Organization – XLOOP 2026

Workshop Chairs:

Workshop Steering Committee:

Workshop Program Committee:

Tekin Bicer (Argonne National Laboratory)
Stuart Campbell (Brookhaven National Laboratory)
Rudolph Dimper (European Synchrotron Radiation Facility)
Kjiersten Fagnan (Lawrence Berkeley National Laboratory)
Rafael Ferreira Da Silva (Oak Ridge National Laboratory)
Geoffrey Fox (University of Virginia)
Alex Hexemer (Lawrence Berkeley National Laboratory)
Tony Hey (Rutherford Appleton Laboratory)
Shantenu Jha (Brookhaven National Laboratory)
Wiebke Koepp (Lawrence Berkeley National Laboratory)
Olga Kuchar (Oak Ridge National Laboratory)
Jakob Luettgau (Inria)
Kurt H. Maier (Pacific Northwest National Laboratory)
Bojan Nikolic (Square Kilometer Array)
Marc F. Paterno (Fermi National Accelerator Laboratory)
Line Pouchard (Sandia National Laboratories)
Thomas E. Proffen (Oak Ridge National Laboratory)
Lavanya Ramakrishnan (Lawrence Berkeley National Laboratory)
Tobias Richter (European Spallation Source)
Jana Thayer (SLAC National Accelerator Laboratory)
Thomas D. Uram (Argonne National Laboratory)

Debbie Bard (National Energy Research Scientific Computing Center) leads the Data Science Engagement Group at the National Energy Research Scientific Computing Center (NERSC) at Berkeley National Lab. A native of the UK, her career spans research in particle physics, cosmology and computing on both sides of the Atlantic. She obtained her PhD at Edinburgh University, and worked at Imperial College London and SLAC National Accelerator Laboratory before joining NERSC, where her group supports supercomputing for experimental science. Her research focuses on data-intensive computing and machine learning at scale.
Eli Dart (ESNet) is a network engineer in the ESnet Science Engagement Group, which seeks to use advanced networking to improve scientific productivity and science outcomes for the DOE science facilities, their users, and their collaborators. Eli is a primary advocate for the Science DMZ design pattern and works with facilities, laboratories, universities, science collaborations, and science programs to deploy data-intensive science infrastructure based on the Science DMZ model. Eli is also a key contributor to the ESnet network requirements program, which collects, synthesizes, and aggregates the networking needs of the science programs ESnet serves. Eli has over 20 years of experience in network architecture, design, engineering, performance, and security in scientific and research environments. His primary professional interests are high-performance architectures and effective operational models for networks that support scientific missions, and building collaborations to bring about the effective use of high-performance networks by science projects.
Mallikarjun (Arjun) Shankar (Oak Ridge National Laboratory) is the head of the Advanced Technologies Section in the National Center for Computational Sciences at Oak Ridge National Laboratory. He is a distinguished R&D staff scientist and is the director of ORNL’s Compute and Data Environment for Science (CADES). Dr. Shankar received his Ph.D. in computer science from the University of Illinois, Urbana-Champaign. His research in the national laboratory setting has focused primarily on designing large-scale data analysis and modeling systems and middleware. His research has resulted in over seventy publications. Dr. Shankar has served on the DOE ASCAC subcommittee on Scientific and Technical Information. He is a member of the AAAS, a Senior Member of the ACM, and a Senior Member of the IEEE.
Christine Simpson (Argonne National Laboratory) is an Assistant Computational Scientist in the Data Science group at ALCF, where she focuses on workflows. Her background is in computational astrophysics, and she has worked with hydrodynamical simulations to explore questions in cosmology, galaxy evolution, and interstellar medium studies. Christine received a BA in Mathematics from Wellesley College in 2005 and an MA in Astronomy from Wesleyan University in 2007. She received her PhD in Astronomy from Columbia University in 2014. She then held postdoctoral appointments at the Heidelberg Institute for Theoretical Studies and the University of Chicago before joining ALCF in 2022.
Christine Sweeney (Los Alamos National Laboratory) is a scientist in the Applied Computer Science group at Los Alamos National Laboratory. She currently works on analytics, tools, and computational workflows to support experimental science, especially in the area of X-ray beam line science. She is the LANL lead for the ExaFEL project and the Control pillar lead for the ExaLearn project, both part of the Exascale Computing Project. She also has experience in programming languages, compilers and programming models that support scientists to create their own software at scale. She is a Senior Member of the IEEE.

Justin M. Wozniak (Argonne National Laboratory) leads the development of the Swift/T workflow system. He won an R&D 100 awards in 2018 and 2023. He coupled experimental and computing resources at the Advanced Photon Source and the Cornell High-Energy Synchrotron Source (CHESS) and helped set a data collection record at CHESS. He received a PhD in Computer Science & Engineering from the University of Notre Dame in 2008.
Nicholas Schwarz (Argonne National Laboratory) leads the scientific software and data management effort at the Advanced Photon Source x-ray user facility. His research interests focus on applying state-of-the-art computational techniques to solve scientific domain problems. At the Advanced Photon Source, Nicholas led the development of a facility-wide data management system. He co-authored the common data policy currently being adopted by US-based x-ray synchrotron light source and free electron laser facilities.
Hannah Parraga (Argonne National Laboratory) is a software engineer at Argonne National Laboratory. As a part of the Scientific Software and Data Management Group at the Advanced Photon Source since 2021, her interests include developing robust and reliable software solutions for scientific research such as automated data processing, secure data access, and intuitive user interfaces.