Prof. Dr. Thomas Ott

Work at ZHAW

Position

Director of Institute of Computational Life Sciences

Focus

Bio-inspired Computing, Applied Neuroinformatics (Pattern Recognition, Machine Learning), Modelling of Complex Systems Forecast methodologies for traffic and logistics Digital Health Assistance Systems

Teaching

• Systeme und Modelle der Physik (BSc ADLS)
• Optimisation Methods and Bio-inspired Algorithms (MSc Life Sciences ACLS)

Network

Membership of networks

• LZSBN Life Sciences Zurich Business Network
• Biotechnet Switzerland

ORCID digital identifier

ORCID ID: 0000-0003-1635-7816

Social media

Linkedin

Projects

• Morphological Marker-Assisted Breeding and Selection (MoMABS) in Peach / Project leader / ongoing
• Shapescience – AI for morpholigcally based fruit variety recognition / Project leader / ongoing
• Stability of self-organizing net fragments as inductive bias for next-generation deep learning / Team member / completed
• An experimental framework to allow evidence-based sustainability policymaking / Project leader / completed
• Extension SARS-CoV-2 Intervention modelling / Team member / completed
• Predictive Analytics for Hospital Supply Chain Management / Project leader / completed
• PiaBreed: Machine Learning for automated ovulation and birth monitoring in horses / Project leader / completed
• Fighting bites with bytes: Promoting public health with crowdsourced tick prevention / Co-project leader / completed
• Smartstones – AI for plant breeding / Project leader / completed
• Neuronal Growth Modelling (BioDynaMo Initial Project) / Project leader / completed
• Infectiology++ – Germ Tracking / Project leader / completed
• Next Generation: Neural Recommendation System / Project leader / completed
• Efficient Urban Pluvial Flood Simulation / Team member / completed
• Comprehensive Sales Forecasting for Supply Chain Optimization in Food Industry / Project leader / completed
• Multi-Asset Investment Process using Bayes Ensembles of Trading Models / Project leader / completed
• Intelligent sensor-based assistance system for retail / Project leader / completed
• Design of an online expert system based on Bayes’ian Networks and its first market implementation / Project leader / completed
• Remote sensing for grape ripeness analysis / Project leader / completed
• Clustering of high-throughput flow-cytometry data / Project leader / completed
• Online clustering for crowdsourcing platform / Project leader / completed
• Forecasting techniques and systems for congestions / Project leader / completed
• Feasibility study for IR-based discrimination of fruits and vegetables / Project leader / completed
• A tool for coherence analysis and clustering of survey data / Project leader / completed
• Machine learning methods for wine IR spectra analysis / Project leader / completed
• Coherence measures / Project leader / completed
• Statistical Analysis of E-Recycling Management SWCO / Project leader / completed

