Seminar za računarstvo i primenjenu matematiku, 3. i 5. oktobar 2023.

Naredni sastanci Seminara biće održani u utorak, 3. oktobra i četvrtak 5. oktobra 2023.  sa početkom u 14.15. Prvo predavanje biće održano na daljinu, dok će drugo predavanje biti održano u sali 301f Matematičkog instituta SANU i njega je takođe moguće pratiti na daljinu.
 
Predavač: Tatjana Jakšić-Kruger, Matematički institut SANU

Naslov predavanja: STATISTICAL CONSIDERATIONS ABOUT MODELING PERFORMANCE OF EXACT AND HEURISTIC ALGORITHMS ON PROBLEM INSTANCES OF PC||max

Apstrakt: When assessing a new algorithmic solution for an optimization problem, a set of problem instances is required on which the proposed algorithms may be compared against existing state-of-the art solvers. To achieve proper evaluation, we must identify key predictors of hardness and performance , i.e., algorithms ability to produce an optimal or best-known solution for a given problem instance.Considering the scheduling problem P||Cmax, we find that the existing literature focuses on problem size and the ratio of tasks to processors. Furthermore, existing methods do not systematically assess the influence of problem features in algorithm tests by considering the full range of values and all combinations of these values. In our presentation we will cover recent papers that addressed this issues for several known optimization problems.

This is a work in progress, realized jointly with Maria Brackin, Mohammed VI Polytechnic University, Rabat, Morocco, and Jana Živković and Momčilo Tošić, research internship students from the Faculty of Mathematics, Belgrade University.

Još jedan sastanak Seminara biće održan u četvrtak, 5. oktobra 2023, u sali 301f Matematičkog instituta SANU sa početkom u 13 časova. U pitanju je zajednički sastanak sa seminarom Odlučivanje - teorija, tehnologije i praksa
 
Predavač: Nataša Pržulj, Barcelona Supercomputing Center, Spain
 
Naslov predavanja: OMICS DATA FUSION FOR UNDERSTANDING MOLECULAR COMPLEXITY ENABLING PRECISION MEDICINE
 
Apstrakt: We are flooded by increasing volumes of heterogeneous, interconnected, systems-level, molecular (multi-omic) data. They provide complementary information about cells, tissues and diseases. We need to utilize them to better stratify patients into risk groups, discover new biomarkers, and repurpose known and discover new drugs to personalize medical treatment. This is nontrivial, because of computational intractability of many underlying problems, necessitating the development of algorithms for finding approximate solutions (heuristics).

We develop a versatile data fusion (integration) machine learning (ML) framework to address key challenges in precision medicine from these data: better stratification of patients, prediction of biomarkers, and re-purposing of approved drugs to particular patient groups, applied to cancer, Covid-19, rare thrombophilia and Parkinson’s Disease. Our new methods stem from graph-regularized non-negative matrix tri-factorization (NMTF), a machine learning technique for dimensionality reduction, inference and co-clustering of heterogeneous datasets, coupled with novel network science algorithms. We utilize our new framework to develop methodologies for improving the understanding the molecular organization and disease from the omics network embedding space.
 
Napomena: Registraciona forma za učešće na Seminaru je dostupna na linku:
https://miteam.mi.sanu.ac.rs/call/wnz6oyxsQsy29LfJA/MjQ__eH607WeAL9X7IFtUI98xdQQgVkp-ljiEKPPfXr

Ukoliko želite samo da pratite predavanje bez mogućnosti aktivnog učešća, prenos je dostupan na linku:
https://miteam.mi.sanu.ac.rs/asset/YoqHWKALRkRTbK9So


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