SC26 Workshop

Becky Springmeyer serves as Division Leader for Livermore Computing and the Director of Lawrence Livermore National Laboratory’s Multiprogrammatic & Institutional Computing Program. Becky manages the Livermore Computing workforce and coordinates activities to deliver High Performance Computing systems, software, and services. Becky has more than 35 years of experience as a computer scientist in management and technical roles at LLNL. Her technical interests include next generation HPC environments, scientific data analysis and visualization, and advanced visualization hardware and software. Becky has a B.A. in Computer Science and Mathematics from Ohio Wesleyan University and a Ph.D. and M.S. in Computer Science from the University of California, Davis.

Ana Marija Sokovic, PhD, is Lead Computational Scientist at the University of Illinois Chicago’s Advanced Cyberinfrastructure for Education and Research (ACER), where she supports researchers using AI, GPU computing, and quantum-HPC workflows. She holds an M.Sc. in Aeronautical Engineering from the University of Belgrade and an MBA from UIC, and has spent 14 years in HPC and research computing, beginning in genomics research at the University of Chicago before moving into bioinformatics, GPU-accelerated AI, and quantum-classical convergence.

In 2026, she founded and chairs Chicago Women in HPC (Chicago WHPC), a 501(c)(3) that has grown to over 300 members. Under her leadership, Chicago WHPC launched Quantum × HPC Pathways, a public platform connecting South Side Chicago residents, students, and educators to real career pathways in quantum computing and HPC, tied directly to the Illinois Quantum and Microelectronics Park being built in the neighborhood. The initiative includes a personalized pathway advisor, free tiered learning resources, and a live map of the region’s actual employers, credentials, and institutions, alongside a Winter 2026 pilot cohort and a teacher-training partnership with Chicago Public Schools.

She has moderated industry panels at ISC High Performance 2026 in Hamburg, published at PEARC 2025, and is a 2026 Change Collective Fellow, where she developed Quantum × HPC Pathways as her civic action plan. A lifelong South Side Chicago resident, she builds her work from the belief that major technology investment should translate into real opportunity for the community it’s built in, not just economic activity around it.

Wendy Wilhelm is the Engineering Director of the HPE HPC & AI Deployment Team for the Americas, overseeing the delivery of high-performance computing and AI systems. Her organization demonstrates teamwork in action every day, deploying small to large systems across the Americas region (HW, SW, Networking, Integration & Test). She has been involved in HPC systems and Server technologies since 1991 when she started as an HPC System Engineer. She spent much of her career in validation, integration, and debug work. She is married, has 2 adult children and volunteers with a local high school robotics team (FRC) as shop and leadership mentor. In her spare time, she loves to read a good book or to play card/board games with family and friends.

Rebecca Hartman-Baker leads the User Engagement Group at the National Energy Research Scientific Computing Center at Lawrence Berkeley National Laboratory. She is a computational scientist with expertise in the development of scalable parallel algorithms. Her career has taken her to Oak Ridge National Laboratory, where she worked on the R&D100-award winning team developing MADNESS and as a scientific computing liaison in the Oak Ridge leadership computing facility; the Pawsey Supercomputing Centre in Australia, where she coached two teams to the Student Cluster Competition at SC and led the decision-making process for determining the architecture of Australia’s first petascale supercomputer; and NERSC, where she’s responsible for NERSC’s engagement with the user community to increase user productivity via strategic communication, advocacy, support, and training. During her tenure, the User Engagement Group has transformed the user training experience, developed the user community of practice, and introduced the first Code of Conduct at an HPC center. Rebecca earned a PhD in Computer Science, with a certificate in Computational Science and Engineering, from the University of Illinois at Urbana-Champaign.

Fernanda Foertter is Executive Director of High Performance Computing and Data Center at the University of Alabama, where she is leading the development of a new research computing data center from the ground up.

