Yang, S. and Koeppl, H. (2020):
The Hawkes Edge Partition Model for Continuous-time Event-based Temporal Networks.
In: Conference on Uncertainty in Artificial Intelligence (UAI), virtual Conference, August 03.-06., 2020, [Online-Edition: http://www.auai.org/uai2020/],
[Conference or Workshop Item]

Sinzger, M. and Gehri, M. and Koeppl, H. (2020):
Poisson channel with binary Markov input and average sojourn time constraint.
In: ISIT'20 - International Symposium on Information Theory, virtual online conference, 21.-26. June 2020, [Online-Edition: https://2020.ieee-isit.org/],
[Conference or Workshop Item]

Prangemeier, T. and Lehr, F. -X. and Schoeman, R.M. and Koeppl, H. (2020):
Microfluidic platforms for the dynamic characterisation of synthetic circuitry.
In: Current Opinion in Biotechnology, 63. Elsevier, pp. 167-176, ISSN 0958-1669,
DOI: 10.1016/j.copbio.2020.02.002,
[Online-Edition: https://www.sciencedirect.com/science/article/pii/S095816692...],
[Article]

KhudaBukhsh, W. R. and Kar, S. and Alt, B. and Rizk, A. and Koeppl, H. (2020):
Generalized Cost-Based Job Scheduling in Very Large Heterogenous Cluster Systems.
In: IEEE Transactions on Parallel and Distributed Systems (TPDS), IEEE, ISSN 1045-9219, e-ISSN 1045-9219,
DOI: 10.1109/TPDS.2020.2997771,
[Online-Edition: https://ieeexplore.ieee.org/document/9099971],
[Article]

Kumar, S. and Lun, X.-K. and Bodenmiller, B. and Rodriguez Martinez, M. and Koeppl, H. (2020):
Stabilized Reconstruction of Signaling Networks from Single-Cell Cue-Response Data.
In: Scientific reports, 10. Springer Nature, p. 1233, ISSN 2045-2322,
DOI: 10.1038/s41598-019-56444-5,
[Article]

Altintan, D. and Koeppl, H. (2019):
Hybrid master equation for jump-diffusion approximation of biomolecular reaction networks.
In: BIT Numerical Mathematics, Springer Nature, Netherlands, ISSN 1572-9125,
DOI: 10.1007/s10543-019-00781-4,
[Online-Edition: https://link.springer.com/article/10.1007/s10543-019-00781-4...],
[Article]

KhudaBukhsh, W. R. and Auddy, A. and Disser, Y. and Koeppl, H. (2019):
Approximate Lumpability for Markovian Agent-based Models Using Local Symmetries.
In: Jounal of Applied Probability, 56 (3), Cambridge University Press, pp. 647-671, DOI: 10.1017/jpr.2019.44,
[Online-Edition: https://www.cambridge.org/core/journals/journal-of-applied-p...],
[Article]

Alt, B. and Šošić, A. and Koeppl, H. (2019):
Correlation Priors for Reinforcement Learning.
In: 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Kanada, 09.12.-13.12.2019, [Online-Edition: https://papers.nips.cc/paper/9564-correlation-priors-for-rei...],
[Conference or Workshop Item]

KhudaBukhsh, W. R. and Kar, S. and Koeppl, H. and Rizk, A. (2019):
Provisioning and Performance Evaluation of Parallel Systems with Output Synchronization.
In: ACM Transactions on Modeling and Performance Evaluation of Computing Systems (TOMPECS), (1), 4. pp. Art. 6, ISSN 2376-3639,
DOI: 10.1145/3300142,
[Online-Edition: https://dl.acm.org/citation.cfm?id=3300142],
[Article]

Kang, H.-W. and Khuda Bukhsh, W.R. and Koeppl, H. and Rempala, G.A. (2019):
Quasi-steady-state approximations derived from the stochastic model of enzyme kinetics.
In: Bulletin of Mathematical Biology, Springer US, pp. 1-34, ISSN 0092-8240,
DOI: 10.1007/s11538-019-00574-4,
[Online-Edition: https://link.springer.com/article/10.1007/s11538-019-00574-4...],
[Article]

Falk, J. and Bronstein, L. and Hanst, M. and Drossel, B. and Koeppl, H. (2019):
Context in Synthetic Biology: Memory Effects of Environments with Mono-molecular Reactions.
In: The Journal of Chemical Physics, 150 (2), American Institute of Physics, ISSN 0021-9606,
DOI: 10.1063/1.5053816,
[Online-Edition: https://aip.scitation.org/doi/10.1063/1.5053816],
[Article]

Linzner, D. and Koeppl, H. (2018):
Cluster Variational Approximations for Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data.
In: 32. Conference on Neural Information Processing Systems, Montreal, Canada, December 3-8, 2018, [Conference or Workshop Item]

Yang, S. and Koeppl, H. (2018):
Collapsed Variational Inference for Nonparametric Bayesian Group Factor Analysis.
In: IEEE International Conference on Data Mining (ICDM'18), Singapore, 17.-20. November 2018, [Conference or Workshop Item]

Al-Sayed, S. and Koeppl, H. (2018):
Network Reconstruction from Time-Course Perturbation Data Using Multivariate Gaussian Processes.
In: IEEE International Workshop on Machine Learning for Signal Processing, In: IEEE International Workshop on Machine Learning for Signal Processing, Aalborg, Denmark, 17.-20. September 2018, [Online-Edition: https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?filter=i...],
[Conference or Workshop Item]

Sulaimanov, N. and Koeppl, H. and Burdet, F. and Ibberson, M. and Pagni, M. and Kumar, S. (2018):
Inferring gene expression networks with hubs using a degree weighted Lasso approach.
In: Bioinformatics (Oxford, England), bty716Oxford University Press, ISSN 1367-4803,
DOI: 10.1093/bioinformatics/bty716,
[Online-Edition: https://academic.oup.com/bioinformatics/advance-article/doi/...],
[Article]

Kruk, N. and Koeppl, H. and Maistrenko, Y. (2018):
Self-propelled Chimeras.
In: Physical Review E, American Physical Society, ISSN 2470-0045,
[Article]

