# People

**Aydın Buluç** is the director of Sparsitute. He is a Senior Scientist at the Lawrence Berkeley National Laboratory (LBNL) and an Adjunct Faculty at the Computer Science Division at UC Berkeley. His research interests include parallel computing, combinatorial scientific computing, high performance graph analysis and machine learning, sparse matrix computations, computational biology.

**Ramakrishnan Kannan** is the deputy director of Sparsitute. He is the group leader for Discrete Algorithms at Oak Ridge National Laboratory. His research expertise is in distributed machine learning and graph algorithms on HPC platforms and their application to scientific data with a focus on accelerating scientific discovery by reducing computation time from weeks to seconds.

**David Gleich** is a Professor and University Faculty Scholar in the Computer Science Department at Purdue University. His research is on novel models and fast large-scale algorithms for data-driven scientific computing including scientific data analysis, bioinformatics, and network analysis.

**Grey Ballard **is an Associate Professor in the Computer Science Department at Wake Forest University. His research interests include numerical linear algebra, high performance computing, and computational science, particularly in developing algorithmic ideas that translate to improved implementations and more efficient software.

**Edgar Solomonik** is an Associate Professor in the Computer Science Department at University of Illinois at Urbana-Champaign. His research interests include numerical linear algebra, parallel algorithms, tensor networks, tensor decompositions, high performance computing.

**Ariful Azad** is an Assistant Professor of Intelligent Systems Engineering (ISE) at Indiana University (IU) School of Informatics, Computing, and Engineering. His research focuses on parallel graph and sparse matrix algorithms, high performance computing, and their applications in scientific computing and bioinformatics.

**Sherry**** Li** is a Senior Scientist in the Applied Math and Computational Research Division, Lawrence Berkeley National Laboratory. She has worked on diverse problems in high performance scientific computations, including parallel computing, sparse matrix computations, high precision arithmetic, and combinatorial scientific computing.

**Aditya Devarakonda** is an Assistant Professor at Wake Forest University. His research interests are at the intersection of high performance computing and machine learning. His research group focuses on deriving s-step numerical optimization algorithms that avoid communication and achieve high performance on distributed-memory parallel systems.

**Aditya Devarakonda**is an Assistant Professor at Wake Forest University. His research interests are at the intersection of high performance computing and machine learning. His research group focuses on deriving s-step numerical optimization algorithms that avoid communication and achieve high performance on distributed-memory parallel systems.

**Dmitriy Morozov** is a staff scientist in the Machine Learning and Analytics group at the Lawrence Berkeley National Laboratory. His main interests are computational topology and geometry, especially, as they apply to data analysis.

**Pablo Moriano** is a research scientist in the Computer Science and Mathematics Division at Oak Ridge National Laboratory. Dr. Moriano's research lies at the intersection of data science, network science, and cybersecurity. In particular, he uses data-driven and computational methods to discover and understand critical security issues in large-scale networked systems

**Pablo Moriano**is a research scientist in the Computer Science and Mathematics Division at Oak Ridge National Laboratory. Dr. Moriano's research lies at the intersection of data science, network science, and cybersecurity. In particular, he uses data-driven and computational methods to discover and understand critical security issues in large-scale networked systems

**Piyush Sao** is a research scientist in the Discrete Algorithm Group. As part

of his research, he is developing algorithms for high-performance computing systems—such as Oak Ridge National Laboratory’s Summit and Frontier supercomputers—that are used to solve AI and scientific computing problems.

**Oguz Selvitopi **is a Research Scientist in the Performance and Algorithms group of Computer Science Department at Lawrence Berkeley National Laboratory. His research interests are high performance computing, parallel sparse matrix computations, combinatorial scientific computing, and bioinformatics.

**Oguz Selvitopi**is a Research Scientist in the Performance and Algorithms group of Computer Science Department at Lawrence Berkeley National Laboratory. His research interests are high performance computing, parallel sparse matrix computations, combinatorial scientific computing, and bioinformatics.

**Yang Liu** is a research scientist in the Scalable Solvers Group of the Computational Research Division at the Lawrence Berkeley National Laboratory. His main research interest is in numerical linear and multi-linear algebras (including sparse solvers, randomized low-rank, butterfly and tensor algebras), computational electromagnetics (including fast iterative time-domain integral equation solvers, fast direct integral and differential equation solvers, and multi-physics modeling), scalable machine learning algorithms, and high-performance scientific computing.

**Paul Laiu** is a Research Staff Mathematician in the Computational and Applied Mathematics (CAM) Group at Oak Ridge National Laboratory. His research interest includes numerical optimization, approximation theory, and numerical schemes for various partial differential equations in kinetic theory.

**Helen Xu** is the 2022 Grace Hopper Postdoctoral Fellow in Computing Sciences at Lawrence Berkeley National Laboratories. Her research interests include parallel computing, cache-efficient algorithms, and performance engineering.

**Tianyi Shi** is a postdoctoral fellow in the Scalable Solvers Group of the Computational Research Division at the Lawrence Berkeley National Laboratory. His main research interest is in numerical linear algebra, including low rank tensor formats and decomposition, randomized algorithms, and sparse solvers. He is also interested in spectral methods for partial differential equations, and high-performance scientific computing.

**Caio Alves **studied at the Mathematics department of the Federal University of Minas Gerais in Brazil, receiving his PhD in 2014. He went on to postdocs at the University of Campinas, the Max-Planck Institute for Mathematics in the Sciences in Germany, the University of Leipzig, and the Alfred Renyi Institute of Mathematics in Hungary. His field of study is discrete probability, more specifically random graph models, such as percolation models and preferential attachment graphs.