Her career spans roles across the advanced computing ecosystem — from the Oak Ridge Leadership Computing Facility and NVIDIA to BioTeam, Genus Plc., and NextSilicon — with work touching leadership-class supercomputers, GPU-accelerated computing, life sciences infrastructure, data discovery, and emerging processor architectures.

She is known for building bridges between technology and the people who use it, translating complex systems into practical strategy, governance, and community.

Ashwini Naik is a Computational Scientist and Visualization Laboratory Operations Lead at the Research Computing Center (RCC) at the University of Chicago, where she integrates scientific visualization, artificial intelligence, and high performance computing into collaborative research workflows spanning the physical sciences, biological sciences, social sciences, and humanities.

She holds a Ph.D. and M.S. in Computer Science from the University of Illinois Chicago, where she conducted research at the Electronic Visualization Laboratory (EVL) in visual data science, immersive analytics, and human-computer interaction. Her doctoral work included designing and executing user studies, mentoring more than fifty students in AR/VR development and visualization, and publishing in venues including IEEE ISMAR and the Journal of Neuroscience Research.

At the RCC, Ashwini leads technical collaborations spanning AI-assisted biomedical image analysis using 3D Slicer and MONAI on NVIDIA A100 and V100 GPUs, immersive cultural heritage reconstruction deployed across display walls and Meta Quest Pro VR headsets from a single Unity pipeline, and an active digital twin initiative using XGRIDS PortalCam for spatial capture. She is currently developing a custom tool for stitching multi-capture Gaussian splat scans into unified, navigable environments. She has delivered workshops reaching more than 75 researchers across five sessions on topics including foundation models for biomedical imaging, AI-enhanced image analysis, and interactive data visualization.

Prior to her doctoral studies, she held engineering roles at Scientific Games, Olenick & Associates, and Birlasoft across the United States and India.

Olivia Maynard is a second year PhD student in Computer Science and Engineering at The Ohio State University, supervised by Dr. Suren Byna. Her work involves profiling I/O and data movement for scientific computing, particularly within scientific deep learning; she additionally has interests in architecture and security within scientific computing. Outside of her research, she is a passionate educator in engineering education and computer science, currently serving as the lead Graduate Teaching Associate for Ohio State’s first-year Engineering Foundations sequence in the Honors track. She graduated with her B.S. in Computer Science and Engineering from Ohio State in 2025.

Dr. Erda Qorri is a postdoctoral researcher working at the intersection of academic research and the biotechnology industry. She obtained her doctoral degree from the University of Szeged in Hungary, where her research focused on improving the classification and interpretation of missense variants. As part of her doctoral work, she developed novel machine-learning features derived from protein structures generated using AlphaFold2, with the aim of supporting more accurate assessment of the functional consequences of genetic variation.

Throughout her PhD, Dr Qorri gained extensive experience working in high-performance computing environments, which sparked a broader interest in supercomputing and its applications in computational biology. Her work involved the development and execution of computationally intensive bioinformatics and structural-modelling workflows, as well as the analysis of large and complex biological datasets.

In her current postdoctoral research, she is investigating the use of AlphaFold2 and related computational approaches for modelling neoantigen-peptide-major histocompatibility complex–T-cell receptor interactions. This research is conducted within an HPC environment and aims to contribute to a better understanding of molecular recognition in the context of cancer immunology and personalized immunotherapy. Her broader research interests include structural bioinformatics, transcriptomics, machine learning, cancer genomics, and the application of high-performance computing to biomedical research.

Eva Fernandez Amez is the Community Manager for Digital Research Infrastructure at Durham University. Her work focuses on developing training opportunities, fostering communities, and supporting the skills development of research technical professionals across the research computing landscape, with a particular focus on advanced and accelerated computing. She works closely with researchers, technical specialists, and community stakeholders to identify skills needs and create resources that support collaboration, knowledge exchange, and professional development. Alongside her community-building and training activities, she contributes to outreach and communications through website development, graphic design, video production, and science communication. She holds a degree in Physics and combines her scientific background with expertise in digital education and communication to make complex technical topics more accessible to diverse audiences.

