Dr. Oliver Dürr

Dr. Oliver Dürr

Dr. Oliver Dürr
ZHAW School of Engineering
Rosenstrasse 3
8400 Winterthur

+41 (0) 58 934 67 47
oliver.duerr@zhaw.ch

Personal profile

https://oduerr.github.io/

Professional development teaching

Membership of networks

Projects

Project team leader

Project team member

Publications

Books and editorships

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Datenanalyse

: Anregungen zur Umsetzung der «Datenanalyse» im eidg. Rahmenlehrplan 2012 für die Berufsmaturität.

Winterthur: ZHAW.

Peer-reviewed articles/chapters

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Learning Embeddings for Speaker Clustering Based on Voice Equality

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In: Proceedings of the 27th IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2017). Roppongi, Tokyo, Japan: IEEE. Peer reviewed.

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Single-cell phenotype classification using deep convolutional neural networks

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Journal of biomolecular screening, 21, 9. 998-1003. Peer reviewed.

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Speaker Identification and Clustering using Convolutional Neural Networks

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In: Proceedings of IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2016). Salerno: IEEE. Peer reviewed.

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Gene Expression Signatures Predictive of Bevacizumab/Erlotinib Therapeutic Benefit in Advanced Nonsquamous Non-Small Cell Lung Cancer Patients (SAKK 19/05 trial)

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Clinical cancer research, 21, 23. 5253/5263. Peer reviewed.

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JOINT_FORCES: Unite Competing Sentiment Classifiers with Random Forest

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Proceedings of the International Workshop on Semantic Evaluation (SemEval-2014) Peer reviewed.

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Meta-Classifiers Easily Improve Commercial Sentiment Detection Tools

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Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC 2014) Peer reviewed.

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Tumor-associated stromal gene expression signatures predict therapeutic response to erlotinib/bevacizumab in non-small cell lung cancer (NSCLC)

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European Respiratory Journal, 44, Suppl 58. 821. Peer reviewed.

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Applied Data Science in Europe

: Challenges for Academia in Keeping Up with a Highly Demanded Topic.

European Computer Science Summit Peer reviewed.

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Potential and Limitations of Commercial Sentiment Detection Tools

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Proceedings of the First International Workshop on Emotion and Sentiment in Social and Expressive Media: approaches and perspectives from AI (ESSEM 2013) , 1096 47/58. Peer reviewed.

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Robust hit identification by quality assurance and multivariate data analysis of a high-content, cell-based assay

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J. Biomol. Screen. , 12, 8. 1042. Peer reviewed.

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Diffusion in polymer electrolytes and the dynamic percolation model

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Solid State Ionics, 149 125. Peer reviewed.

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Dynamic percolation theory for particle diffusion in a polymer network

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J. Chem. Phys., 117 441. Peer reviewed.

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Effective medium theory of conduction in stretched polymer electrolytes

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Journal of Physical Chemistry, 106 441. Peer reviewed.

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Melt viscosities of lattice polymers using a Kramers potential treatment

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J. Chem. Phys., 115 9042. Peer reviewed.

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Percolation concepts in solid state ionics

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Physica A, 266 229-237. Peer reviewed.

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Model studies of diffusion in glassy and polymer ion conductors

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Solid State Ionics: Science & Technology Proc. 6-th Asian Conf. on Solid State Ionics, edited by B. V. R. Chowdari and S. Chandra 33. Peer reviewed.

Non-peer-reviewed articles/chapters

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Deep Learning on a Raspberry Pi for Real Time Face Recognition

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EG 2015 - Posters 11-12.

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Using community structure for complex network layout

: Poster.

In: 19th Annual International conference on Intelligent Systems for Molecular Biology . Konferenz. (17. July 2011). Wien: international society for computational biology.

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Multivariate analysis of siRNA / High Content Screening data

: Vortrag.

In: Right Symposium on RNAi & High-Content Screening. Symposium . (12 September 2006). Dresden: Max Plank Institute.

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Quantifying Bioactivity on a Large Scale: Quality Assurance and Analysis of Multiparametric Ultra-HTS Data

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J. Association of Lab Automation, 207 10.

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Coupled ion and network dynamics in polymer electrolytes

: Monte Carlo study of a lattice model.

J. Chem. Phys., 121 12732 .

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Glassy and Polymeric Ionic Conductors: Statistical Modeling and Monte Carlo Simulations

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In: Superionic Conductor Physics. (77-80). Singapore: World Scientific.

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Charge Transport in Polymer Ion Conductors: a Monte Carlo Study

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In: Barndt, A; Bernholc, J; Binder, Kurt (Hg.). Multiscale Computational Methods in Chemistry and Physics (288-292). NATO Science Series, 177. Amsterdam: IOS Press.

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Stochastic modelling of ion diffusion in complex systems

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In: Lecture Notes in Physics. Anomalous diffusion from basics to applications . (175-185). Berlin: Springer.