Quantum Computing Quantum computers are expected to accelerate scientific discovery spanning many different areas such as medicine, AI, material science, and financial predictions. Quantum hardware manipulates with much more complex than binary information that is represented in classical computers. We are interested in quantum algorithms and methods of their hybridization with classical computing systems as well as how classical computing can facilitate and improve quantum computing.
Machine Learning Many machine learning algorithms are prohibitive for large-scale number of variables, samples and high dimensionality. For example, this can happen because of the slow convergence or NP-hardness of underlying optimization problems (such as in support vector machines and cut-based clustering). We are interested in algorithms that cope with such problems.
AI, Literature Based Discovery and Text Mining Hypothesis generation is becoming a crucial time-saving family of techniques which allow researchers to quickly discover implicit connections between important concepts. We are interested in such techniques and complex text mining problems, in general. Applications include biomedical discovery with scientific texts, healthcare and social media.
Network Science and Graph Algorithms We are interested in computational, modeling, theory and data problems related to complex networks in social/natural/information sciences, and engineering. Their analysis often requires scalable algorithms for frequent pattern discovery, outliers detection, quantitative methods for importance ranking of network elements, time-dependent data analysis, evolution modeling, visualization, and community detection.
Combinatorial Scientific Computing This is an area in which we study discrete optimization problems on large-scale graphs that are used to accelerate the performance of scientific computing algorithms. Examples include (hyper)graph partitioning, reordering, and coloring to improve load-balancing, task mapping, and data locality on HPC.
Multiscale Methods A broad range of scientific problems involve multiple scales. Traditional monoscale approaches have proven to be inadequate, even with the largest supercomputers, because of the prohibitively large number of variables involved. We develop multiscale approaches in which a hierarchy of coarse scale approximations is used to solve large-scale problems efficiently.
