SC 2020 Workshop

Dr. Francine Berman is the Edward P. Hamilton Distinguished Professor of Computer Science at Rensselaer Polytechnic Institute (RPI). She is a Faculty Associate at the Berkman Klein Center for Internet and Society at Harvard University and was selected as the 2019-2020 Katherine Hampson Bessell Fellow at the Radcliffe Institute for Advanced Study at Harvard University. Berman was the inaugural recipient of the ACM/IEEE-CS Ken Kennedy Award for “influential leadership in the design, development, and deployment of national-scale cyberinfrastructure.” In 2015, Berman was nominated by President Obama and confirmed by the U.S. Senate to become a member of the National Council on the Humanities. In 2019, she was elected to membership in the American Academy of Arts and Sciences.

In 2020, Berman was elected as a Fellow of the National Academy of Public Administration. In early 2020, she was also announced as the recipient of the Paul Evan Peters Award which “recognizes notable, lasting achievements in the creation and innovative use of network-based information resources and services that advance scholarship and intellectual productivity”. The award is jointly sponsored by the Coalition for Networked Infrastructure, the Association of Research Libraries, and Educause.

Berman is a data scientist whose current work focuses on the social and environmental impacts of information technology, and in particular of the Internet of Things (IoT) — a deeply interconnected ecosystem of billions of devices and systems that are transforming commerce, science and society. Berman is exploring the overarching ecosystem needed to guide the development of information technologies that maximize benefits, minimize risks, and promote individual protections, the public interest, and planetary responsibility. Recent other work has focused on data stewardship and preservation, particularly with respect to the policy, practice and infrastructure needed to ensure the integrity, longevity and usefulness of the data on which modern research relies. Berman’s interests are in the broad area of Public Interest Technology, and focus on strategies that promote technology as a tool to advance humanity.

Dr. Berman is a founder of the Research Data Alliance (RDA), a community-driven international organization whose mission is to build global infrastructure that enables data sharing and data-driven research. Since its launch in 2012, RDA has attracted over 9600 members from over 130 countries and built data infrastructure in use by groups and projects all over the world. Berman served as co-Chair of RDA’s leadership Council and as Chair of RDA’s U.S. region for the organization’s first 6 years.

Previously to co-founding RDA, Berman served as Vice President for Research at RPI from 2009 to 2012. From 2001-2009, Berman served as Director of the San Diego Supercomputer Center (SDSC). In this role, she led a staff of 250+ interdisciplinary scientists, engineers, and technologists. She also directed the National Partnership for Advanced Computational Infrastructure (NPACI), a consortium of 41 research groups, institutions, and university partners with the mission of developing national infrastructure to support data-intensive and computationally-intensive applications. Concurrently, Berman was Professor in the U.C. San Diego Department of Computer Science and Engineering and first holder of the High Performance Computing Endowed Chair in the Jacobs School of Engineering.

Berman is a Fellow of the Association of Computing Machinery (ACM), a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), and a Fellow of the American Association for the Advancement of Science (AAAS). She currently serves as a member of the Board of Trustees of the Alfred P. Sloan Foundation and the National Council on the Humanities. Berman previously served as Chair of the Board of Trustees of the Anita Borg Institute for Women and Technology, as co-Chair of the National Academies Board on Research Data and Information, as co-Chair of the Blue Ribbon Task Force on Sustainable Digital Preservation and Access, as co-Chair of the National Science Foundation Advisory Committee for the Computer and Information Science and Engineering Directorate, and as Chair of the Information, Computing and Communication Section (Section T) of the American Association for the Advancement of Science, among other positions. For her accomplishments, leadership, and vision, Berman has been recognized by the Library of Congress as a “Digital Preservation Pioneer”, as one of the top women in technology by BusinessWeek and Newsweek, and as one of the top technologists by IEEE Spectrum.

Robert Ross is a Senior Computer Scientist at Argonne National Laboratory, a Senior Fellow in the Northwestern-Argonne Institute for Science and Engineering, and the Director of the DOE SciDAC RAPIDS Institute for Computer Science and Data. Rob’s research interests are in system software for high performance computing systems, in particular distributed storage systems and libraries for I/O and message passing. Rob received his Ph.D. in Computer Engineering from Clemson University in 2000. Rob was a recipient of the 2004 Presidential Early Career Award for Scientists and Engineers and has been part of two teams awarded the R&D 100 Award.

