Quantum Computing Quantum computing is creating new ways to approach challenging problems in optimization, simulation, artificial intelligence, scientific discovery, and data analysis. Our lab works broadly across the quantum computing stack, from fundamental algorithms to methods that connect quantum processors with large-scale classical computing systems. Our research includes quantum and hybrid quantum–classical algorithms, variational quantum algorithms, quantum optimization, quantum simulation, quantum circuit design and synthesis, compilation and qubit mapping, resource-efficient computation, and rigorous theoretical analysis of quantum algorithms. We are particularly interested in scalable methods that combine quantum computing with high-performance computing and AI, including AI for quantum computing (using machine learning and generative models to design, optimize, and control quantum computations) and quantum computing for AI, where quantum methods are developed for machine learning, generative modeling, and scientific discovery. A central theme of our work is understanding how quantum, classical, AI, and HPC technologies can be combined to solve problems that are difficult for any one computational paradigm alone.
Machine Learning Modern machine learning problems often involve enormous numbers of variables, samples, features, and interacting components, making conventional approaches computationally expensive or fundamentally difficult to optimize. Our research develops scalable machine learning and AI methods for problems involving large-scale optimization, complex structured data, graphs, and high-dimensional representations. We are particularly interested in algorithms that overcome slow convergence, combinatorial complexity, and NP-hard optimization subproblems, as well as methods that combine machine learning with optimization, scientific computing, graph algorithms, and emerging computing architectures.
AI, Literature Based Discovery and Text Mining Scientific literature contains an enormous amount of knowledge, but many important connections remain implicit across papers, disciplines, and data sources. We develop AI systems for hypothesis generation and literature-based discovery that identify such hidden relationships and help researchers generate and prioritize new scientific hypotheses. Our work combines natural language processing, large-scale knowledge graphs, graph learning, machine learning, and large language models with experimental and curated scientific databases. A major application is biomedical and drug discovery, where our systems integrate information from millions of scientific publications and large biological and chemical datasets. We are also interested more broadly in scientific text mining, knowledge discovery, information extraction, and AI systems that can reason over large collections of heterogeneous scientific evidence.
Graph Algorithms and Network Science Complex networks arise throughout science and engineering, including biological systems, social networks, communication systems, scientific knowledge graphs, transportation systems, and technological infrastructure. We study computational, algorithmic, modeling, and theoretical problems associated with such networks, with a particular emphasis on very large graphs. Our research includes scalable graph algorithms, community detection, graph partitioning, network optimization, anomaly and outlier detection, importance ranking, temporal and evolving networks, graph representation learning, visualization, and multilevel methods. We are especially interested in algorithms that uncover structure in networks too large or complex for conventional methods.
Combinatorial Scientific Computing Many of the most expensive computations in science and engineering contain large discrete optimization problems hidden inside numerical algorithms. Combinatorial Scientific Computing develops graph- and hypergraph-based methods for exposing and exploiting this structure. Our work includes large-scale graph and hypergraph partitioning, ordering, coloring, clustering, task mapping, and related optimization problems. These techniques are used to improve load balancing, communication, memory locality, parallelism, and overall performance on modern high-performance computing systems. We are particularly interested in algorithms that connect combinatorial optimization with scientific computing, HPC, AI, and emerging quantum computing platforms.
Multiscale Methods A broad range of scientific and optimization problems contain structure at multiple scales. Solving such problems directly at their finest resolution can require prohibitively large computational resources, even on modern supercomputers. We develop multiscale and multilevel algorithms that construct a hierarchy of progressively smaller representations of a problem, solve or analyze the problem at appropriate scales, and transfer information efficiently between them. These ideas originate in multigrid and multilevel scientific computing but extend naturally to graph algorithms, combinatorial optimization, machine learning, and quantum optimization. A major goal of our research is to use multiscale structure to make otherwise intractable problems computationally manageable.
