Наредни састанци Семинара биће одржани у уторак, 3. октобра и четвртак 5. октобра 2023. са почетком у 14.15. Прво предавање биће одржано на даљину, док ће друго предавање бити одржано у сали 301ф Математичког института САНУ и њега је такође могуће пратити на даљину.
Предавач: Татјана Јакшић-Кругер, Математички институт САНУ
Наслов предавања: STATISTICAL CONSIDERATIONS ABOUT MODELING PERFORMANCE OF EXACT AND HEURISTIC ALGORITHMS ON PROBLEM INSTANCES OF P||Cmax
Апстракт: 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., an algorithm’s 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.
Још један састанак Семинара биће одржан у четвртак, 5. октобра 2023, у сали 301ф Математичког института САНУ са почетком у 13 часова. У питању је заједнички састанак са семинаром Одлучивање – теорија, технологије и пракса
Предавач: Наташа Пржуљ, Barcelona Supercomputing Center, Spain
Наслов предавања: OMICS DATA FUSION FOR UNDERSTANDING MOLECULAR COMPLEXITY ENABLING PRECISION MEDICINE
Апстракт: 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.
Напомена: Регистрациона форма за учешће на Семинару је доступна на линку:
https://miteam.mi.sanu.ac.rs/call/wnz6oyxsQsy29LfJA/MjQ__eH607WeAL9X7IFtUI98xdQQgVkp-ljiEKPPfXr
Уколико желите само да пратите предавање без могућности активног учешћа, пренос је доступан на линку:
https://miteam.mi.sanu.ac.rs/asset/YoqHWKALRkRTbK9So