Siavash Haghiri

Siavash



University of Tübingen
Department of Computer Science
Sand 14
72076 Tübingen
Germany


Room: B107
Phone: +49 (0)7071 29-70465
E-mail: haghiri(at)informatik.uni-tuebingen.de


I am currently a PhD student in Theory of Machine Learning group (TML), at Tuebingen University, Computer science department. My main project is: ”Analysis of Functional Brain Networks of Stroke Patients”. I am also a member of the SFB936, "Multi-Site communication in the Brain", UKE, Hamburg. SFB936 is a research collaboration network based in Hamburg with more than 20 scientists in various fields concerning neuroscience and network science. Cognitive processes, such as perception, memory, emotion, or conscious awareness, are based on the activation of highly distributed networks in the brain involving numerous interacting modules and brain regions. In recent years there was enough evidence that many neurological and psychiatric disorders are due to disturbances in these complex networks. We as data scientist are concerned with “Graph Theoretical” analysis of these networks.

As a side project, I work on the machine learning problems with ordinal comparisons of distances. In the traditional settings of machine learning, the algorithm has access to actual distance/similarity values between pair of items. However this is not always the case. Consider a recommendation system in an online movie database. To learn the preferences of users we need the similarity of movies. This information is usually gathered from reviewers (users of the database) by asking them to provide answers to similarity questions among items. In this way, we ask the users to provide a similarity value in the range of 1 to 10 for pairs of items. These values highly depend on the users and can be biased. As an alternative approach one can ask if item A is more similar to item B or item C. Human beings are better in this kind of comparison tasks. We are concerned with developing algorithms, which take the ordinal data information and perform classic machine learning tasks, such as classification and clustering. Here you can find the related publications:


  • S. Haghiri, D. Garreau, U. von Luxburg, “Comparison-Based Random Forests”, accepted in ICML 2018.

  • S. Haghiri, D. Ghoshdastidar, U. von Luxburg, “Comparison-Based Nearest Neighbor Search”, in AISTATS 2017.

Prior to my PhD program, I received my B.Sc. and M.Sc. both in Computer Engineering from Sharif University of Technology, Iran. My research topic was: "Classification based on sparse representation". You can find the related publication here:

  • S. Haghiri, H. R. Rabiee, A. Soltani-Farani, S. A. Hosseini, M. Shadloo, "Locality Preserving Discriminative Dictionary Learning", IEEE Int. Conf. Image Processing, 2014.
You can also find more details about the project here.


Here is the link to my full CV.