Yang, S. and Koeppl, H. (2018):
Dependent Relational Gamma Process Models for Longitudinal Networks.
80In: Proceedings of Machine Learning Research (PMLR), In: Thirty-fifth International Conference on Machine Learning, Stockholm, Denmark, July 10-15, 2018, pp. 5547-5556, [Online-Edition: https://icml.cc/Conferences/2018/Schedule?showEvent=1942],
[Conference or Workshop Item]

Bronstein, L. and Koeppl, H. (2018):
Marginal process framework: A model reduction tool for Markov jump processes.
In: Physical Review E, E 97, 062147American Physical Society, ISSN 2470-0045,
DOI: 10.1103/PhysRevE.97.062147,
[Online-Edition: https://journals.aps.org/pre/abstract/10.1103/PhysRevE.97.06...],
[Article]

Šošić, A. and Zoubir, A. M. and Koeppl, H. (2018):
A Bayesian Approach to Policy Recognition and State Representation Learning.
In: IEEE Transactions on Pattern Analysis and Machine Intelligence, (6), 40. pp. 1295-1308, DOI: 10.1109/TPAMI.2017.2711024,
[Online-Edition: https://ieeexplore.ieee.org/document/7937852],
[Article]

Šošić, A. and Zoubir, A. M. and Koeppl, H. (2018):
Reinforcement Learning in a Continuum of Agents.
In: Swarm Intelligence, 12 (1), pp. 23-51, DOI: 10.1007/s11721-017-0142-9,
[Online-Edition: http://rdcu.be/wKay],
[Article]

Bronstein, L. and Koeppl, H. (2018):
A variational approach to moment-closure approximations for the kinetics of biomolecular reaction networks.
In: The Journal of Chemical Physics, 148 (1), American Institute of Physics (AIP), ISSN 00219606,
DOI: 10.1063/1.5003892,
[Online-Edition: http://aip.scitation.org/doi/10.1063/1.5003892],
[Article]

Šošić, A. and Rueckert, E. and Peters, J. and Zoubir, A. M. and Koeppl, H. (2018):
Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling.
In: Journal of Machine Learning Research, 19 (69), pp. 1-45, [Online-Edition: http://www.jmlr.org/papers/volume19/18-113/18-113.pdf],
[Article]

Šošić, A. and Zoubir, A. M. and Koeppl, H. (2018):
Inverse Reinforcement Learning via Nonparametric Subgoal Modeling.
In: AAAI Spring Symposium on Data-Efficient Reinforcement Learning, [Online-Edition: https://aaai.org/ocs/index.php/SSS/SSS18/paper/view/17531/15...],
[Conference or Workshop Item]

Yang, S. and Koeppl, H. (2018):
A Poisson Gamma Probabilistic Model for Latent Node-group Memberships in Dynamic Networks.
In: AAAI 2018, Association for the Advancement of Artificial Intelligence, New Orleans, 2018, [Conference or Workshop Item]

KhudaBukhsh, W. R. and Rizk, A. and Froemmgen, A. and Koeppl, H. (2017):
Optimizing Stochastic Scheduling in Fork-Join Queueing Models: Bounds and Applications.
In: Technical Program of IEEE INFOCOM 2017, IEEE, [Online-Edition: https://arxiv.org/abs/1612.05486],
[Article]

KhudaBukhsh, W. R. and Rizk, A. and Froemmgen, A. and Koeppl, H. (2017):
Optimizing Stochastic Scheduling in Fork-Join Queueing Models: Bounds and Applications.
In: Technical Program of IEEE INFOCOM 2017, IEEE, [Online-Edition: http://tubiblio.ulb.tu-darmstadt.de/84610/],
[Article]

Ruess, J. and Koeppl, H. and Zechner, C. (2017):
Sensitivity estimation for stochastic models of biochemical reaction networks in the presence of extrinsic variability.
In: The Journal of Chemical Physics, 146 (124122), AIP, [Online-Edition: http://aip.scitation.org/doi/10.1063/1.4978940],
[Article]

KhudaBukhsh, W. R. and Woroszylo, C. and Rempala, G. A. and Koeppl, H. (2017):
Functional Central Limit Theorem for Susceptible-Infected Process On Configuration Model Graphs.
In: Annals of Applied Probability (under review), [Article]

Šošić, A. and KhudaBukhsh, W. R. and Zoubir, A. M. and Koeppl, H. (2017):
Inverse Reinforcement Learning in Swarm Systems.
In: AAMAS Workshop on Transfer in Reinforcement Learning, [Conference or Workshop Item]

Šošić, A. and KhudaBukhsh, W. R. and Zoubir, A. M. and Koeppl, H. (2017):
Inverse Reinforcement Learning in Swarm Systems (Best Paper Award Finalist).
In: International Conference on Autonomous Agents and Multiagent Systems, [Online-Edition: http://dl.acm.org/citation.cfm?id=3091320],
[Conference or Workshop Item]

Bronstein, L. and Diemer, J. and Koeppl, H. and Schneider, C. and Suess, Beatrix (2017):
ROC'n'Ribo: Characterizing a riboswitching expression system by modeling single-cell data.
In: ACS Synthetic Biology, (7), ACS, pp. 1211-1224, ISSN 2161-5063,
[Online-Edition: http://pubs.acs.org/doi/10.1021/acssynbio.6b00322],
[Article]

Bronstein, L. and Koeppl, H. (2016):
Scalable inference using PMCMC and parallel tempering for high-throughput measurements of biomolecular reaction networks.
In: 55th IEEE Conference on Decision and Control, Las Vegas, December 2016, [Online-Edition: http://ieeexplore.ieee.org/document/7798361/#full-text-secti...],
[Conference or Workshop Item]

Sulaimanov, N. and Koeppl, H. (2016):
Graph reconstruction using covariance based methods.
In: EURASIP Journal on Bioinformatics and Systems Biology, Springer, [Online-Edition: http://bsb.eurasipjournals.springeropen.com/articles/10.1186...],
[Article]

Bronstein, L. and Koeppl, H. (2016):
A Diagram Technique for cumulant equations in biomolecular reaction networks with mass-action kinetics.
In: 55th IEEE Conference on Decision and Control, Las Vegas, USA, December 2016, [Online-Edition: http://ieeexplore.ieee.org/document/7799170/?part=1],
[Conference or Workshop Item]

