CV: In 2002 Fabian Moerchen graduated from the University of Wisconsin, Milwaukee with the Master of Science. His thesis work on training neural networks in parallel on a cluster of computers was published in the IEEE Transactions of Neural Network. He joined the research group of Prof. Dr. Ultsch at the Philipps-University of Marburg, Germany and graduated with the PhD in 2006. The research on temporal pattern mining, music information retrieval, and self-organizing maps led to numerous conference and journal publications. At Siemens Corporate Research he worked from 2006 to 2012 as a research scientist and manager developing data driven decision support systems using machine learning and data mining. The applications included preventative maintenance, bio-informatics, medical imaging, financial risk scoring, and text mining. At Amazon, he started as manager in the Search & Discovery group using big data analytics to improve the vast product catalog of Amazon. He is currently a Principal Machine Learning Scientist for Amazon Music working on personalization, recommenders, bandits, offline policy evaluation, natural language understanding, entity resolution, and audio content understanding.
Moerchen, F., Ernst, P., Zappella, G.: Personalizing Natural Language Understanding using Multi-armed Bandits and Implicit Feedback, In Proceedings 29th ACM International Conference on Imformation and Knowledge Management, (2020) PDF
2019
Tatti, N., Moerchen, F., Calders, T.: Finding Robust Itemsets Under Subsampling, CoRR (2019), pp. 705-714 PDF
2016
Batal, I. and
Cooper, G. and
Fradkin, D. and
Harrison, J. and
Moerchen, F. and
Hauskrecht, M.: An Efficient Pattern Mining Approach for Event Detection in Multivariate Temporal Data, Knowledge and Information Systems 46(1)(2016), pp. 115-150
2015
Fradkin, D., Moerchen, F.: Mining Sequential Patterns for Classification, Knowledge and Information Systems 45(3)(2015), pp. 731-749
2014
Sipos, R., Fradkin, D., Moerchen, F., Wang, Z.: Log-based Predictive Maintenance, In Proceedings 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2014), pp. 1867-1876 PDF
Sipos, R., Fradkin, D., Moerchen, F., Wang, Z.: Log-based Predictive Maintenance, In Proceedings 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2014), pp. 1867-1876 PDF
Tatti, N., Moerchen, F.: Finding Robust Itemsets Under Subsampling, ACM Transactions on Database Systems 39(3)(2014) TODS
2013
Lam, H.T., Calders, T., Yang, J., Moerchen, F., Fradkin, D.: Zips: Mining Compressing Sequential Patterns in Streams, In Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics, (2013), pp. 54-62 IDEA
Lahiri, B., Moerchen, F., Akrotirianakis, I.: Finding Critical Thresholds for Defining Bursts in Event Logs, Transactions on Large-Scale Data- and Knowledge-Centered Systems (TLDKS) (2013) Technical Report
Wang, Q. and
Zhang, K. and
Marsic, I. and
Li, J. K. J. and
Moerchen, F.: Patient-friendly detection of early peripheral arterial
diseases (PAD) by budgeted sensor selection, In PervasiveHealth, (2012), pp. 89-96 IEEE
Lahiri, B., Moerchen, F., Akrotirianakis, I.: Finding Critical Thresholds for Defining Bursts, In Proceedings of the 13th International Conference on Data Warehousing and Knowledge Discovery (DaWaK), (2011), pp. 484-495 Technical Report
Malik, H.H., Fradkin, D., Moerchen, F.: Single Pass Text Classification by Direct Feature Weighting, Knowledge and Information Systems 28(1)(2011), pp. 79-98 Springer
Fradkin, D., Moerchen, F.: Margin-Closed Frequent Sequential Pattern Mining, In Proceedings Useful Patterns Workshop, Fifteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2010)
Malik, H.H., Kender, J.R., Fradkin, D., Moerchen, F.: Hierarchical document clustering using local patterns, Data Mining and Knowledge Discovery 21(1)(2010) Springer
Dejori, M., Malik, H.H., Moerchen, F., Neubauer, C.: Development of data infrastructure for the Long Term Bridge Performance program, In Proceedings of the Structures Congress (ASCE), (2009)