Publications

Articles in scientific journal, peer-reviewed

• Wróbel, A., Sandamirskaya, Y., & Ott, T. (2025). Implementing a neuromorphic classifier for NIR spectra analysis of fruits. Nonlinear Theory and Its Applications, 16(2), 222–232. https://doi.org/10.1587/nolta.16.222
• Glüge, S., Balabanov, S., Koelzer, V. H., & Ott, T. (2023). Evaluation of deep learning training strategies for the classification of bone marrow cell images. Computer Methods and Programs in Biomedicine, 243, 107924. https://doi.org/10.1016/j.cmpb.2023.107924
• Epper, P., Glüge, S., Vidondo, B., Wróbel, A., Ott, T., Sieme, H., Kaeser, R., & Burger, D. (2023). Increase of body temperature immediately after ovulation in mares. Journal of Equine Veterinary Science, 127(104565). https://doi.org/10.1016/j.jevs.2023.104565
• Garcia, V., Horn, C., & Ott, T. (2022). The sciences of data – moving towards a comprehensive systems perspective. Archives of Data Science, Series A, 8(2). https://doi.org/10.5445/IR/1000150241
• Müller, A., Glüge, S., Vidondo, B., Wróbel, A., Ott, T., Sieme, H., & Burger, D. (2022). Increase of skin temperature prior to parturition in mares. Theriogenology, 190, 46–51. https://doi.org/10.1016/j.theriogenology.2022.07.007
• Uwate, Y., Nishio, Y., & Ott, T. (2021). Synchronization of chaotic circuits with stochastically-coupled network topology. International Journal of Bifurcation and Chaos, 31(1), 2150015. https://doi.org/10.1142/S0218127421500152
• Uwate, Y., Takamaru, Y., Ott, T., & Nishio, Y. (2019). Clustering using chaotic circuit networks with weighted couplings. International Journal of Bifurcation and Chaos, 29(4), 1950053. https://doi.org/10.1142/S0218127419500536
• Tanner, C., Narvaez, D., Christen, M., & Ott, T. (2016). Using thesauruses as a heuristics for mapping values. Cognitive Systems Research, 40, 59–74. https://doi.org/10.1016/j.cogsys.2016.02.003
• Christen, M., Niederberger, T., Ott, T., Aryobsei, S., & Hofstetter, R. (2015). Micro-text classification between small and big data. Nonlinear Theory and Its Applications, 6(4), 556–569. https://doi.org/10.1587/nolta.6.556
• Schwendner, P., Schüle, M., Ott, T., & Hillebrand, M. (2015). European government bond dynamics and stability policies : taming contagion risks. Journal of Network Theory in Finance, 1(4), 1–25. https://doi.org/10.21314/JNTF.2015.012
• Christen, M., Ott, T., & Schwarz, D. (2013). A new measure for party coherence : applying a physics-based concept to the Swiss party system. Advances in Complex Systems, 16(6), 1350011. https://doi.org/10.1142/S0219525913500112
• Savi, D., Kasser, U., & Ott, T. (2013). Depollution benchmarks for capacitors, batteries and printed wiring boards from waste electrical and electronic equipment (WEEE). Waste Management, 33(12), 2737–2743. https://doi.org/10.1016/j.wasman.2013.08.014
• Landis, F., Ott, T., & Stoop, R. (2010). Hebbian self-organizing integrate-and-fire networks for data clustering. Neural Computation, 22(1), 273–288. https://doi.org/10.1162/neco.2009.12-08-926

Book chapters, peer-reviewed

• Ott, T., Glüge, S., Bödi, R., & Kauf, P. (2019). Economic measures of forecast accuracy for demand planning : a case-based discussion. In M. Braschler, T. Stadelmann, & K. Stockinger (Eds.), Applied data science : lessons learned for the data-driven business (pp. 371–386). Springer. https://doi.org/10.1007/978-3-030-11821-1_20
• Christen, M., & Ott, T. (2013). Quantified coherence of moral beliefs as predictive factor for moral agency. In B. Musschenga & A. van Harskamp (Eds.), What makes us moral? : on the capacities and conditions for being moral (pp. 73–96). Springer. https://doi.org/10.1007/978-94-007-6343-2_5