Ganguly, A. and Altintan, D. and Koeppl, H. (2016):
Efficient Simulation of Multiscale Reaction.
In: American Control Conference, Boston, Juli 2016, [Conference or Workshop Item]

Hill, S. M. and Heiser, L. M. and Cokalaer, T. and Unger, M. and Nesser, N. K. and Carlin, D. E. and Zhang, Y. and Sokolov, A. and Paull, E. O. and Wong, C. K. and Graim, K. and Bivol, A. and Wang, H. and Zhu, F. and Afsari, B. and Danilova, L. V. and Favorov, A. V. and Lee, W. S. and Taylor, D. and Hu, C. W. and Long, B. L. and Noren, D. P. and Bisberg, A. J. and Mills, G. B. and Gray, J. W. and Kellen, M. and Norman, T. and Friend, S. and Qutub, A. A. and Fertig, E. J. and Guan, Y. and Song, M. and Stuart, J. M. and Spellman, P. T. and Koeppl, H. and Stolovitzky, G. and Saez-Rodriguez, J. and Mukherjee, S. (2016):
Interferring causal molecular networks: empirical assessment through a community-based effort.
In: Nature methods, Nature Publishing Group, [Online-Edition: http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth...],
[Article]

Studer, L. and Paulevé, L. and Zechner, C. and Reumann, M. and Rodriguez Martinez, M. and Koeppl, H. (2016):
Marginalized Continuous Time Bayesian Networks for Network Reconstruction from Incomplete Observations.
Phoenix, USA, In: AAAI, Association for the Advancement of Artificial Intelligence, Phoenix, USA, 12.-17.02.2016, [Online-Edition: http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/123...],
[Conference or Workshop Item]

KhudaBukhsh, W. R. and Rueckert, J. and Wulfheide, J. and Hausheer, D. and Koeppl, H. (2016):
Analysing and Leveraging Client Heterogeneity in Swarming-based Live Streaming.
In: IFIP International Conference on Networking (NETWORKING), In: IFIP International Conference on Networking, Wien, Austria, Mai 2016, pp. 386-394, [Online-Edition: http://dl.ifip.org/db/conf/networking/networking2016/1570236...],
[Conference or Workshop Item]

KhudaBukhsh, W. R. and Rueckert, J. and Wulfheide, J. and Hausheer, D. and Koeppl, H. (2016):
Analysing and Leveraging Client Heterogeneity in Swarming-based Live Streaming.
In: IFIP International Conference on Networking (NETWORKING), pp. 386-394, [Online-Edition: http://tubiblio.ulb.tu-darmstadt.de/83371/],
[Conference or Workshop Item]

Richerzhagen, B. and Wulfheide, J. and Koeppl, H. and Mauthe, A. U. and Nahrstedt, K. and Steinmetz, R. (2016):
Enabling Crowdsourced Live Event Coverage with Adaptive Collaborative Upload Strategies.
In: 2016 IEEE 17th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM), Coimbra, Portugal, [Conference or Workshop Item]

Šošić, A. and Zoubir, A. M. and Koeppl, H. (2016):
Policy Recognition via Expectation Maximization.
In: IEEE International Conference on Acoustics, Speech and Signal Processing, DOI: 10.1109/ICASSP.2016.7472589,
[Online-Edition: https://doi.org/10.1109/icassp.2016.7472589],
[Conference or Workshop Item]

Huang, L. and Hansen, A. S. and Pauleve, L. and Unger, M. and Zechner, C. and Koeppl, H. (2016):
Reconstructing dynamic molecular states from single-cell time series.
In: Journal of The Royal Society Interface, Royal Society Publishing, ISSN 1742-5689,
[Online-Edition: http://rsif.royalsocietypublishing.org/content/13/122/201605...],
[Article]

Sutter, T. and Ganguly, A. and Koeppl, H. (2016):
A variational approach to path estimation and parameter inference of hidden diffusion processes.
In: Journal of Machine Learning Research, [Online-Edition: http://jmlr.org/papers/v17/16-075.html],
[Article]

Hegemann, B. and Unger, M. and Lee, S. S. and Stoffel-Studer, I. and van den Heuvel, J. and Pelet, S. and Koeppl, H. and Peter, M. (2015):
A Cellular System for Spatial Signal Decoding in Chemical Gradients.
In: Developmental Cell, 35 (4), Elsevier, pp. 458-470, [Online-Edition: http://www.cell.com/developmental-cell/fulltext/S1534-5807%2...],
[Article]

Huang, L. and Hjalmarsson, H. and Koeppl, H. (2015):
Almost sure stability and stabilization of discrete-time stochastic systems.
In: Systems & Control Letters, 82pp. 26-32, [Online-Edition: http://www.sciencedirect.com/science/article/pii/S0167691115...],
[Article]

Altintan, D. and Ganguly, A. and Koeppl, H. (2015):
Error bound and simulation algorithm for piecewise deterministic approximations of stochastic reaction systems.
In: American Control Conference (ACC), 2015, In: American Control Conference (ACC), 2015, Chicago, 1-3 July 2015, [Online-Edition: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7170830],
[Conference or Workshop Item]

Bronstein, L. and Zechner, C. and Koeppl, H. (2015):
Bayesian inference of reaction kinetics from single-cell recordings across a heterogeneous cell population.
In: ScienceDirect - Methods, Elsevier, [Online-Edition: http://www.sciencedirect.com/science/journal/aip/10462023],
[Article]

KhudaBukhsh, W. R. and Rueckert, J. and Wulfheide, J. and Hausheer, D. and Koeppl, H. KhudaBukhsh W. R. (Corporate Creator) (2015):
A Comprehensive Analysis of Swarming-based Live Streaming to Leverage Client Heterogenieity.
In: Technical Report, Darmstadt, Technische Universität Darmstadt, [Online-Edition: http://www.bcs.tu-darmstadt.de/biocomm/people_1/phdstudents/...],
[Report]