Debbie Bard is acting group lead for 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 Ph.D. at Edinburgh University, and worked at Imperial College London and SLAC National Accelerator Laboratory before joining the Data and Analytics group at NERSC, where she focuses on data-intensive computing and research.

I am a Principal Member of Technical Staff in the Scalable System Software Group at Sandia National Laboratories. I work on issues related to scientific data for high performance computing applications. Most of my work has been related to IO and data movement while more recent work has been investigating infrastructure to support workflows, storage systems, energy use of data, and resilience techniques to enable reducing data movement.

Dr. Kaoutar El Maghraoui is a principal research scientist at the IBM Research AI organization where she is focusing on innovations at the intersection of systems and artificial intelligence. She leads the End-Use experimental AI testbed of the IBM Research AI Hardware Center, a global research hub focusing on enabling next-generation chips and systems for AI workloads. She is currently focusing on the operationalization aspects of AI systems in hybrid cloud environments. Kaoutar has extensive experience and deep expertise in HPC, systems software, Cloud computing and machine learning.

Kaoutar co-led IBM’s Global Technology Outlook in 2017 where she worked on creating IBM’s vision for the future of IT across global labs and business units focusing on IBM’s AI leadership. Prior to that, she was a member of the scalable systems groups, where she has conducted research on several aspects of the AIX operating system such as performance, multi-thread and multi-core scheduling, Flash SSD storage, OS crash diagnosis and recovery, interactions with systems architecture, and applying analytics techniques for OS problem diagnosis. She led a research project to apply IBM Watson’s cognitive technology to systems problem diagnosis and resolution for the POWER platform. Her primary research interests are cloud computing, operating systems, high-performance computing, distributed systems, and analytics.

Kaoutar obtained a PhD degree in Computer Science in 2007 from Rensselaer Polytechnic Institute (RPI), Troy, New York and a Masters degree in Computer Networks in 2001 from Al Akhawayn University, Morocco. She was a lecturer of Computer Science in the School of Science and Engineering at Al Akhawayn University in 2001 and 2002. Kaoutar has received several fellowships and awards including the American Association of University Women fellowship, the Robert McNaughton Award for best thesis in computer science at RPI, IBM’s Eminence and Excellence award for leadership in increasing Women’s presence in science and technology, and IBM’s Tier II award for contributions to the foundational POWER software technologies and promoting these systems in Africa. She is a member of ACM,  IEEE Computer Society, and the Society of Women Engineers. Kaoutar has co-authored several conference and journal publications in the areas of systems research, distributed systems and high performance computing. She has served in many technical conferences as co-chair, member of the program committee, and reviewer.

Dr. Kaoutar El-Maghraoui is co-chair of the Arab Women in Computing (ArabWIC) Anita Borg Institute Systers’ community and co-chair of IBM Research Watson Women’s Network. She is very passionate about mentoring, promoting, and increasing the participation of women in STEM and computing fields. She has been an active member of the leadership team of the Grace Hopper Conference (GHC), the world’s largest conference of women in computing. Under her leadership, she helped put together the most International poster/career track committees the conference has ever had. She has served as the program Co-Chair of GHC in 2015 and is currently serving as the general Co-Chair of GHC in 2016.

Lorna Rivera, M.S. serves as a Research Scientist in Program Evaluation at the Georgia Tech Center for Education Integrating Science, Mathematics and Computing (CEISMC). Her work focuses on the intersection of scientific content, pedagogy, and equity with the goal of being both methodologically innovative and socially responsible. Rivera has conducted evaluations primarily funded by the National Science Foundation’s Division of Advanced Cyberinfrastructure. This has led her to work with over 18 universities as well as multiple international high performance computing centers and organizations such as Compute Canada, EPCC, PRACE, RIKEN, and XSEDE. Rivera received both her Bachelor of Science in Health Education and her Master of Science in Health Education and Behavior from the University of Florida. Prior to joining Georgia Tech, Rivera worked with various institutions, including the March of Dimes, Shands HealthCare, University of Illinois at Urbana-Champaign, and the University of Florida College of Medicine. Her research interests include the evaluation of innovative programs and their sustainability.

