Program of DAMSS 2021 will be announced after registration of participants.
DAMSS 2021: Plenary Speakers
Assoc. Prof. Tatiana Tchemisova
Tatiana Tchemisova is an associate professor at the Department of Mathematics and researcher of CIDMA, Center for Research and Development in Mathematics and Applications at the University of Aveiro, Portugal. She graduated from the Belarusian State University and received PhD in Physical and Mathematical Sciences by the National Academy of Sciences of Belarus in 1996. During more than 20 years of teaching at the University of Aveiro, she lectured more than 20 different curricular units including subjects on linear, nonlinear, and network optimization, operations research for students of undergraduate and postgraduate level, supervised research seminars on convex, semi-infinite and semidefinite programming. Her expertise includes different aspects of mathematical optimization, mostly in continuous and convex optimization, semi-infinite and semidefinite optimization and optimization over convex cones. Another area of her research is related to mass transfer and minimal resistance problems, shape optimization and applications of optimization methods in Data Mining and Decision Support. Tatiana Tchemisova is the author and co-author of more than 40 scientific articles and several didactic textbooks. She is a member of the Editorial Boards of six international journals, served as Editor of several Proceedings Books of International Conference and as a Guest Editor of Special Issues in International Journals such as Optimization, DAM (Discrete and Applied Mathematics), and Springer series of Communications in Computer and Information Science (CCIS). She was chair and co-chair of about ten and member of organizing and scientific committees of more than fifty international conferences. During the last ten years, she was Vice-chair of the Managing Board of EUROPT -- "EURO Working Group on Continuous Optimization" and Vice-president of APDIO – Portuguese Association of Operations Research. Since 2020, Tatiana is a national representative of APDIO in "WISDOM Forum of EURO" (WISDOM--Women In Society: Doing Operational Research and Management Science) of EURO--European Association on Operations Research.
Talk title: Generating of non-regular instances of semidefinite programming problems
Abstract: Semidefinite programming (SDP) deals with the problem of minimizing linear functions subject to linear matrix inequalities (LMIs) and belongs to conic optimization. A wide variety of nonlinear convex optimization problems can be formulated as problems involving LMIs, and hence efficiently solved using recently developed interior-point methods. Semidefinite programming has been recognized in combinatorial optimization as a valuable technique for obtaining bounds on the solution of NP-hard problems. It provides important numerical tools for analysis and synthesis in systems and control theory, robust optimization, computational biology, systems and control theory, sensor network location, and data analysis, among others. Regularity is an important property of optimization problems. Various notions of regularity are known from the literature, being defined for different classes of problems. Usually, optimization methods are based on the optimality conditions, that in turn, often suppose that the problem is regular. The absence of regularity leads to theoretical and numerical difficulties, and solvers may fail to provide a trustworthy result. Therefore, it is very important to verify if a given problem is regular in terms of certain regularity conditions and in the case of nonregularity, to apply specific methods. On the other hand, in order to test new stopping criteria and the computational behaviour of new methods, it is important to have an access to sets of reasonably-sized nonregular test problems. We present a generator that constructs nonregular SDP instances with prescribed irregularity degrees and a database of nonregular test problems created using this generator. Numerical experiments using popular SDP solvers on the problems of this database permit us to conclude that the most efficient solvers are not efficient when applied to nonregular problems.
Prof. Dr. Rytis Maskeliunas
Prof. Dr. Rytis Maskeliunas currently works as a professor and as a chief researcher at the Faculty of Informatics in the Kaunas University of Technology. Rytis also serves as an invited professor at the Faculty of Applied Mathematics in the Silesian University of Technology. His main area of scientific research is applying modern methods of artificial intelligence in multimodal signal processing. He is an author /co-author of over 200 refereed research publications (h=19) and serves as an expert for multiple International scientific organizations, as well as an editor/reviewer/committee member for various International refereed journals. In this field, Rytis has experience of supervising 8 PhD students and he has coordinated/participated in multiple research projects in the Computer Science domain and was involved in the EU COST actions 278, 2102, IC1002, CA15122, CA16101 and currently is an MC member in CA19136.
Talk title: Deep learning in Alzheimer's disease
Abstract: Deep learning has shown tremendous potential in medical applications, not excluding hard to detect symptoms of Alzheimer’s and related diseases. Accessibility of data deriving from neuroimaging techniques, such as structural and functional MRI, positron emission tomography and imaging genetics allowed a breakthrough in clinical decision support. The presentation showcases a range of models and applications, discussing challenges and implications within this topic.
Prof. Dr. Audris Mockus
Audris Mockus has worked at AT&T, then Lucent Bell Labs and Avaya Labs for 21 years. Now he is the Ericsson-Harlan D. Mills Chair professor of Digital Archeology and Evidence Engineering in the Department of Electrical Engineering and Computer Science of the University of Tennessee. Dr. Mockus received a B.S. and an M.S. in Applied Mathematics from Moscow Institute of Physics and Technology in 1988. In 1991 he received an M.S. and in 1994 he received a Ph.D. in Statistics from Carnegie Mellon University.
Talk title: World of Code: Enabling a Research Workflow for Mining and Analyzing the Universe of Open Source VCS Data
Abstract: Open source software (OSS) is essential for modern society and, while substantial research has been done on individual (typically central) projects, only a limited understanding of the periphery of the entire OSS ecosystem exists. For example, how are the tens of millions of projects in the periphery interconnected through technical dependencies, code sharing, or knowledge flow? To answer such questions we: a) create a very large and frequently updated collection of version control data in the entire FLOSS ecosystems named World of Code (WoC), that can completely cross-reference authors, projects, commits, blobs, dependencies, and history of the FLOSS ecosystems and b) provide capabilities to efficiently correct, augment, query, and analyze that data. Our current WoC implementation is capable of being updated on a monthly basis and contains over 18B Git objects. To evaluate its research potential and to create vignettes for its usage, we employ WoC in conducting several research tasks. In particular, we find that it is capable of supporting trend evaluation, ecosystem measurement, and the determination of package usage. We expect WoC to spur investigation into global properties of OSS development leading to increased resiliency of the entire OSS ecosystem. Our infrastructure facilitates the discovery of key technical dependencies, code flow, and social networks that provide the basis to determine the structure and evolution of the relationships that drive FLOSS activities and innovation.
Prof. Dr. Pasi Fränti
Pasi Fränti received his MSc and PhD degrees from the University of Turku, 1991 and 1994 in Science. Since 2000, he has been a professor of Computer Science at the University of Eastern Finland. He has published 72 journals and 165 peer review conference papers, including 14 IEEE transaction papers. Pasi Fränti is the head of the Machine Learning research group. His current research interests include clustering algorithms, location-based services, data, web and text mining. He has supervised 25 PhD graduates and is currently supervising eight more.
Talk title: Web tools for analysing location-based data
Abstract: Tracking people location has become every day practice and created lots of new geotagged data. This presentation gives an overview for recent methods on analysing collected geotagged data via developed web applications. These include various GPS trajectories analysis including similarity, averaging, reduction, move type detection, distance calculation, clustering, and optimizing facility locations. Most methods are publicly available on the web either as demonstrations, APIs or web tools where user can upload his/her own data.
Each session talk is 15-20 minutes long. Poster sessions will provide an opportunity for authors to display the results and conclusions of their research. Posters will be exhibited during the workshop.
The recommended paper size of posters is A1 (84.1 cm x 59.4 cm.), language is English.