Altintan, D. and Ganguly, A. and Koeppl, H. (2015):
Jump-Diffusion Approximation of Stochastic Reaction Dynamics: Error bounds and Algorithms.
In: SIAM Multiscale Modeling and Simulation, SIAM (Society for Industrial and Applied Mathematics), ISSN 1540-3459,
[Online-Edition: http://arxiv.org/abs/1409.4303],
[Article]

Koeppl, H. and Zechner, C. (2014):
Uncoupled analysis of stochastic reaction networks in fluctuating environments.
In: PLOS Computational Biology, 10 (12), Cornell University, ISSN 1476-928X,
[Online-Edition: http://journals.plos.org/ploscompbiol/article?id=10.1371/jou...],
[Article]

Koeppl, H. and Hafner, M. and Lu, J. (2014):
From Specification to Parameters: A Linearization Approach.
In: A Systems Theoretic Approach to Systems and Synthetic Biology II: Analysis and Design of Cellular Systems, Netherlands, Springer, pp. 245-256, [Online-Edition: http://link.springer.com/chapter/10.1007/978-94-017-9047-5_1...],
[Book Section]

Zechner, C. and Unger, M. and Pelet, S. and Peter, M. and Koeppl, H. (2014):
Scalable inference of heterogeneous reaction kinetics from pooled single-cell recordings.
In: Nature methods, 11 (2), pp. 197-202, [Online-Edition: http://www.nature.com/nmeth/journal/v11/n2/full/nmeth.2794.h...],
[Article]

Zechner, C. and Wadehn, F. and Koeppl, H. (2014):
Sparse learning of Markovian population models in random environments.
Cornell, In: IFAC 2014, The 19th World Congress of the International Federation of Automatic Control, Promoting automatic control for the benefit of humankind, Cape Town, South Africa, 24-29 August 2014, pp. 1723-1728, [Online-Edition: http://arxiv.org/abs/1401.4026],
[Conference or Workshop Item]

Geiger, B. C. and Petrov, T. and Kubin, G. and Koeppl, H. (2014):
Optimal Kullback-Leibler Aggregation via Information Bottleneck.
In: IEEE Transactions on Automatic Control, IEEE, ISSN 0018-9286,
[Online-Edition: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6...],
[Article]

Ganguly, A. and Petrov, T. and Koeppl, H. (2013):
Markov chain aggregation and its applications to combinatorial reaction networks.
In: Journal of mathematical biology, 69 (3), pp. 767-797, [Online-Edition: http://link.springer.com/article/10.1007/s00285-013-0738-7],
[Article]

Tarca, A. L. and Lauria, M. and Unger, M. and Bilal, E. and Boue, S. and Kumar Dey, K. and Hoeng, J. and Koeppl, H. and Martin, F. and Meyer, P. and Nandy, P. and Norel, R. and Peitsch, M. and Rice, J. and Romero, R. and Stolovitzky, G. and Talikka, M. and Xiang, Y. and Zechner, C. (2013):
Strengths and limitations of microarray-based phenotype prediction: lessons learned from the IMPROVER Diagnostic Signature Challenge.
In: Bioinformatics (Oxford, England), 29 (22), pp. 2892-2899, [Online-Edition: http://www.ncbi.nlm.nih.gov/pubmed/23966112],
[Article]

de Heras Ciechomski, P. and Klann, M. and Mange, R. and Koeppl, H. (2013):
From biochemical reaction networks to 3D dynamics in the cell: The ZigCell3D modeling, simulation and visualisation framework.
In: IEEE Symposium on Biological Data Visualization (BioVis), IEEE, pp. 41-48, [Online-Edition: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumbe...],
[Article]

Klann, M. and Koeppl, H. (2013):
Reaction schemes, escape times and geminate recombinations in particle-based spatial simulations of biochemical reactions.
In: Physical biology, 10 (4), pp. 046005, [Online-Edition: http://iopscience.iop.org/1478-3975/10/4/046005/article],
[Article]

Lu, J. and August, E. and Koeppl, H. (2013):
Inverse problems from biomedicine : Inference of putative disease mechanisms and robust therapeutic strategies.
In: Journal of mathematical biology, 67 (1), Springer Verlag, pp. 143-168, ISSN 0303-6812,
[Online-Edition: http://link.springer.com/article/10.1007/s00285-012-0523-z],
[Article]

Paulevé, L. and Craciun, G. and Koeppl, H. (2013):
Dynamical properties of Discrete Reaction Networks.
In: Journal of mathematical biology, [Online-Edition: http://arxiv.org/abs/1302.3363],
[Article]

Nandy, P. and Unger, M. and Zechner, C. and Dey, K. and Koeppl, H. (2013):
Learning diagnostic signatures from microarray data using Ll-regularized logistic regression.
In: Systems Biomedicine, 1 (4), Taylor & Francis, [Online-Edition: http://www.tandfonline.com/doi/full/10.4161/sysb.25271?mobil...],
[Article]

Koeppl, H. and Petrov, T. (2013):
Approximate model reductions for combinatorial reaction systems; European Control Conferenc (ECC 2013).
In: European Control Conferenc (ECC 2013), Zuerich, 17-19 July 2013, pp. 4172-4177, [Online-Edition: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6669734...],
[Conference or Workshop Item]

Klann, M. and Paulevé, L. and Petrov, T. and Koeppl, H. (2013):
Coarse-Grained Brownian Dynamics Simulation of Rule-Based Models.
8130Springer Berlin Heidelberg, In: 11th International Conference on Computational Methods in Systems Biology (CMSB 2013), pp. 64-77, [Online-Edition: http://link.springer.com/chapter/10.1007/978-3-642-40708-6_6...],
[Conference or Workshop Item]

Koeppl, H. and Hafner, M. and Lu, J. (2013):
Mapping behavioral specifications to model parameters in synthetic biology.
In: BMC Bioinformatics, 14pp. S9, [Online-Edition: http://www.biomedcentral.com/1471-2105/14/S10/S9],
[Article]

Zechner, C. and Deb, S. and Koeppl, H. (2013):
Marginal dynamics of stochastic biochemical networks in random environments.
IEEE, In: 2013 European Control Conference (ECC), Zürich, 2013, pp. 4269-4274, [Online-Edition: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6669606],
[Conference or Workshop Item]