Written conference contributions, peer-reviewed

• Wróbel, A., Sandamirskaya, Y., & Ott, T. (2023, September). A spiking neural network for classifying NIR spectra of fruits. International Symposium on Nonlinear Theory and Its Applications (NOLTA), Catania, Italy, 26-29 September 2023. https://doi.org/10.21256/zhaw-30070
• Gygax, G., Ratnaweera, N., Tischhauser, W., Smits, T. H. M., Laube, P., & Ott, T. (2023). An oracle for the optimization of underconstrained compositions of neural networks : the tick hazard use case [Conference paper]. In J. J. Schneider, M. S. Weyland, D. Flumini, & R. M. Füchslin (Eds.), Artificial Life and Evolutionary Computation (pp. 24–31). Springer. https://doi.org/10.1007/978-3-031-23929-8_3
• Füchslin, R. M., Ambühl, J., Faggian, A., Fellermann, H. M., Flumini, D., Geller, A., Hanczyc, M. M., Klinkert, A., Krütli, P., Matuttis, H.-G., Ott, T., Scheidegger, S., Schmid, G. B., Smieszek, T., Schneider, J. J., Steiner, A., & Weyland, M. S. (2023, January 22). Ethical aspects of computational modelling in science, decision support and communication. Artificial Life and Evolutionary Computation. https://doi.org/10.1007/978-3-031-23929-8_24
• Vachey, G., & Ott, T. (2022). Self-organizing maps of artificial neural classifiers : a brain-like pin factory [Conference paper]. In J. J. Schneider, M. S. Weyland, D. Flumini, & R. M. Füchslin (Eds.), Artificial Life and Evolutionary Computation (pp. 163–171). Springer. https://doi.org/10.1007/978-3-031-23929-8_16
• Uwate, Y., Ott, T., & Nishio, Y. (2021). Effect of stochastically coupling on frustrated triangular oscillatory network. 2021 IEEE International Symposium on Circuits and Systems (ISCAS). https://doi.org/10.1109/ISCAS51556.2021.9401462
• Uwate, Y., Nishio, Y., & Ott, T. (2021). Frustrated complex networks of nonlinear circuits with stochastically coupling [Conference paper]. 2020 International SoC Design Conference (ISOCC), 181–182. https://doi.org/10.1109/ISOCC50952.2020.9333023
• Gygax, G., Füchslin, R. M., & Ott, T. (2020). Self-organized division of labor in networks of forecasting models for time series with regime switches [Conference paper]. Proceedings of the NOLTA 2020 Conference, 278–281.
• Miniussi, M., Ott, T., & Fellermann, H. (2020). Impact of noise and network size in coupled maps with asymmetric influence amplification [Conference paper]. Proceedings of the NOLTA 2020 Conference, 282–285.
• Uwate, Y., Schüle, M., Ott, T., & Noshio, Y. (2020). Echo state network with chaos noise for time series prediction [Conference paper]. Proceedings of the 2020 International Symposium on Nonlinear Theory and Its Applications, 274.
• Wróbel, A., Gygax, G., Schmid, A., & Ott, T. (2020). Going for 2D or 3D? : investigating various machine learning approaches for peach variety identification [Conference paper]. In F.-P. Schilling & T. Stadelmann (Eds.), Artificial Neural Networks in Pattern Recognition (pp. 257–265). Springer. https://doi.org/10.1007/978-3-030-58309-5_21
• Füchslin, R. M., Schneider, J. J., Ott, T., & Walker, R. (2019). Simplified modeling of the evolution of skills in a spatially resolved environment [Conference paper]. In H. Fellermann, J. Bacardit, A. Goñi-Moreno, & R. M. Füchslin (Eds.), Proceedings of the Artificial Life Conference 2019 (pp. 324–330). Massachusetts Institute of Technology. https://doi.org/10.1162/isal_a_00182
• Ott, T., Schüle, M., Fellermann, H., & Uwate, Y. (2018). Structural evolution in networks of coupled maps with asymmetric influence amplification [Conference paper]. 2018 International Symposium on Nonlinear Theory and Its Applications (NOLTA2018), Tarragona, Spain, 2-6 September 2018, 546–549.
• Uwate, Y., Ott, T., & Nishio, Y. (2018). Producing complex networks using coupled oscillatory circuits with evolutionary connections [Conference paper]. IEEE International Symposium on Circuits and Systems (ISCAS), 1–5. https://doi.org/10.1109/ISCAS.2018.8351665
• Schüle, M., & Ott, T. (2018). Synchronization in cellular automata : the learning approach. 2018 International Symposium on Nonlinear Theory and Its Applications (NOLTA2018), Tarragona, Spain, 2-6 September 2018.
