WHPC Rising Stars at SC26

WHPC formally recognizes the exceptional early career researchers as WHPC Rising Stars. These are a select group of individuals from underrepresented groups, who are recognized for their research contributions and individual excellence. Applications for the Rising Star program undergo a competitive peer-review process, typically with an acceptance rate of less than 50%.

As part of the both the ISC and SC programs, WHPC Rising Stars receive mentorship support, a lightning talk opportunity, eligibility for the best WHPC submission award, and eligibility for travel support. These up-and-coming HPC researchers are the future of our workforce, and bringing them to large HPC conferences connects them to the community.

For SC26, we are grateful to our sponsors to be able to offer some level of funding to every one of the SC26 Rising Star cohort, with 4 of our Rising Stars receiving full travel support.

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.

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.