Feret, J. and Koeppl, H. and Petrov, T. (2013):
Stochastic fragments: A framework for the exact reduction of the stochastic semantics of rule-based models.
In: International Journal of Software and Informatics, 7 (4), pp. 527-604, [Online-Edition: http://www.ijsi.org/ch/reader/view_abstract.aspx?file_no=i17...],
[Article]

Paulevé, L. and Andrieux, G. and Koeppl, H. (2013):
Under-approximating cut sets for reachability in large scale automata Networks.
8044Springer, In: 25th International Conference on Computer Aided Verification (CAV 2013), pp. 69-84, [Online-Edition: http://link.springer.com/chapter/10.1007%2F978-3-642-39799-8...],
[Conference or Workshop Item]

August, E. and Koeppl, H. (2012):
Computing enclosures for uncertain biochemical systems.
In: IET Systems Biology, 6 (6), pp. 232-240, [Online-Edition: http://digital-library.theiet.org/content/journals/10.1049/i...],
[Article]

August, E. and Craciun, G. and Koeppl, H. (2012):
Finding invariant sets for biological systems using monomial domination.
Maui, HI, USA, IEEE, In: 51st IEEE Conference on Decision and Control (CDC), 2012, 2012, pp. 3001-3006, [Online-Edition: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6...],
[Conference or Workshop Item]

Zechner, C. and Nandy, P. and Unger, M. and Koeppl, H. (2012):
Optimal variational perturbations for the inference of stochastic reaction dynamics.
IEEE, In: 2012 IEEE 51st IEEE Conference on Decision and Control (CDC), pp. 5336-5341, [Online-Edition: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumbe...],
[Conference or Workshop Item]

Petrov, T. and Feret, J. and Koeppl, H. (2012):
Reconstructing species-based dynamics from reduced stochastic rule-based models.
In: Proceedings of the 2012 Winter Simulation Conference, Winter Simulation Conference, p. 225, [Online-Edition: http://dl.acm.org/citation.cfm?id=2429759.2430062],
[Article]

Klann, M. and Ganguly, A. and Koeppl, H. (2012):
Hybrid spatial Gillespie and particle tracking simulation.
In: Bioinformatics (Oxford, England), 28 (18), pp. i549, [Online-Edition: http://bioinformatics.oxfordjournals.org/content/28/18/i549....],
[Article]

Koeppl, H. and Zechner, C. and Ganguly, A. and Pelet, S. and Peter, M. (2012):
Accounting for extrinsic variability in the estimation of stochastic rate constants.
In: International Journal of Robust and Nonlinear Control, 22 (10), Wiley-Blackwell, pp. 1103-1119, [Online-Edition: http://doi.wiley.com/10.1002/rnc.2804],
[Article]

Nandy, P. and Unger, M. and Zechner, C. and Koeppl, H. (2012):
Optimal Perturbations for the Identification of Stochastic Reaction Dynamics.
Elsevier, In: 16th IFAC Symposium on System Identification, pp. 686-691, [Online-Edition: http://www.ifac-papersonline.net/Detailed/54661.html],
[Conference or Workshop Item]

Petrov, T. and Ganguly, A. and Koeppl, H. (2012):
Model Decomposition and Stochastic Fragments.
In: Electronic Notes in Theoretical Computer Science, 284pp. 105-124, [Online-Edition: http://linkinghub.elsevier.com/retrieve/pii/S157106611200019...],
[Article]

Pantea, C. and Koeppl, H. and Craciun, G. (2012):
Global injectivity and multiple equilibria in uni- and bi-molecular reaction networks.
In: Discrete and Continuous Dynamical Systems - Series B, 17 (6), American Institute of Mathematical Sciences, pp. 2153-2170, [Online-Edition: https://aimsciences.org/journals/displayArticlesnew.jsp?pape...],
[Article]

Zechner, C. and Ruess, J. and Krenn, P. and Pelet, S. and Peter, M. and Lygeros, J. and Koeppl, H. (2012):
Moment-based inference predicts bimodality in transient gene expression.
In: Proceedings of the National Academy of Sciences of the United States of America, 109 (21), pp. 8340-8345, [Online-Edition: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3361437/?tool=pm...],
[Article]

Feret, J. and Henzinger, T. and Koeppl, H. and Petrov, T. (2012):
Lumpability Abstractions of Rule-based Systems.
In: Journal of Theoretical Comuter Science, 431pp. 137-164, [Online-Edition: http://linkinghub.elsevier.com/retrieve/pii/S030439751101025...],
[Article]

Hafner, M. and Koeppl, H. and Gonze, D. (2012):
Effect of network architecture on synchronization and entrainment properties of the circadian oscillations in the suprachiasmatic nucleus.
In: PLoS computational biology, 8 (3), pp. e1002419, [Online-Edition: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3297560/?tool=pm...],
[Article]

Hiroi, N. and Klann, M. and Iba, K. and de Heras Ciechomski, P. and Yamashita, S. and Tabira, A. and Okuhara, T. and Kubojima, T. and Okada, Y. and Oka, K. and Mange, R. and Unger, M. and Funahashi, A. and Koeppl, H. (2012):
From microscopy data to in silico environments for in vivo-oriented simulations.
In: EURASIP Journal on Bioinformatics and Systems Biology, 2012 (1), p. 7, [Online-Edition: http://bsb.eurasipjournals.com/CONTENT/2012/1/7],
[Article]

Klann, M. and Koeppl, H. and Reuss, M. (2012):
Spatial modeling of vesicle transport and the cytoskeleton: the challenge of hitting the right road.
In: PloS one, 7 (1), pp. e29645, [Online-Edition: http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjourna...],
[Article]

Klann, M. and Koeppl, H. (2012):
Spatial simulations in systems biology: from molecules to cells.
In: International journal of molecular sciences, 13 (6), pp. 7798-7827, [Online-Edition: http://www.mdpi.com/1422-0067/13/6/7798],
[Article]

Koeppl, H. and Petrov, T. (2012):
Reductions of stochastic rule-based models: HOG pathway in yeast.
In: ICSB : The 13th International Conference on Systems Biology, [Online-Edition: http://abstracts.genetics-gsa.org/2012/icsb/book_ICSB_final....],
[Conference or Workshop Item]

