Sunday, September 22, 2024

What we do/Team/In news

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.

Machine Learning and Data Mining    Many standard machine learning and data mining algorithms are prohibitive for large-scale number of variables. 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    Computational, modeling, theory and data problems related to complex networks in social/natural/information sciences, and engineering. The analysis often includes 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    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.

Saturday, September 21, 2024

Best Paper Award in Quantum Algorithms and IEEE Quantum Week

Our team had an amazing time at IEEE Quantum Computing and Engineering 2024 (aka IEEE Quantum Week) in Montreal, where we were honored to receive the Best Paper in Quantum Applications Award for the paper "MLQAOA: Graph Learning Accelerated Hybrid Quantum-Classical Multilevel QAOA"! The conference was packed with excellent talks, workshops, tutorials and panels. 

It was also wonderful to catch up with our lab’s alumni, the Algorithms and Computational Science Lab legends Ankit Kulshrestha Joey Xiaoyuan Liu Ruslan Shaydulin, and see their success in the field. Graduate students Bao Bach and Cameron Ibrahim gave excellent talks on their papers. Big thanks to everyone who made this event such a success. This year there were over 1,550 participants from universities, national labs, and industry. Looking forward to even better event next year in Albuquerque, NM! 

#IEEEQuantum #QCE24 #QuantumComputing #QuantumWeek #IEEE #QuantumWeek2024





What we do/Team/In news

Quantum Computing     Quantum computers are expected to accelerate scientific discovery spanning many different areas such as medicine, AI, ...