• Glüge, S., Böck, R., & Ott, T. (2017). Emotion recognition from speech using representation learning in extreme learning machines [Conference paper]. In C. Sabourin, J. Julian Merelo, U.-M. O’Reilly, K. Madani, & K. Warwick (Eds.), Proceedings of the 9th International Joint Conference on Computational Intelligence (pp. 179–185). SciTePress. https://doi.org/10.5220/0006485401790185
• Ott, T., Schüle, M., Held, J., Albert, C., & Stoop, R. (2016). Clustered multidimensional scaling with Rulkov neurons [Conference paper]. 2016 International Symposium on Nonlinear Theory and Its Applications, 389–392. https://doi.org/10.21256/zhaw-3532
• Schüle, M., Ott, T., & Schwendner, P. (2016). Forecasting correlation structures. Proceedings of the 2016 International Symposium on Nonlinear Theory and Its Applications.
• Glüge, S., Pomati, F., Albert, C., Kauf, P., & Ott, T. (2014). The challenge of clustering flow cytometry data from phytoplankton in Lakes [Conference paper]. In V. M. Mladenov & P. C. Ivanov (eds.), Nonlinear Dynamics of Electronic Systems 22nd International Conference, NDES 2014, Albena, Bulgaria, July 4-6, 2014. Proceedings (pp. 379–386). Springer. https://doi.org/10.1007/978-3-319-08672-9_45
• Eggel, T., Christen, M., & Ott, T. (2014). Generating low-dimensional denoised representations of nonlinear data with superparamagnetic agents [Conference paper]. Proceedings of the 2014 International Symposium on Nonlinear Theory and Its Applications (NOLTA2014), 180–183. https://doi.org/10.21256/zhaw-3565
• Bruckmann, D., Jackson, C., Ott, T., & Weidmann, U. (2014). Improving the forecast of freight transport demand using machine learning and time series analysis. 14th Swiss Transport Research Conference, Ascona, 14-16 May 2014.
• Nef, A., Glüge, S., Ott, T., & Kauf, P. (2014). Causality detection in complex time dependent systems examplified in financial time series [Conference paper]. Proceedings of the 2014 International Symposium on Nonlinear Theory and Its Applications (NOLTA2014), 176–179. http://www.ieice.org/nolta/symposium/archive/2014/nolta14fullvol.pdf
• Uwate, Y., Ott, T., & Nishio, Y. (2013). Clustering phenomena in coupled chaotic circuits with different coupling strength. European Conference on Circuit Theory and Design (ECCTD), Dresden, Deutschland, 8-12 September 2013. https://doi.org/10.1109/ECCTD.2013.6662220
• Müller, C., Grabherr, P. M., Ott, T., Brombach, C., & Hauck, R. (2013, October). A user-friendly tool for assessing sustainability of menus in large-scale catering. Resource efficiency, governance and lifestyles : meeting report.
• Takamaru, Y., Uwate, Y., Ott, T., & Nishio, Y. (2012). Clustering phenomena of coupled chaotic circuits for large scale networks [Conference paper]. Nonlinear Dynamics of Electronic Systems, Proceedings of NDES 2012, 70–73. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6293766
• Niederberger, T., Stoop, N., Christen, M., & Ott, T. (2012). Hebbian principal component clustering for information retrieval on a crowdsourcing platform [Conference paper]. Proceedings of NDES 2012, 249–252. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6293769
• Niederberger, T., Merlo, O., & Ott, T. (2011). Predator-prey dynamics in Hopfield-type networks [Conference paper]. Proceedings of the 2011 International Symposium on Nonlinear Theory and Its Applications (NOLTA2011), 541–544. http://www.ieice.org/proceedings/NOLTA2011/nolta11fullvol.pdf
• Mürset, U., & Ott, T. (2010). Data clustering based on Hebbian learning in inhomogeneous coupled map lattices. Proceedings of NDES.
• Christen, M., Starostina, T., Schwarz, D., & Ott, T. (2009). A spin-based measure of the coherence of belief-systems : theory and application. Proceedings Od NDES′09.
• Mürset, U., & Ott, T. (2009). Semi-supervised classifiaction with single-layer isingtrons. Proceedings of NDES′09.
• Jasa, T., Stoop, R., Ott, T., & Lanz, T. (2008). Sequential superparamagnetic clustering as a predictor of visual fixations [Conference paper]. Proceedings of NOLTA (Nonlinear Theory and Applications), Budapest, Hungary, September 7-10, 2008, 120–123. http://www.ieice.org/proceedings/NOLTA2008/nolta08fullvol.pdf
• Schüle, M., Stoop, R., & Ott, T. (2008). Global dynamics of finite cellular automata [Conference paper]. Artificial Neural Networks - ICANN 2008 : Proceedings Part I, 71–78. https://doi.org/10.1007/978-3-540-87536-9_8