Klann, M. and Koeppl, H. (2012):
Spatial stochastic simulation of transcription factor binding reveals mechaniscms to control gene activation.
61Tampere University of Technology, Tampere International Center for Signal Processing, In: 9th International Workshop on Computational Systems Biology (WCSB 2012), pp. 51-54, [Online-Edition: http://www.cs.tut.fi/wcsb12/WCSB2012.pdf],
[Conference or Workshop Item]

August, E. and Lu, J. and Koeppl, H. (2012):
Trajectory enclosures for systems with uncertainties in initial conditions and parameter values.
Fairmont Queen Elizabeth, Montreal, Canada, In: 2012 American Control Conference, Fairmont Queen Elizabeth, Montreal, Canada, 2012, pp. 1488-1493, [Online-Edition: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6314741...],
[Conference or Workshop Item]

Zechner, C. and Pelet, S. and Peter, M. and Koeppl, H. (2011):
Recursive Bayesian estimation of stochastic rate constants from heterogeneous cell populations.
In: IEEE Conference on Decision and Control and European Control Conference, IEEE, pp. 5837-5843, [Online-Edition: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumbe...],
[Article]

Koeppl, H. and Hafner, M. and Ganguly, A. and Mehrotra, A. (2011):
Deterministic characterization of phase noise in biomolecular oscillators.
In: Physical biology, 8 (5), p. 55008, [Online-Edition: http://iopscience.iop.org/1478-3975/8/5/055008/fulltext/],
[Article]

Meyer, P. and Alexopoulos, L. G. and Bonk, T. and Califano, A. and Cho, C. R. and de la Fuente, A. and de Graaf, D. and Hartemink, A. J. and Hoeng, J. and Ivanov, N. V. and Koeppl, H. and Linding, R. and Marbach, D. and Norel, R. and Peitsch, M. C. and Rice, J. J. and Royyuru, A. and Schacherer, F. and Sprengel, J. and Stolle, K. and Vitkup, D. and Stolovitzky, G. (2011):
Verification of systems biology research in the age of collaborative competition.
In: Nature biotechnology, 29 (9), pp. 811-815, [Online-Edition: http://www.nature.com/nbt/journal/v29/n9/abs/nbt.1968.html],
[Article]

Koeppl, H. and Petrov, T. (2011):
Stochastic Semantics of Signaling as a Composition of Agent-view Automata.
In: Electronic Notes in Theoretical Computer Science, 272pp. 3-17, [Online-Edition: http://linkinghub.elsevier.com/retrieve/pii/S157106611100069...],
[Article]

Lu, J. and Grass, P. and Koeppl, H. (2011):
Computational identification of optimal multi target drug intervention strategies for combination theory.
Zurich, In: Eighth International Workshop on Computational Systems Biology, WCSB 2011, June 6-8, 2011, Zurich, Switzerland, p. 132, [Online-Edition: http://www.wcsb2011.ethz.ch/programme],
[Conference or Workshop Item]

August, E. and Wang, Y. and Doyle, F. J. and Lu, J. and Koeppl, H. (2011):
Computationally implementable sufficient conditions for the synchronisation of coupled dynamical systems with time delays in the coupling.
San Francisco, CA, USA, IEEE, In: Proceedings of the 2011 American Control Conference, pp. 839-844, [Online-Edition: http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=599073...],
[Conference or Workshop Item]

Danos, V. and Koeppl, H. and Wilson-Kanamori, J. (2011):
Cooperative assembly systems.
In: DNA Computing and Molecular Programming, pp. 1-21, [Online-Edition: http://link.springer.com/chapter/10.1007/978-3-642-23638-9_1],
[Article]

Koeppl, H. and Andreozzi, S. and Steuer, R. (2011):
Guaranteed and Randomized Methods for Stability Analysis of Uncertain Metabolic Networks.
In: Lecture notes in control and information sciences, 407Springer, pp. 297-309, [Online-Edition: http://link.springer.com/chapter/10.1007/978-3-642-16135-3_2...],
[Article]

Klann, M. and Ganguly, A. and Koeppl, H. (2011):
Improved Reaction Scheme for Spatial Stochastic Simulations with Single Molecule Detail.
57Tampere, Tampere University of Technology, In: Eighth International Workshop on Computational Systems Biology (WCSB 2011), pp. 93-96, [Online-Edition: http://www.wcsb2011.ethz.ch/programme],
[Conference or Workshop Item]

Falk, M. and Ott, M. and Ertl, T. and Klann, M. and Koeppl, H. (2011):
Parallelized Agent-based Simulation on CPU and Graphics Hardware for Spatial and Stochastic Models in Biology Categories and Subject Descriptors.
In: CMSB '11 Proceedings of the 9th International Conference on Computational Methods in Systems Biology, New York, New York, USA, ACM Press, pp. 73-82, [Online-Edition: http://dl.acm.org/citation.cfm?doid=2037509.2037521],
[Conference or Workshop Item]

Unger, M. and Lee, S.-S. and Peter, M. and Koeppl, H. (2011):
Pulse Width Modulation of Liquid Flows.
San Diego, CA, Chemical and Biological Microsystems Society, In: 15th International Conference on Miniaturized Systems for Chemistry and Life Sciences, pp. 1567-1569, [Online-Edition: http://e-citations.ethbib.ethz.ch/view/pub:68788],
[Conference or Workshop Item]

Hafner, M. and Lu, J. and Petrov, T. and Koeppl, H. (2011):
Rational design of robust biomolecular circuits: From specification to parameters.
In: Analysis and Design of Biomolecular Circuits, New York, NY, Springer, pp. 253-281, [Online-Edition: http://link.springer.com/chapter/10.1007/978-1-4419-6766-4_1...],
[Book Section]

Hafner, M. and Koeppl, H. (2011):
Stochastic Simulations in Systems Biology.
In: Handbook of Research on Computational Science and Engineering: Theory and Practice, IGI Global, pp. 267-286, [Online-Edition: http://www.igi-global.com/chapter/handbook-research-computat...],
[Book Section]

