NEWS: Starting May 2017, I will join the Oxford-Man Institute and the Department of Engineering Science at Oxford as a Senior Research Fellow.

From November 2014-April 2017 I was 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 was working within the Autonomous and Intelligent Systems Partnership (AISP) and am grateful for having received funding from EPSRC and Schlumberger. Furthermore, I have been working in the area of computational mechanism design and am grateful for an award in support of this work from EPSRC-NCSML.

Contact information (Outdated)

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PAPERS (Outdated, please refer to my website at Oxford for more up-to-date information)

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

PROFESSIONAL SERVICE

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