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 AVGUST 2014. GODINE

Petak, 15.08.2014. u 14:15, Sala 301f, MI SANU:
Joint meeting with Mathematics Colloquium
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 (secondary appointment), Temple University
UTILIZING TEMPORAL PATTERNS FOR ESTIMATING UNCERTAINTY IN INTERPRETABLE EARLY DECISION MAKING

Abstract: Providing classification of time series as early as possible is vital in many domains including the medical, where early diagnosis can save patients. lives by providing early treatment. However, applications often require the method to be interpretable and have uncertainty estimates. These two aspects were not jointly addressed in previous studies, such that a difficult choice of selecting one of these aspects is required. To address this problem, in this study we propose a simple and yet effective method to provide uncertainty estimates for an interpretable early classification algorithm recently developed in our laboratory. The question we address here is "how to provide estimates of uncertainty in regard to interpretable early prediction." We showed that the proposed method is more effective than the state-of-the-art alternatives in providing reliability estimates in early classification, is simple to implement, and provides interpretable results.

This is joint research with M. Ghalwash and V. Radosavljevic and the results will be published at the Proc. 20th ACM SIGKDD Conf. on Knowledge Discovery and Data Mining, New York, NY, Aug. 2014.

Biography: Zoran Obradovic 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 Statistics, and is the Director of the Center for Data Analytics and Biomedical Informatics. His research interests include data mining and complex networks applications in health management. Zoran 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/

RUKOVODIOCI SEMINARA

MI SANU
Vera Kovačević-Vujčić
Milan Dražić

FON
Zorica Bogdanovic
Marijana Despotovic-Zrakic

IEEE
Bozidar Radenkovic