Lisa Claus is a Postdoctoral Research Scientist in the Scalable Solvers Group of the Computational Research Division at Lawrence Berkeley National Laboratory, in Berkeley, California. She is a mathematician with experiences in algorithm development, linear algebra and high performance computing. Lisa recently started working on the high-performance scalable software library STRUMPACK which offers a direct solver and a preconditioner for large sparse linear systems. In 2019, Lisa Claus completed her PhD in Applied Mathematics titled “Multigrid smoothers for saddle point systems” at the University of Wuppertal in Germany. During her PhD studies she developed new algorithms in the field of multigrid methods to solve partial differential equations that model fluid motion. Lisa extended her research by joining Lawrence Livermore National Laboratory for two Computer Science internships. There, she advanced algorithms to automatically construct algebraic multigrid methods with a focus on Maxwell’s equation which model electrical and magnetic fields. In 2018, she joined the Supercomputing conference as a Student Volunteer. Lisa serves in several volunteer positions including her Social Committee Leader role at the Berkeley Laboratory PostDoc Association. She is an active member in the Women in HPC organization and the WHPC mentorship program.

I have a degree in Physics by National Autonomous University of Mexico (UNAM) in 2006. My main interest is the modeling of the Earth’s complex system by using cellular automaton models. My bachelor project was the study of the fractal growth in sub-aquatic caves by means of a modified Diffusion Limited Aggregation model. In 2007 I started my post-graduate studies in the Polytechnic University of Catalonia on the program research of Computational and Applied Physics (https://doctorat.upc.edu/en/programmes/computational-applied-physics). I focused my specialty in Computational Physics in the Geophysics group studying the earthquakes as statistical phenomena. To study this phenomenon I applied fractal techniques and statistical analysis. We applied and studied a numerical model capable of reproducing real features. In 2014 I done a postdoc in the UNAM. There I had the opportunity to continue developing the earthquakes model adding space and magnitude characteristics. In 2016 I was awarded a postdoctoral fellowship in the Barcelona Supercomputing Center (BSC) thanks to the agreement CONACYT- BSC. I worked from February to December 2016 in the atmospheric composition group to update the HERMES database. From January 2017 I’m working in Computer Applications in Science & Engineering department at BSC. My research focuses on developing earthquake models based on the Fiber Bundle model. Moreover, I’m working in a European Project where I’m developing an Urgent Seismic Computing Service using HPC.

Wenqian is a fourth year Ph.D. student in Computer Science at the University of California, Merced. Her research focus on High-performance computing (large-scale parallel/distributed systems).

She is working on (i) Scientific machine learning: accelerating HPC applications using machine learning-based approximation, (ii) Automatic Machine Learning: automatically machine learning model construction for HPC ap-plications, and (iii) Automatic Performance Tuning: performance optimization and quality controlon accelerating HPC applications using machine learning-based approximation. In this work, she uses innovative methods to make AI models interpretable and robust for accelerating a scientific application, power grid simulation. She proposes a iteractive learning model as information-sharing to predict multiple tasks and incorporate complex constraints imposed by physical principles to improve the interpretability and robustness of ML model. The results show that the simulation time is reduced by an average of 2.60×(up to 3.28×) without losing the optimality of the solution.

McKenzie A. Hughes is affiliated with the Department of Integrative Biology at Oregon State University. Currently, she is studying for the MCAT to apply to medical schools next cycle with the goal of incorporating data-driven solutions into the field of neurology and healthcare. Traditionally a wet-lab disease oriented scientist, she has branched out into understanding the holistic aspects of healthcare and population health through data and computation, an exciting and serendipitous adventure. Part of this push has come from a recent project applying high performance computing to healthcare with XSEDE through the Advanced Computing for Social Change Institute 2020 program.

Beatriz Martínez Montesinos was born in Barcelona, Spain. She worked as a computer programmer at the Sysdata/Transiciel/Sogeti Information Technology company for several years. She gained a Bachelor Degree in Mathematics in 2010 and a Master of Science Degree in Advanced and Professional Mathematics in 2012, both from the University of Barcelona.

To pursue her interest in the field of Earth Sciences, she joined the Marie Curie Initial Training Network Project NEMOH (Numerical, Experimental and stochastic Modelling of vOlcanic processes and Hazard) as a research assistant in the School of Geological Sciences in University College Dublin, Ireland, in September 2013. In the Seismology Laboratory she developed numerical simulations for wave propagation through complex mediums and calculated sensitivity kernels to examine the influence of near-surface low-velocity volcanic structure on the recorded seismic data.

After two years, under the Horizon 2020-Marie Skłodowska-Curie Project CREEP (Complex RhEologies in Earth dynamics and industrial Processes) she moved to Germany to develop 3D software to simulate fluid injection and crack propagation in poro-visco-elasto-plastic rheologies in the Johannes Gutenberg University of Mainz. There she carried out her PhD thesis “Numerical Approaches to Model and Monitor Geomechanical Reservoir Integrity” obtaining a doctorate in June 2019.