Other publications

• Füchslin, R. M., Krütli, P., Ott, T., Scheidegger, S., Schneider, J. J., Seric, M., Smieszek, T., & Weyland, M. S. (2022). Minimal models for spatially resolved population dynamics : applications to coexistence in multi – trait models [Conference paper]. ALIFE 2022: The 2022 Conference on Artificial Life, 75. https://doi.org/10.1162/isal_a_00504
• Kauf, P., & Ott, T. (2017). Smart together : lernfähige Prognose-Algorithmen unterstützen Menschen. Logistics Innovation, 2017(1), 32–35.
• Dorndorf, U., Kauf, P., & Ott, T. (2015). Bessere Absatzprognosen : eine Frage der Faktoren. Logistics Innovation, 2015(2), 8–11.
• Burkhard, M., Ott, T., Stoop, N., Kauf, P., Jackson, C. E., Frei, S., Ruesch, M., Hegi, P., Moreni, G., & Koy, T. (2014). Stauprognoseverfahren und -systeme : Forschungsprojekt ASTRA 2011/010. Bundesamt für Strassen.
• Takamaru, Y., Uwate, Y., Ott, T., & Nishio, Y. (2013). Dependence of clustering patterns on density of chaotic circuits in networks. Journal of Signal Processing, 17(4), 103–106. https://doi.org/10.2299/jsp.17.103
• Köhler, S., & Ott, T. (2013). Einflussfaktoren auf das Patientenaufkommen in Notfallstationen. Transfer, 2013(1), 7. https://doi.org/10.21256/zhaw-4415
• Koller, C., Kellenberger, R., Ott, T., & Meyer, T. (2013). PV-Tracking-System auf dem Skilift in Tenna - eine erste Auswertung : Annual Report 2012. In Programm Photovoltaik Ausgabe 2013 : Überblicksbericht, Liste der Projekte : Jahresberichte der Beauftragen 2012 (pp. 251–256). Bundesamt für Energie. https://netenergy.ch/fileadmin/webmaster/dokumente/Jahresberichte/PVJB2013_4th.pdf
• Krüsi, B., Lustenberger, P. I., Hepenstrick, D., & Ott, T. (2012). iGräser - das innovative und benutzerfreundliche mobile Gräserbestimmungs-Tool fürs iPhone. Ingenieurbiologie, 2012(3), 61–71. https://doi.org/10.21256/zhaw-2033
• Ott, T., & Krüsi, B. (2012). Zukünftige Bestimmungsschlüssel denken mit. Transfer, 2012(3), 6. https://doi.org/10.21256/zhaw-4410
• Christen, M., Ott, T., & Schwarz, D. (2011). Spaltpilz in den Parteien : mit Physik die Politik erklären. Neue Zürcher Zeitung.

