ὅδε οἶκος, ὦ ἑταῖρε, μνημεῖον ἐστιν ζῴων τῶν σοφῶν ἀνδρῶν, καὶ τῶν ἔργων αὐτῶν

Seminar on Computer Science and Applied Mathematics

 

PROGRAM


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.

PLAN RADA SEMINARA ZA OKTOBAR 2014. GODINE

Ponedeljak, 6.10.2014. u 14:15, Sala 301f, MI SANU:
Zoran Obradovic, L.H. Carnell Professor of Data Analytics Director, Data Analytics and Biomedical Informatics Center, Professor, Computer and Information Sciences Department, Professor, Statistics Department, Fox School of Business Temple University
NEURAL GAUSSIAN CONDITIONAL RANDOM FIELDS AND HOSPITAL PRICING ESTIMATION BY STRUCTURED REGRESSION AN A TEMPORAL GRAPH

Abstract:
First, we will show how to improve the representational power of Gaussian Conditional Random Field (GCRF) model for structured regression by (1) introducing an adaptive feature function that can learn nonlinear relationships between inputs and outputs and (2) allowing the weights of feature functions to be dependent on inputs. Experimental evaluation on the remote sensing problem of aerosol estimation from satellite measurements and on the problem of document retrieval showed that the proposed model is more accurate than the benchmark alternatives. Then, we will describe how we used GCRF to estimate unreported hospital charges by utilizing structured regression on a temporal graph of more than 4,000 hospitals observed over 8 years constructed from the US National Inpatient Sample database. The estimates of cost-to-charge ratio obtained using convex optimization of the GCRF parameters on the constructed graph were much better than those relying on group average based cost-to-charge estimates. In addition, cost-to-charge ratio estimates by our GCRF model outperformed regression by nonlinear artificial neural networks.

The first result is obtained in joint research with V. Radosavljevic and S. Vucetic and will be published at the Proc. European Conf. on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Nancy, France, September, 2014. The second result is obtained jointly with A. Polychronopoulou and will be published at the Proc. 2014 IEEE Int.l Conf, on Bioinformatics and Biomedicine, Belfast, UK, Nov. 2014.

Biography:
Zoran Obradovic's research interests include data mining and complex networks applications in health management. He is the executive editor at the journal on Statistical Analysis and Data Mining, which is the official publication of the American Statistical Association and is an editorial board member at eleven journals. He was general 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. In 2014-2015 he chairs the SIAM Activity Group on Data Mining and Analytics. His work is published in about 300 articles and is cited more than 13,000 times (H-index 44). For more details see http://www.dabi.temple.edu/~zoran

Utorak, 21.10.2014. u 14:15h, Sala 301f, MI SANU:
Jelena Lukic, Javno preduzece "Elektromreza Srbije", Beograd, Srbija
RAZVOJ MODELA POSLOVNE INTELIGENCIJE U B2B ELEKTRONSKOM POSLOVANJU ELEKTROPRIVREDE

Rezime: Rad predstavlja novi metodoloski pristup izgradnji skladista podataka i sistema poslovne inteligencije za medjunarodna trzista elektricne energije sa fokusom na modelovanje podataka. Pristup je zasnovan na principima i preporukama do kojih se doslo kritickom analizom Kimbalove i ASAP metodologije. Rezultati pokazuju na koji nacin ovaj metodoloski pristup moze da posluzi kao osnova za razvoj skladista podataka i poslovne inteligencije u energetskim sistemima. Pored toga, moze se koristiti u procesu projektovanja infrastrukture za upravljanje znanjem smart grid kompanija. Predlozeni pristup je testiran na primeru sistema za analizu trzista elektricne energije JP "Elektromreza Srbije".

RUKOVODIOCI SEMINARA

MI SANU
Vera Kovačević-Vujčić
Milan Dražić

FON
Zorica Bogdanovic
Marijana Despotovic-Zrakic

IEEE
Bozidar Radenkovic