Camporesi, F. and Feret, J. and Koeppl, H. and Petrov, T. (2010):
Combining Model Reductions.
In: Electronic Notes in Theoretical Computer Science, 265pp. 73-96, [Online-Edition: http://linkinghub.elsevier.com/retrieve/pii/S157106611000085...],
[Article]

Petrov, T. and Koeppl, H. (2010):
Maximal reduction of deterministic semantics of rule-based models - Google-Suche.
In: Proceedings of the International Workshop on computational Systems Biology (WCSB) in 2010, pp. 83-87, [Online-Edition: https://www.google.de/?gfe\_rd=ctrl\&ei=l3o6U7aXG\_Da8geKzYC...],
[Conference or Workshop Item]

Koeppl, H. and Setti, G. and Pelet, S. and Mangia, M. and Petrov, T. and Peter, M. (2010):
Probability metrics to calibrate stochastic chemical kinetics.
In: Circuits and Systems (ISCAS), Proceedings of 2010 IEEE International Symposium on, [Article]

Hafner, M. and Koeppl, H. and Hasler, M. and Wagner, A. (2009):
'Glocal' robustness analysis and model discrimination for circadian oscillators.
In: PLoS Computational Biology, 5 (10), pp. e1000534, [Online-Edition: http://journals.plos.org/ploscompbiol/article?id=10.1371/jou...],
[Article]

Koeppl, H. and Setti, G. (2009):
Analysis and design of biological circuits and systems.
Taipeh, Taiwan, IEEE, In: 2009 IEEE International Symposium on Circuits and Systems, pp. 297-300, [Online-Edition: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumbe...],
[Conference or Workshop Item]

Rodrigues, A. and Koeppl, H. and Ohtsuki, H. and Satake, A. (2009):
A Game Theoretical Model of deforestation in human-environment relationships.
In: Journal of Theoretical Biology, 258 (1), pp. 127-134, [Online-Edition: http://www.sciencedirect.com/science/article/pii/S0022519309...],
[Article]

Koeppl, H. (2009):
A Local Nonlinear Model for the Approximation and Identification of a Class of Systems.
In: IEEE Transactions on Circuits and Systems II: Express Briefs, 56 (4), pp. 315-319, [Online-Edition: http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=4801647],
[Article]

Parisi, F. and Koeppl, H. and Naef, F. (2009):
Network inference by combining biologically motivated regulatory constraints with penalized regression.
In: Annals of the New York Academy of Sciences, 1158pp. 114-124, [Online-Edition: http://onlinelibrary.wiley.com/doi/10.1111/j.1749-6632.2008....],
[Article]

Hafner, M. and Koeppl, H. and Wagner, A. (2009):
Robustness and evolution in oscillatory systems with feedback loops.
Denver, USA, IEEE, In: Proc. of the Third IEEE International Conference on Foundations of Systems Biology in Engineering (FOSBE), p. 4, [Online-Edition: http://infoscience.epfl.ch/record/130922?ln=en http://arxiv....],
[Conference or Workshop Item]

Koeppl, H. and Haeusler, S. (2009):
Motifs, algebraic connectivity and computational performance of two data- based cortical circuit templates.
In: Proceedings of the sixth International Workshop on Computational Systems Biology, pp. 83-86, [Online-Edition: http://infoscience.epfl.ch/record/131569?ln=en],
[Article]

Hafner, M. and Danos, V. and Koeppl, H. (2009):
Rule-based modeling for protein-protein interaction networks - the Cyanobacterial circadian clock as a case studyproceedings.
Aarhus, Denmark, In: Proceedings of the International Workshop on Computational Systems Biology (WCSB), pp. 87-90, [Online-Edition: http://citeseerx.ist.psu.edu/viewdoc/summary;jsessionid=D87D...],
[Conference or Workshop Item]

Koeppl, H. and Hafner, M. and Steuer, R. (2009):
Semi-quantitative stability analysis constrains saturation levels in metabolic networks.
Aarhus, Denmark, In: Proceedings of the Intenational Workshop on Computational Systems Biology (WCSB), pp. 91-94, [Online-Edition: http://65.54.113.26/Publication/5234529],
[Conference or Workshop Item]

Koeppl, H. and Schumacher, L. and Danos, V. (2009):
A Statistical analysis of receptor.
Aarhus, Denmark, In: Proceedings of the International Workshop on Computtional Systmes Biology (WCSB), pp. 95-98, [Conference or Workshop Item]

Krall, C. and Witrisal, K. and Leus, G. and Koeppl, H. (2008):
Minimum Mean-Square Error Equalization for Second-Order Volterra Systems.
In: IEEE Transactions on Signal Processing, 56 (10), IEEE, pp. 4729-4737, [Online-Edition: http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=4558047],
[Article]

Murmann, B. and Vogel, C. and Koeppl, H. (2008):
Digitally enhanced analog circuits: System aspects.
IEEE, In: 2008 IEEE International Symposium on Circuits and Systems, pp. 560-563, [Online-Edition: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4541479...],
[Conference or Workshop Item]

Singerl, P. and Koeppl, H. (2007):
A Low-rate identification method for digital predistorters based on Volterra kernel interpolation.
In: Analog Integrated Circuits and Signal Processing, 56 (1-2), Springer, pp. 107-115, [Online-Edition: http://link.springer.com/article/10.1007/s10470-007-9074-4/f...],
[Article]

Koeppl, H. (2007):
The Composition Rule for Multivariate Volterra Operators and its Application to Circuit Analysis.
IEEE, In: 2007 IEEE International Symposium on Circuits and Systems, pp. 441-444, [Online-Edition: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumbe...],
[Conference or Workshop Item]

Koeppl, H. and Chua, L. O. (2007):
An Adaptive Cellular Nonlinear Network and its Application.
In: Proceedings of the International Symposium on Nonlinear Theory and its Applications (NOLTA), pages 15–18, Sept. 16-19, 2007, Vancouver, Canada., [Online-Edition: http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=417672...],
[Article]

Huang, C.-H. and Koeppl, H. (2007):
A Bio-inspired Computer Fovea Model based on hexagonal-type cellular neural networks.
In: IEEE Transactions on circuits and systems-I : regular papers, 54 (1), IEEE, [Online-Edition: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4061016],
[Article]