Oral conference contributions and abstracts

• Schwendner, P., Schüle, M., Ott, T., & Hillebrand, M. (2018). Sentiment in European sovereign bonds. 3rd European COST Conference on Mathematics for Industry in Switzerland, Winterthur, 6 September 2018. https://www.zhaw.ch/storage/engineering/institute-zentren/iamp/sp_acss/Schwendner_20180906.pdf
• Ott, T., Glüge, S., Schüle, M., & Hill, C. (2018, June). A dynamic network approach for the analysis of pathogen transmission chains. The 26th Nonlinear Dynamics of Electronic Systems Conference, (NDES 2018), Acireale, 11-13 June 2018.
• Schüle, M., Ott, T., & Schwendner, P. (2018, June). Influence networks in financial markets : forecast scenarios. NDES 2018, 26th Nonlinear Dynamics of Electronic Systems Conference, Acireale, Italy, June, 11-13 2018.
• Schüle, M., Ott, T., & Schwendner, P. (2017, June 6). Forecasting correlation structures. NDES 2017, 25th Nonlinear Dynamics of Electronic Systems Conference, Zernez, 5-7 June 2017. https://www.ini.uzh.ch/~lorimert/NDES2017/assets/NDES2017_programme_booklet.pdf
• Ott, T., & Füchslin, R. M. (2017, June 5). The future’s back : first generic models of self-defeating road traffic forecasting. NDES 2017, 25th Nonlinear Dynamics of Electronic Systems Conference, Zernez, 5-7 June 2017.
• Kauf, P., & Ott, T. (2016). Humans and algorithms : creation and measurement of economic value in demand forecasting. 3rd Swiss Conference on Data Science, Winterthur, 16. September 2016. http://www.zhaw.ch/storage/hochschule/institute-zentren/datalab/SDS/2016/Slides/kauf.pdf
• Ott, T. (2016). (Machine × Human Learning)^n – Partners Learning Together : Keynote. CeBIT 2016, Hannover, Deutschland, 14.-18. März 2016.
• Hillebrand, M., Ott, T., Schüle, M., & Schwendner, P. (2016). European government bond dynamics and stability policies. ADEMU Workshop on Risk-Sharing Mechanisms for the European Union, Fiesole, Italy, 20-21 May 2016.
• Ott, T. (2015). A spin physics-based framework for hybrid recommendation systems. NDES 2015, 23th Nonlinear Dynamics of Electronic Systems Conference, Como, Italy, 2015.
• Ott, T. (2015). Big Data und Produktionssteuerung in der Lebensmittel-Industrie. Wädenswiler Lebensmitteltagung: Lebensmittel 4.0, Wädenswil, 19. November 2015.
• Ott, T. (2014). Complex forecasting. Rüdlinger Tagung 2014, Rüdlingen, 9.-10. September 2014.
• Ott, T. (2014). Stauentwicklungen : Daten- und modellgestützte Erkenntnisse zur Verkehrsflussprognose und Verkehrsflussoptimierung. 1. Nationaler Anti-Stautag Viasuisse, Luzern, 19. Mai 2014.
• Niederberger, T., Ott, T., Albert, C., & Pomati, F. (2013). A sequential classification concept using flawed training data : an application to flow cytometry. NDES 2013, 21st Nonlinear Dynamics of Electronic Systems Conference, Bari, Italy, 2013.
• Ott, T., Christen, M., Niederberger, T., Aebersold, R., Suleiman, A., & Hofstetter, R. (2013). A semi-supervised learning system for micro-text classification. NDES 2013, 21st Nonlinear Dynamics of Electronic Systems Conference, Bari, Italy, 2013.
• Grabherr, P. M., Ott, T., Brombach, C., Hauck, R., & Müller, C. (2013, March). Benutzerfreundliches Tool zur Nachhaltigkeitsbeurteilung von Menüs in der Gemeinschaftsgastronomie. 50. Wissenschaftlicher Kongress der DGE, Bonn, Deutschland, 20.-22. März 2013.