Yuqian Huo is a PhD student in Computer Science at Rice University, advised by Dr. Tirthak Patel in the Positive Technology Lab. Her research focuses on making quantum computers more reliable and efficient through better system design, compilers, and performance optimization. She works with both superconducting and neutral atom quantum computers. She is also interested in quantum machine learning, as well as security and privacy in quantum computing.

Her research has been published at SIGMETRICS, ICCAD, AISTATS, and QCE. She also shares open-source code and tools with her papers so that other researchers can use and build on her work. She received the SandboxAQ Global Travel Scholarship in 2025 and was selected as a DAC Young Fellow in 2024.

Daria Godorozha is an MSc by Research student at the London School of Economics, where she founded the LSE Beavers HPC team. LSE has no STEM departments apart from statistics and mathematics. The team includes two women and members from a range of ethnic backgrounds, and finished 1st in one of two challenges and 3rd overall at the CIUK Cluster Challenge. It was also one of two student teams accepted onto an AMD/DIRAC UKRI funded residential research development programme, and received GPU and RSE support. She has spoken at Imperial College London HPC SIG on the topic of creating a successful HPC educational initiative. Previously, she completed an MSc in AI and Data Science with Distinction.

This year she has led research teams working on NLP and LLMs at the University of Oxford and ETH Zurich. She has also worked in medicine, health and life sciences, leading a research project at the Swansea University Faculty of Medicine, Health and Life Sciences, where she engaged with the Welsh Parliament and is contributing to a health consultation. She presented “Compute Optimal Diffusion on Learned Conformer Manifolds” at the AIBIO BBSRC funded network conference and came 2nd at the Edinburgh BIOAI conference.

From September she will be a teaching assistant in data at Imperial College London further to her work on the QMUL Designing an Inclusive Curriculum project. Her interest in HPC includes its role in regional economic development, particularly in Wales and in its ability to empower diverse regional talent.

Elizabeth Koleva is an HPC software engineer at the Discoverer petascale supercomputer in Sofia, Bulgaria, and works on agentic AI workflow management and deployment at BRAIN++, the Bulgarian AI Factory under EuroHPC, where she is sole developer of the organisation’s internal platform.

She recently completed an MSc in Innovation and Research in Informatics, specialising in High Performance Computing, at the Facultat d’Informàtica de Barcelona, Universitat Politècnica de Catalunya – a double degree with Sofia University St. Kliment Ohridski through EUMaster4HPC. Her thesis, “Predicting and Explaining Alzheimer Disease Using UK Biobank,” built a leakage-controlled, cost-sensitive machine learning pipeline that treats auditability as a primary result, supervised by Prof. Jordi Torres Viñals and Prof. Petia Radeva.

Her earlier work spans knowledge graphs, disinformation-prevention research, and fine-tuning NLP models for Bulgarian at the European Software Institute. She won the EuroHPC Summit Student Challenge 2025 in Kraków, placed second in the EUMaster4HPC Student Challenge 2025–2026 for benchmarking AI Factories on MeluXina, and received the Most Innovative Business Solution award at the AmCham Bulgaria Hackathon 2025.

Elizabeth serves as an HPC Student Ambassador Mentor for the EuroHPC Joint Undertaking, having been an ambassador herself, and represents EUMaster4HPC at information sessions. She previously chaired the Supervisory Board of the Sofia University Students Council and sat on the Transform4Europe Student Council. She began her career teaching C++ to secondary school students and has taught computer graphics at Sofia University. She is preparing to begin a PhD at the intersection of HPC, explainable AI, and health research.

Emma Urquhart and is a 3rd year PhD student in Computer Science at the University of Cambridge. Her research focuses on accelerating unstructured scientific methods on wafer-scale hardware. Her research interests include computer architecture, low-level software performance optimizations and scientific computing. Emma completed her undergraduate studies at the University of Galway, followed by the MPhil in Advanced Computer Science at the University of Cambridge, ranking first in her cohort.