Wolkerstorfer, M. and Koeppl, H. (2007):
On the Projection Dynamic for Selfish Routing.
Dresden, Germany, In: European Complex Systems Conference, [Online-Edition: http://infoscience.epfl.ch/record/112928],
[Conference or Workshop Item]

Koeppl, H. and Singerl, P. (2006):
An Efficient Scheme for Nonlinear Modeling and Predistortion in Mixed-Signal Systems.
In: IEEE Transactions on Circuits and Systems II: Express Briefs, 53 (12), pp. 1368-1372, [Online-Edition: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumbe...],
[Article]

Koeppl, H. (2006):
An Adaptive Cellular Network for Subspace Extraction.
Pacific Grove, CA, USA, IEEE, In: 2006 Fortieth Asilomar Conference on Signals, Systems and Computers, pp. 1037-1041, [Online-Edition: http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=417672...],
[Conference or Workshop Item]

Koeppl, H. (2006):
Information Rate Maximization over a Resistive Grid.
Vancouver, BC, IEEE, In: The 2006 IEEE International Joint Conference on Neural Network Proceedings, pp. 5196-5203, [Online-Edition: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumbe...],
[Conference or Workshop Item]

Singerl, P. and Koeppl, H. (2005):
A Low-rate identification method for digital predistorters based on Volterra kernel interpolation.
In: Circuits and Systems, 2005. 48th Midwest Symposium, 2IEEE, pp. 1533-1536, [Online-Edition: http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=1594406&tag...],
[Article]

Schwingshackl, D. and Koeppl, H. and Kubin, G. (2005):
Exact discrete-time representation of continuous-time Volterra filters.
In: NSIP 2005. Abstracts. IEEE-Eurasip Nonlinear Signal and Image Processing, 2005., IEEE, p. 11, [Online-Edition: http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=150222...],
[Article]

Krall, C. and Witrisal, K. and Koeppl, H. and Leus, G. and Pausini, M. (2005):
Nonlinear equalization for frame-differential IR-UWB receivers.
In: 2005 IEEE International Conference on Ultra-Wideband, IEEE, pp. 576-581, [Online-Edition: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumbe...],
[Article]

Singerl, P. and Koeppl, H. (2005):
Volterra kernel interpolation for system modeling and predistortion purposes.
1IEEE, In: International Symposium on Signals, Circuits and Systems, 2005. ISSCS 2005., pp. 251-254, [Online-Edition: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumbe...],
[Conference or Workshop Item]

Shutin, D. and Koeppl, H. (2004):
Application of the Evidence Procedure to Linear Problems in Signal Processing.
735AIP, In: AIP Conference Proceedings, pp. 161-168, [Online-Edition: http://adsabs.harvard.edu/abs/2004AIPC..735..161S http://sci...],
[Conference or Workshop Item]

Koeppl, H. and Josan, A. S. and Paoli, G. and Kubin, G. (2004):
The Cramer-Rao Bound and DMT Signal Optimisation for the Identification of a Wiener-Type Model.
In: EURASIP Journal on Applied Signal Processing, 12pp. 1817-1830, [Online-Edition: http://asp.eurasipjournals.com/content/2004/12/642938],
[Article]

Koeppl, H. and Schwingshackl, D. (2004):
Comparison of discrete-time approximations for continuous-time nonlinear systems.
In: 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2IEEE, pp. ii-881, [Online-Edition: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumbe...],
[Article]

Koeppl, H. (2004):
Nonlinear System Identification for Mixed Signal Processing | Signal Processing and Speech Communication Laboratory.
Graz Universitay of Technology, Graz, Austria, [Online-Edition: http://www.spsc.tugraz.at/PhD_Theses/nonlinear-system-identi...],
[Ph.D. Thesis]

Koeppl, H. and Kubin, G. and Paoli, G. (2003):
Bayesian methods for sparse RLS adaptive filters.
2Pacific Grove, CA, USA, IEEE, In: The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003, pp. 1273-1277, [Online-Edition: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumbe...],
[Conference or Workshop Item]

Vogel, C. and Koeppl, H. (2003):
Behavioral Modeling of Time-Interleaved ADCs using MATLAB.
In: October, pp. 45-48, [Online-Edition: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.212....],
[Article]

Koeppl, H. and Paoli, G. and Kubin, G. (2003):
The Cramer-Rao bound for a factorizable Volterra system.
Grado, Italy, IEEE, In: IEEE Workshop on Nonlinear Signal and Image Processing, [Conference or Workshop Item]

Koeppl, H. and Paoli, G. (2002):
Non-Linear System Identification of a Broadband Subscriber Line Interface Circuitry Using the Volterra Approach.
In: Mathematics in Signal Processing V, V (Chapter 13), Oxford University Press, [Online-Edition: http://ukcatalogue.oup.com/product/9780198507345.do],
[Article]

Koeppl, H. and Paoli, G. (2002):
Non-linear modeling of a broadband SLIC for ADSL-Lite-over-POTS using harmonic analysis.
2Scottsdale, Arizona, USA, IEEE, In: 2002 IEEE International Symposium on Circuits and Systems. Proceedings (Cat. No.02CH37353), pp. II-133, [Online-Edition: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumbe...],
[Conference or Workshop Item]

Koeppl, H. (2001):
Identification of a non-linear analog circuitry for an ADSL application.
Karl-Franzens-Universität, Graz, Austria, [Online-Edition: http://search.obvsg.at/primo_library/libweb/action/dlDisplay...],
[Master Thesis]

Paoli, G. and Koeppl, H. (2001):
Non-linear identification and modeling of large scale analog integrated circuitties for DMT based applications.
Bratislava, Slovakia, In: Proc. of the Electronic Circuits and Systems Conference, pp. 84-87, [Online-Edition: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.18.9...],
[Conference or Workshop Item]

Koeppl, H. and Paoli, G. The Institute of Mathematics and its Applications (IMA) (Corporate Creator) (2000):
Non-Linear System Identification of a Broadband Subscriber Line Interface Circuit for ADSL-Lite Using the Volterra Approach.
In: 5th IMA International Conference on Mathematics in Signal Processing, Warwick, United Kingdom, pp. 359-362, [Conference or Workshop Item]