISC

  • 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
    above).
  • 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.

PROFESSIONAL SERVICE

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