Seminar on Computer Science and Applied Mathematics

 

PROGRAM

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Matematički Institut SANU, Beograd
Knez Mihajlova 36
Fakultet organizacionih nauka, Univerzitet u Beogradu,
Jove Ilica 154
IEEE Chapter Computer Science (CO-16) Belgrade, Republic of Serbia

SEMINAR ZA RAČUNARSTVO I PRIMENJENU MATEMATIKU

MI SANU, Knez Mihailova 36, sala 301f

Upravni odbor Matematickog instituta SANU je na nedavnoj sednici doneo odluku da se dosadasnji Seminar za primenjenu matematiku, sada nazove Seminar za racunarstvo i primenjenu matematiku, a u cilju potenciranja znacaja racunarstva kao jedne od oblasti delatnosti Instituta. Istovremeno, Upravni odbor doneo je odluku o osnivanju Odeljenja za racunarstvo i primenjenu matematiku i vezao rad novog odeljenja za rad Seminara za racunarstvo i primenjenu matematiku.

PETAK, 01.09.2017. u 14:15, Sala 301f, MI SANU, Kneza Mihaila 36
Zoran Obradović, L.H. Carnell Professor of Data Analytics, Temple University, Philadelphia, USA
STRUCTURED REGRESSION IN LARGE TEMPORAL NETWORKS
In the first part of this talk we will present a novel sampling-based structured regression method for prediction on top of temporal networks. The algorithm allows efficient learning of an ensemble model by automatically skiping the entire re-training or some phases of the training process in an evolving environment. In conducted experiments the new method was about 140 time faster than alternative structured regression approaches while it was also more accurate as evident on modeling the H3N2 Virus Influenza network. The second part of the talk will describe an efficient algorithm to uncover the underlying dependency structure in high dimensional data. This is achieved by relaying on Cholesky decomposition to learn a sparse Gaussian Markov Random Field. The new method is applied to discover the connectivity structure among gene expressions in septic patients.
Results reported in this talk are published at:

1. Pavlovski, M., Zhou, F., Stojkovic, I., Kocarev, Lj., Obradovic, Z., "Adaptive Skip-Train Structured Regression for Temporal Networks", Proc. European Conf. Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), Sept. 2017.
2. Stojkovic, I., Jelisavcic, V., Milutinovic, V., Obradovic, Z., "Fast Sparse Gaussian Markov Random Fields Learning Based on Cholesky Factorization", Proc. 26th Int’l Joint Conf. Artificial Intelligence (IJCAI), Aug. 2017.

Zoran Obradovic an Academician at the Academia Europaea (the Academy of Europe) and a Foreign Academician at the Serbian Academy of Sciences and Arts. He is a L.H. Carnell Professor of Data Analytics at Temple University, Professor in the Department of Computer and Information Sciences with a secondary appointment in Department of Statistical Science, and is the Director of the Center for Data Analytics and Biomedical Informatics. His research interests include data science and complex networks in decision support systems. He is the executive editor at the journal on Statistical Analysis and Data Mining and is an editorial board member at eleven journals. He is the program co-chair for the IEEE Big Data 2017 conference and was co-chair for 2013 and 2014 SIAM International Conference on Data Mining and was the program or track chair at many data mining and biomedical informatics conferences. He also served as the chair at the SIAM Activity Group on Data Mining and Analytics for 2014 and 2015 years, He has published more than 350 articles and is cited more than 19,500 times (H-index 52). For more details see https://www.dabi.temple.edu/~zoran/


UTORAK, 05.09.2017. u 14:15, Sala 301f, MI SANU, Kneza Mihaila 36
Borko Stosic, Department of Statistics and Informatics, UFRPE, Brazil
(Departamento de Estatística e Informática Universidade Federal Rural de Pernambuco, Brazil)
RECENT ADVANCES IN NON-PARAMETRIC EFFICIENCY EVALUATION
Over the last years, Data Envelopment Analysis (DEA) has turned into probably the most widely used econometric tool for efficiency evaluation, while its "cousin" Free Disposal Hull (FDH) has received far less attention for practical applicability reasons. In this work some recent advances addressing practical applicability of these methods is presented. It is first shown how "granular" versions of Multiple Data Envelopment Analysis (MDEA) and Multiple Free Disposal Hull (MFDH) may enhance the performance of these methods. Next, the Jackstrap technique (combination of Jackknife and Bootstrap) for identification of outliers is addressed, and finally several parallelization strategies are compared.


PONEDELJAK, 18.09.2017. u 14:15, Sala 301f, MI SANU, Kneza Mihaila 36
Iztok Savnik, Faculty of mathematics, natural sciences and information technologies, University of Primorska, Slovenia
LARGE-SCALE GRAPH DATABASE SYSTEMS: SOME PROBLEMS AND SOLUTIONS
Large-scale graphs are stored and managed in the graph database systems where each arc is represented by a triple: (subject, predicate, object). The scalability of the storage system and the query processing engine for managing from Tera towards Peta triples is currently possible solely by using the distribution of data into the large shared-nothing clusters. The query execution system in such environment must be able to employ various types of parallelism to allow simultaneous execution of queries.
The seminar will present the problems and some solutions in the design and the implementation of large-scale distributed graph database system big3store. The system is implemented in Erlang programming environment. It is based on the dataflow architecture of query processing---each query is a tree of algebra operations that is dynamically mapped to the tree composed of processes interconnected by streams of graphs. The scheduler that maps query trees to the set of processes balances the computation load among the servers of the cluster. One of the leading ideas that has shaped the architecture of big3store is the use of the higher-level semantic data stored in the graph database, i.e., the so-called knowledge graph, to define the distribution of the graph database, as well as, to lead the query processing. The development of the big3store system is a joint project between Yahoo Japan Research and University of Primorska.
Biography: Iztok Savnik is a Docent at Faculty of mathematics, natural sciences and information technologies, University of Primorska, Slovenia. He is teaching courses in the areas of databases and programming languages. He has collaborated in a number of research and industrial projects with national Institute "Jozef Stefan". From 2013 he is working on a joint project with Yahoo Japan Research on the development of a large-scale distributed graph database system.


PETAK, 29.09.2017. u 14:15, Sala 301f, MI SANU, Kneza Mihaila 36
Angelo Sifaleras, Department of Applied Informatics, School of Information Sciences, University of Macedonia, Thessaloniki, Greece
OPTIMIZATION MODELING WITH JULIA AND GUROBI
In this lecture we present recent advances in optimization software packages. In particular, we will cover the JuMP, which consists an open-source modeling language that allows users to express a wide range of optimization problems (linear, mixed-integer, quadratic, conic-quadratic, semidefinite, and nonlinear) in a high-level, algebraic syntax. JuMP takes advantage of advanced features of the Julia high-level, high-performance, dynamic programming language for numerical computing. We also show here, how to solve mixed-integer optimization problems using the state-of-the-art Gurobi Optimizer, which exploits modern architectures and multi-core processors.
Related references:

1. Dunning I., Huchette J., and Lubin M., “JuMP: A modeling language for mathematical optimization”, SIAM Review, Vol. 59, No. 2, pp. 295-320, 2017.
2. Changhyun Kwon, Julia Programming for Operations Research: A Primer on Computing, CreateSpace Independent Publishing Platform, 2016.

RUKOVODIOCI SEMINARA

MI SANU
Vera Kovačević-Vujčić
Milan Dražić

FON
Zorica Bogdanovic
Marijana Despotovic-Zrakic

IEEE
Bozidar Radenkovic

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