Dr Inden is a Lecturer in Computing. He is currently module leader for Artificial Intelligence (ISYS30221, undergraduate level 6), Applied Artificial Intelligence (COMP40511, postgraduate level 7), Research Methods (COMP40321, postgraduate level 7), and Major Project (COMP40311, postgraduate level 7) and contributes to teaching Python programming and professional development.
Dr Inden was a research assistant at the Max Planck Institute for Mathematics in the Sciences (Leipzig, Germany) from 2003 to 2007, and received his PhD from the University of Leipzig in 2007 . From 2008 to 2014, he worked as a postdoctoral researcher at Bielefeld University (Germany) From 2014 to 2016, he was a lecturer at the University of Bedfordshire (UK). He joined Nottingham Trent University in September 2016.
Next to his degrees in Computer Science, he also holds a degree in Life Sciences from the Open University (UK), and is a Fellow of the Higher Education Academy.
Dr Inden is a member of the Computational Intelligence and Applications Group (CIA). His research is focused on using evolutionary algorithms to train artificial neural networks, in particular:
- Evolution of locomotion and navigation strategies for autonomous agents
- Pattern recognition and multisensory integration in evolving neural networks
- Genetic representations for large and partly regular neural networks
- Intelligent selection techniques
- Conditions that lead to sustained increases in the complexity of agent behaviours
Further research interests:
- Robots that interact with animal and plant populations
- Using genetic programming to learn compositional rules of music
- Machine learning in general
Opportunities to carry out postgraduate research towards an MPhil / PhD exist and further information may be obtained from the NTU Doctoral School.
Dr Inden has been a member of the program committee for Robotics: Science and Systems (2015) and the ACM Genetic and Evolutionary Computation Conference (2018, 2019), and a reviewer for various journals including Soft Computing, Theory in Biosciences, IEEE Transactions on Neural Networks, IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, and Neurocomputing.
Inden, B., & Jost, J., 2019. "Evolving neural networks to follow trajectories of arbitrary complexity". Neural Networks, 116, 224-236.
Mistry, J. & Inden, B., 2018. An approach to sign language translation using the Intel Realsense camera. In: 10th Computer Science and Electronic Engineering Conference (CEEC’18), University of Essex, Colchester, 19-21 September 2018.
Inden, B. & Jost, J. 2015 “Effects of Several Bioinspired Methods on the Stability of Coevolutionary Complexification”, in: Proceedings of the 2015 IEEE Symposium Series on Computational Intelligence.
Inden, B., Malisz, Z., Wagner, P., & Wachsmuth, I. 2013. “Timing and entrainment of multimodal backchanneling behavior for an embodied conversational agent”, in: J. Epps, F. Chen, S. Oviatt, K. Mase, A. Sears, K. Jokinen, et al. (Eds), Proceedings of the 15th International Conference on Multimodal Interaction, ICMI'13 - Sydney. New York: ACM.
Inden, B., & Jost, J. 2013. “Neural agents can evolve to reproduce sequences of arbitrary length”, in: P. Liò, O. Miglino, G. Nicosia, S. Nolfi, & M. Pavone (Eds), Advances in Artificial Life, ECAL 2013: Proceedings of the Twelfth European Conference on the Synthesis and Simulation of Living Systems. Cambridge (MA, USA): The MIT Press.
Inden, B., Y. Jin, R. Haschke, H. Ritter, & B. Sendhoff. 2013. “An examination of different fitness and novelty based selection methods for the evolution of neural networks” Soft Computing, 17(5), 753–767.
Inden, B., Z. Malisz, P. Wagner, & I. Wachsmuth. 2012. “Rapid entrainment to spontaneous speech: A comparison of oscillator models”, in: N. Miyake, D. Peebles, & R.P. Cooper (Eds.), Proceedings of the 34th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society.
Inden, B. 2012. “Open-ended Coevolution and the Emergence of Complex Irreducible Functional Units in Iterated Number Sequence Games”, in: Proceedings of the 14th annual conference on genetic and evolutionary computation. New York, NY, USA: ACM, 113 - 200.
Inden, B., Y. Jin, R. Haschke, & H. Ritter. 2012. “Evolving neural fields for problems with large input and output spaces”. Neural Networks, 28, 24 - 39.
Inden, B., Y. Jin, R. Haschke, & H. Ritter. 2011. “Evolution of multisensory integration in large neural fields”, in: Proceedings of the Tenth International Conference on Artificial Evolution, Evolution Artificielle. Lecture Notes in Computer Science, 7401. Springer-Verlag, 181 - 192.