Serena Ma is a PhD researcher at the University of Leeds working at the intersection of high-performance computing, compiler optimization, and quantum computing systems. Her research journey has spanned CPU-side compiler optimization, large-scale GPU and AI infrastructure, and emerging quantum computing. Her broader research goal is to develop software and compiler techniques that maximize the useful performance of emerging computing hardware. Her interests include HPC, compiler optimization, heterogeneous systems, reinforcement learning for systems, and quantum software systems.

Suné Toerien is a South African engineering student and high-performance computing (HPC) enthusiast based in Johannesburg. She holds a Bachelor of Engineering Science in Biomedical Engineering (BEngSc) from the University of the Witwatersrand and is currently a fourth-year Information Engineering student. Her interdisciplinary interests include computational methods, HPC performance, and applications in biomedical contexts. Suné’s earlier work includes a paper on skeletal muscle oxygen tension in COVID-19, exploring its potential as a disease severity marker. She also contributed to a collaborative HPC study on the productivity-to-energy trade-off via DVFS, core scaling, and C-state control. The work placed second in the ACM Student Research Competition at PASC26 in Switzerland and won Best Student Short Paper at PEARC26 in the USA.

Preetika Kaur is a final-year Ph.D. candidate in Civil Engineering (Hydrologic Sciences) at the University of Wyoming, where she works on snow remote sensing. Her dissertation asks where satellite radar can be trusted to measure mountain snowpack. She built the first feasibility map of L-band InSAR snow water equivalent retrieval for the western United States, published in Geophysical Research Letters, and runs the Fortran-based SnowModel system on Wyoming’s Medicine Bow HPC cluster to simulate snowpack liquid water content across large mountain domains. She is now quantifying snow density variability at Arctic tundra and boreal forest sites in Alaska. She came to computational hydrology sideways. After a B.S. in agricultural engineering at Punjab Agricultural University in India, she completed an M.S. at Auburn University using X-ray computed tomography to quantify how tillage and cover crops reshape soil pore networks, producing three first-author publications and Auburn’s outstanding master’s student and thesis awards. Her HPC and cloud computing skills were assembled through hackweeks and summer schools rather than coursework, an experience that shapes how she teaches. She helped organize a University of Washington eScience Institute hackweek and taught a hands-on Google Earth Engine workshop at Wyoming. She is Vice President of the Society of Women Engineers at Wyoming and secretary of both its ASPRS student chapter and Wyomingites in Math, Science and Engineering. She won second prize in Wyoming’s Three-Minute Thesis competition and a Boyd Scott graduate paper award from ASABE, and completes her Ph.D. in May 2027.

Nada Abdalgawad is a PhD candidate in the Computer Science and Engineering department at University of Michigan. Her current area of research is computer architecture with focus on memory architectures. She graduated with a Bachelor of Science in Computer Engineering from the American University of Sharjah, Sharjah, United Arab Emirates in 2020.

Sonal Jha is a Ph.D. student in Computer Engineering at Virginia Tech, where her research focuses on early and explainable health risk assessment through Graph AI and high performance computing (HPC). To improve the accuracy of risk assessment, Sonal developed graph-based methods that model clinically useful information from irregular and sparse clinical data. Her work has progressed from a graph-based approach using a single clinical variable to Graph AI methods for multivariable and multimodal clinical data. To improve the scalability, she studied bottlenecks in Graph AI training and developed distributed approaches for efficient multi-GPU training. On the professional front, Sonal serves as a Graduate Research Assistant (GRA) with Virginia Tech’s Advanced Research Computing (ARC@VT), where she provides HPC and AI consulting to faculty, postdocs, and students across 26 departments. Her work spans migrating, profiling, optimizing, and scaling scientific workloads; debugging parallel and distributed jobs; managing large-scale data; installing scientific software; and helping researchers integrate AI workflows on ARC’s HPC systems. She has interned at Hewlett Packard Enterprise (HPE) as an HPC and AI Visiting Scholar and at Los Alamos National Laboratory as a Data Science at Scale Intern.