Publications before appointment at the ZHAW

• • T. Ott and R. Stoop; Benefits and Pitfalls of Belief Propagation-mediated Superparamagnetic Clustering. Physical Review E 74 (4): 042103, 2006. • M. Christen, A. Nicol, K. Kendrick, T. Ott and R. Stoop; Odour Encoding in Olfactory Neuronal Networks Beyond Synchronisation. Neuroreport, 17 (14): 1499-1503, 2006. • M. Christen, A. Kohn, T. Ott and R. Stoop; Measuring Spike Pattern Reliability with the Lempel-Ziv Distance. Journal of Neuroscience Methods, 156: 342-350, 2006. • T. Ott and R. Stoop; The Neurodynamics of Belief Propagation on Binary Markov Random Fields. Advances in Neural Information Processing Systems NIPS ’06, 2006. • R. Stoop and T. Ott; Towards a Quantitative Theory of Biocomputation. Proc. of NOLTA ’06 Bologna, 2006. • N. Stoop, T. Ott and R. Stoop; Loopy Belief Propagation: Benefits and Pitfalls on Ising-like Systems. Proc. of NOLTA ’06 Bologna, 2006. • T. Ott, A. Kern and R. Stoop; Faster Spike Sorting with Belief Propagation. Proc. of NOLTA ’06 Bologna, 2006.
• • A. Kern, T. Ott and R. Stoop; Acoustic Source Separation by Atomic Signal Decomposition. Proceedings of NOLTA ’06 Bologna, 2006. • A. Kern, T. Ott and R. Stoop; Source Separation by Atomic Signal Decomposition. Proc. of NDES’06, p. 77-80, 2006. • T. Ott, M. Christen and R. Stoop; An Unbiased Clustering Algorithm Based on Self-organisation processes in Spiking Neural Networks. Proc. of NDES’06, p. 143-146, 2006. • Y. Uwate, T. Ott, R. Stoop and Y. Nishio; Performance of Feedforward Neural Network with External Influence Function for Back Propagation Learning. RISP InternationalWorkshop on Nonlinear Circuits and Signal Processing (NCSP’06), 2006. • T. Ott, A. Kern, W.-H. Steeb and R. Stoop; Sequential Clustering: Tracking Down the Most Natural Clusters. Journal of Statistical Mechanics: theory and experiment: P11014, 2005.
• • M. Christen, T. Ott, A. Kern, N. Stoop and R. Stoop; Periodic Economic Cycles: The Effect of Evolution and Control. Journal of Statistical Mechanics: theory and experiment: P11013, 2005. • T. Ott, M. Frey, R. Stoop; Criticality and Computation in Random Threshold Networks with Noise. Proc. of NOLTA 2005 Bruges, 2005. • T. Ott, J. Dauwels and R. Stoop; Sequential Clustering by Loopy Belief Propagation. Proc. of ECCTD 2005 Cork, 2005. • T. Ott, A. Kern, A. Schuffenhauer, M. Popov, P. Acklin, E. Jacoby and R. Stoop; Sequential Superparamagnetic Clustering for Unbiased Classification of High-dimensional Chemical Data. Journal of Chemical Information and Computer Sciences, 44 (4): 1358-1364, 2004.
• • J.-J. van der Vyver, M. Christen, N. Stoop, T. Ott, W.-H. Steeb and R. Stoop; Towards Genuine Machine Autonomy. Robotics and Autonomous Systems, 46 (3): 151-157, 2004. • T. Ott and R. Stoop; Human-like Perception Using Sequential Superparamagnetic Clustering. Proceedings of NOLTA’04 Fukuoka, 2004. • T. Ott, W.-H. Steeb and R. Stoop; The Stochastic Network Approach to Clustering. Proc. of NOLTA’04 Fukuoka, 2004.   • M. Christen, T. Ott and R. Stoop; Spike Train Clustering Using a Lempel-Ziv Complexity-based Distance Measure. Proc. of NOLTA’04 Fukuoka, 2004.

Affiliations

Current Affiliations

• Prognosix AG: Member of Board of Directors
  Member of Board of Directors, Zürich
  03 / 2017 - today
• Life Sciences Zurich Business Network: Member of Board of LSZBN
  Member of the managing board of the Life Science Zurich Business Network, Zürich
  06 / 2024 - today
• Biotechnet Switzerland: Member of executive board of Biotechnet
  Member of the executive board of Biotechnet Switzerland, Muttenz
  01 / 2025 - today

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