Within the framework of ChEESE (Center of Excellence for Exascale in Solid Earth) she started her current job in the Volcanoes Department of the National Institute of Geophysics and Volcanology, Italy, in October 2019. She is collaborating on the development of a Pilot Demonstrator to show the usefulness of HPC in Probabilistic Volcanic Hazard Assessment at two target volcanoes, Campi Flegrei in Southern Italy and Jan Mayen, Norway in the North Atlantic.

Dr. Sarah M. Neuwirth is a postdoctoral research scientist and lecturer in the Computer Architecture Group at Heidelberg University, Germany. Her research and development interests are focused on high-performance computing and networking, parallel I/O and file systems, communication protocols, system and storage area networks, hardware disaggregation and virtualization, and modular supercomputing. In 2018, Dr. Neuwirth was awarded her Ph.D. in Computer Science from Heidelberg University, Germany. She was honored with the “ZONTA Science Award 2019” for her outstanding dissertation. She also holds a M.Sc. in Computer Science and Mathematics from University of Mannheim, Germany. Dr. Neuwirth has worked on numerous research collaborations including working with: the Jülich Supercomputing Centre (DEEP Project Series), the Oak Ridge National Laboratory, and the Virginia Polytechnic Institute and State University.

Emily Herron is a third year PhD Student in the Bredesen Center Data Science and Engineering Program at the University of Tennessee. This is a joint program between the University of Tennessee and Oak Ridge National Laboratory. She holds a B.S. in Computational Science from Mercer University. Her research focuses on deep learning and high performance computing, with a focus on evolutionary algorithms and neural architecture search.

Ruiqin Tian is a PhD candidate in College of William and Mary advised by Dr. Bin Ren. Her research interests lie in compiler optimzation, runtime optimzations. Building compilers to automatically analyse and optimize the code is her biggest dream. She is now a PhD research intern in Pacific Northwest National Lab mentored by Dr. Gokcen Kestor.

Jo Adegbola has had a varied career in the tech industry, one that has taken a winding path from Management Consultant, to Game Developer, to Sr Manager of High Performance Computing for AWS. Her passion for the last 20+ years has been in building highly performant teams that enjoy working together to deliver for the customer, unlocking entertainment experiences for millions or enabling a single research scientist to make the next big discovery. Jo has been with AWS for 2 ½ years, where she heads the development group for HPC, a group responsible for the development of AWS Batch, ParallelCluster, and EFA.

I am an Assistant Professor at Texas State University (TXST). Before joining TXST, I was an assistant professor at Western Washington University (2017-2019), and a postdoctoral scholar at Lawrence Livermore National Laboratory (2013-2017). Broadly, I am interested in leveraging data science methodologies to address challenging questions that pertain to extreme-scale computing environments. My research spans fault-tolerance, performance modeling, prediction, and reproducibility for large-scale applications. I earned my Ph.D. in Electrical and Computer Engineering from Purdue University, and B.Sc. in Computer Science and Engineering from Bangladesh University of Engineering and Technology. My work enables large-scale simulations, often used in different fields such as bioinformatics, earthquake engineering, material science, to leverage the incredible computational capabilities of modern clusters.

Dr Mozhgan Kabiri Chimeh is a GPU developer advocate at NVIDIA helping to bring GPU and HPC to growing user community in Europe and around the world. She is a community builder with a passion for open source software and is actively involved in the HPC and RSE communities. As a Software Sustainability Institute Fellow, and Research Software Engineer (RSE) advocate, she is actively promoting reproducible and sustainable software, use of HPC and particularly GPUs through training, seminars, research software consultancy, and outreach.

Prior to joining Nvidia, Mozhgan was a Research Software Engineer in Massive Scale Complex Systems Simulation with Accelerated Computing at the University of Sheffield, UK. She worked in the area of complex system modelling using emerging high-performance parallel architectures.

She is actively involved in outreach programs to encourage and empower minorities’ involvement at all levels within the HPC sector and is a long-standing Women in HPC volunteer, including leading WHPC’s workshop last year and this year at International Supercomputing Conference. Mozhgan has chaired several technical and scientific conferences and served as a committee member of high profile HPC conferences. She holds a Ph.D. in computer science and a master’s degree in Information Technology from the University of Glasgow, UK.