Nico Görnitz




Berlin Institute of Technology
Department of Software Engineering
and Theoretical Computer Science
Machine Learning Group
Marchstr. 23
Berlin, 10578, Germany

nico.goernitz[at]tu-berlin.de
 

[ Bio | Teaching | Research | Publications | Supplements | Code | Activities ]


Short Bio

    I received a diploma degree (MSc equivalent) in computer engineering (Technische Informatik) from the Berlin Institute of Technology with a thesis in machine learning for computer security. Currently, I am enrolled as a PhD student in the machine learning program at the Berlin Institute of Technology headed by Klaus-Robert Müller. From 2010-2011, I was also affiliated with the Friedrich Miescher Laboratory of the Max Planck Society in Tübingen where I was co-advised by Gunnar Rätsch. Since the machine learning in biology group of Gunnar Rätsch moved to the Memorial Sloan-Kettering Cancer Center in New York, 2012, I am singly employed in Berlin as part of the ALICE (Autonomous Learning in Complex Environments) team.


Teaching

  • Machine Learning II (Invited Lecture on structured output prediction, Berlin Institute of Technology, Winter 2010/2011)


Research Interests

    My primary interests cover machine learning and applications in computational biology, computer security, brain computer interfaces and renewable energies. This includes especially structured output prediction, latent variable models, density level-set estimation and anomaly detection, semi-supervised and transductive learning, tackling non-i.i.d. (label) noise as well as corresponding convex and non-convex optimization techniques. Specifically, I have been working on large-scale label sequence learning for gene and transcript prediction with next-generation sequence data, network intrusion detection using semi-supervised anomaly detection, and tackling systematic label noise with latent variable models.


Publications


Supplements


Code

  • mTIM: margin-based transcript mapper matlab/c implementation on GitHub
  • tilitools: LatentSVDD, SVDD, OcSVM, Structured Output SVM, Semi-supervised Anomaly Detection , lp-Norm Multiple Kernel Learning Wrapper, as python scripts on GitHub

Activities

2014
Nominated representative for BMBF founded ALICE project at the CeBIT trade show.

Reviewer: JMLR, ICPR
2013
ISCB Member

Reviewer: GCPR, MLSP, NIPS, TNNLS.

Program Comittee: IDA Seminar Schloss Wulkow.
2012
ISCB Member

Reviewer: ICPR, TNNLS.

Mentor: GSoC (Google Summer of Code); Building a generic structured output learning framework (for Shogun).
2011
ISCB Member

Program Committee: IJCAI (22th International Joint Conference on Artificial Intelligence).

Reviewer: DAGM, ICML, STCO.

Mentor: GSoC (Google Summer of Code); Building a generic structured output learning framework (for Shogun).
2010
Reviewer: CSDA, ECML, NIPS, TAAI.




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