Yu, Y., Joe, K., Oria, V., Moerchen, F., Downie, J.S., Chen, L.: Multi-Version Music Search Using Acoustic Feature Union and Exact Soft Mapping, Int. J. Semantic Computing 3(2)(2009), pp. 209-234 PDF
2008
Moerchen, F.: Organic pie charts, In Proceedings IEEE International Conference on Data Mining, 30, (2008), pp. 947-952
Yu, Y., Downie, J.S., Moerchen, F., Chen, L., Joe, K.: Using Exact Locality Sensitive Mapping to Group and Detect
Audio-Based Cover Songs, In Proceedings 10th IEEE International Symposium on Multimedia (ISM), IEEE Computer Society, (2008), pp. 302-309 IEEE
Yu, Y., Downie, J.S., Moerchen, F., Chen, L., Joe, K., Oria, V.: COSIN: content-based retrieval system for cover songs, Abdulmotaleb El-Saddik and
Son Vuong and
Carsten Griwodz and
Alberto Del Bimbo and
K. Selccuk Candan and
Alejandro Jaimes (Eds), In Proceedings 16th ACM International Conference on Multimedia, ACM, (2008), pp. 987-988 ACM
Moerchen, F., Dejori, M., Fradkin, D., Etienne, J., Wachmann, B., Bundschus, M.: Anticipating annotations and emerging trends in biomedical literature, In Proceedings Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 26, (2008), pp. 954-962
Weihs, C., Ligges, U., Moerchen, F., Müllensiefen, D.: Classification in Music Research, Advances in Data Analysis and Classification 1(3)Springer, (2007), pp. 255-291 SpringerLink
Moerchen, F., Brinker, K., Neubauer, C.: Any-time clustering of high frequency news streams, In Proceedings Data Mining Case Studies Workshop (DMCS), The Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,, San Jose, CA, USA, 23, (2007)
Moerchen, F.: Algorithms For Time Series Knowledge Mining, Tina Eliassi-Rad, Lyle H. Ungar, Mark Craven, Dimitrios Gunopulos (Eds), In Proceedings The Twelveth ACM SIGKDD International Conference on Knowledge
Discovery and Data Mining, Philadelphia, PA, USA, (2006), pp. 668-673 ACM Digital Library
Noecker, M., Moerchen, F., Ultsch, A.: Fast and reliable ESOM learning, M. Verleysen (Eds), In Proceedings 14th European Symposium on Artificial Neural Networks
(ESANN'06), Bruges, Belgium, (2006), pp. 131-136
Moerchen, F., Ultsch, A., Noecker, M., Stamm, C.: Visual mining in music collections, In Proceedings 29th Annual Conference of the German Classification Society
(GfKl 2005), Springer, Heidelberg, (2005), pp. 724-731
Moerchen, F., Ultsch, A.: Finding persisting states for knowledge discovery in time series, In From Data and Information Analysis to Knowledge Engineering - Proceedings 29th Annual Conference of the German Classification Society (GfKl 2005), Magdeburg, Germany, Springer, Heidelberg, (2005), pp. 278-285
url SpringerLink
Moerchen, F., Ultsch, A., Thies, M., Loehken, I. and
Noecker, M., Stamm, C., Efthymiou, N., Kümmerer,
M.: MusicMiner: Visualizing timbre distances of music as topographical
maps, Technical Report No. 47, Dept. of Mathematics and Computer Science, University of Marburg,
Germany, (2005)
Moerchen, F., Ultsch, A.: Discovering Temporal Knowledge in Multivariate Time Series, Weihs, C., Gaul, W. (Eds), In Classification; The Ubiquitous Challenge, Proceedings 28th Annual
Conference of the German Classification Society (GfKl 2004), Dortmund, Germany, Springer, Heidelberg, (2005), pp. 272-279
2004
Moerchen, F., Ultsch, A., Hoos, O.: Discovering interpretable muscle activation patterns with the Temporal
Data Mining Method, Jean-Franccois Boulicaut, Floriana Esposito, Fosca Giannotti
and Dino Pedreschi (Eds), In Knowledge Discovery in Databases: Proceedings 8th European Conference
on Principles and Practice of Knowledge Discovery in Databases (PKDD
2004), Pisa, Italy, Springer, (2004), pp. 512-514
Moerchen, F., Ultsch, A.: Mining Hierarchical Temporal Patterns in Multivariate Time Series, Susanne Biundo, Thom W. Frühwirth, Günther Palm (Eds), In KI 2004: Advances in Artificial Intelligence, Proceedings 27th Annual
German Conference in AI, Ulm, Germany, Springer, Heidelberg, (2004), pp. 127-140
Java code to visualize music collections with maps such that similar songs are placed close to each other and different sounding songs are far apart or seperated by high mountains.
