Babak Hosseini

I want to design effective machine learning algorithms which are interpretable for both humans and machines.

I am a Researcher in the Pattern Recognition Lab. of TU Dortmund University working with Gernot Fink. My research focus is design and implementation of advanced machine learning algorithms for specialized embedded devices.

I did my Ph.D. study in the Machine Learning Lab. of Cognitive Interaction Technology Center (CITEC) at Bielefeld University under the supervision of Barbara Hammer. The subject of my Ph.D. project was Semantic analysis of motion data.

My academic background is in Machine learning, Robotics, and Control Theory, and I have vocational experience in industrial sectors as a control/intelligent systems engineer. The topic of my Master dissertation was Concept Learning and Transfer among Heterogeneous Agents.

I was fortunate to work with Majid Nili and Babak N. Araabi at the University of Tehran, and with Ali K. Sedigh at the K. N. Toosi University.

News

  • Oct 2019: Just started my work in Pattern Recognition group of TU Dortmund University

  • Aug 2019: Deep-Aligned Convolutional Neural Network for Skeleton-based Action Recognition and Segmentation is accepted at ICDM 2019

  • Aug 2019: Interpretable Multiple-Kernel Prototype Learning for Discriminative Representation and Feature Selection is accepted at CIKM 2019

  • June 2019: Interpretable Discriminative Dimensionality Reduction and Feature Selection on the Manifold is accepted at ECML 2019

  • March 2019: Large-Margin Multiple Kernel Learning for Discriminative Features Selection and Representation Learning is accepted at IJCNN 2019

  • Multiple-Kernel Dictionary Learning for Reconstruction and Clustering of Unseen Multivariate Time-series is accepted at ESANN 2019.

  • Confident Kernel Sparse Coding and Dictionary Learning is accepted at ICDM 2018.

  • Non-Negative Local Sparse Coding for Subspace Clustering is accepted at IDA 2018.

  • Feasibility Based Large Margin Nearest Neighbor Metric Learning is accepted at ESANN 2018.

  • Talk on Non-negative Kernel Sparse Coding Frameworks for Efficient Analysis of Motion Data at BMVA Symposium on Human Activity Recognition and Monitoring 2017.

  • Non-Negative Kernel Sparse Coding for Analysis of Motion Data is accepted at ICANN 2016.

  • Efficient Metric Learning for the Analysis of Motion Data is accepted at DSAA 2015.

Research Interests

  • Interpretable Machine Learning

  • Deep learning

  • Time-series analysis

  • Applied Machine Learning

  • Kernel-learning


Education

  • PhD, Computer Science 2019 (expected)

Intelligent Systems PhD program

Bielefeld University

  • MSc, Control Engineering 2009

Focus on Machine Learning and Robotics

University of Tehran

  • BSc, Control Engineering 2006

K. N. Toosi University

Selected Publications and Projects

Kernel Based Dictionary Learning for Discriminative Representation of Multivariate Time-series

Babak Hosseini, Francois Petitjean, Germain Forestier, Barbara Hammer.

Working article


Deep-Aligned Convolutional Neural Network for Skeleton-based Action Recognition and Segmentation

Babak Hosseini, Romain Montagne , Barbara Hammer.

ICDM 2019, Beijing

Interpretable Multiple-Kernel Prototype Learning for Discriminative Representation and Feature Selection

Babak Hosseini, Barbara Hammer.

CIKM 2019, Beijing

Interpretable Discriminative Dimensionality Reduction and Feature Selection on the Manifold

Babak Hosseini, Barbara Hammer.

ECML 2019, Wurzburg

[Paper] [slides]

Large-Margin Multiple Kernel Learning for Discriminative Features Selection and Representation Learning

Babak Hosseini, Barbara Hammer.

IJCNN 2019, Budapest

[Paper] [slides]

Multiple-Kernel Dictionary Learning for Reconstruction and Clustering of Unseen Multivariate Time-series.

Babak Hosseini, Barbara Hammer.

ESANN 2019, Bruges.

[Paper] [Slides]

Confident Kernel Sparse Coding and Dictionary Learning.

Babak Hosseini, Barbara Hammer.

ICDM 2018, Singapore.

[Paper] [Slides]

Non-Negative Local Sparse Coding for Subspace Clustering

Babak Hosseini, Barbara Hammer.

IDA 2018, 's-Hertogenbosch.

[Paper] [Slides]

Feasibility Based Large Margin Nearest Neighbor Metric Learning

Babak Hosseini, Barbara Hammer.

ESANN 2018, Bruges.

[Paper]

Non-Negative Kernel Sparse Coding for the Analysis of Motion Data

Babak Hosseini, Felix Hülsmann, Mario Botsch, Barbara Hammer.

ICANN 2016, Barcelona.

[Paper] , [Code]

Efficient Metric Learning for the Analysis of Motion Data

Babak Hosseini, Barbara Hammer.

DSAA 2015, Paris.

[Paper] [Code]

Abstract Concept Learning Approach Based on Behavioural Feature Extraction

Babak Hosseini, Majid Nili, Babak N. Araabi

ICCEE 2009, Dubai.


Contact

babak[dot]hosseini[at]cs[dot]uni[dash]dortmund[dot]de