Biography

Urjoshi Sinha is a Graduate Research Assistant in Computer Science at Iowa State University and a former Computer Systems Engineer at Lawrence Berkeley National Laboratory. With over ten years of experience spanning academia, national labs, and industry, her work focuses on scientific software testing, HPC workflow optimization, and sustainable data management. At Berkeley Lab, she maintained and optimized databases supporting 400+ scientific projects across multiple domains, and contributed to the enhancement of HPC workflows. Her academic research explores metamorphic testing and genetic algorithms to improve the reliability of scientific software, such as biological simulation tools and drone autopilot software. Urjoshi is an IEEE Senior Member and an active ACM SIGHPC program coordinator, where she supports inclusive research initiatives and mentorship for underrepresented groups in computing. Her work has earned multiple awards, including the Best Student Paper Award (IEEE/ACM SEAMS 2023) and recognition for her outreach in global translation and accessibility through TED Translate. She is passionate about building bridges between open science, and equitable access to research computing which are principles that align deeply with the mission of the Sustainable Research Pathways program.

Academic Status

PhD Student - 5th

Research Area/Department

Computer Science

Major/Specialty

Computer Science

Degrees Earned or in Progress

PhD (in progress) MS Computer Science (Completed) Bachelor of Technology in Computer Science and Engineering (Completed)

Academic Preparation

Principles of Artificial Intelligence, Design and Analysis of Algorithms, Advanced Topics in Software Engineering: Foundations, Theory of Computation, Deep Learning: Theory and Practice, Gerontechnology in Smart Home Environments, Software Requirements Engineering, Information Warfare, Machine Learning, Advanced Topics in Database Systems. I have also presented research forums and have had previous internship experience in both national labs and industry.

Research/Publications

1. "Using a genetic algorithm to optimize configurations in a data-driven application, International Symposium on Search Based Software Engineering, 2. Towards Real-Time Safety Analysis of Small Unmanned Aerial Systems in the National Airspace", AIAA AVIATION 2022 Forum 3. "Self-Adaptive Mechanisms for Misconfigurations in Small Uncrewed Aerial Systems", 2023 IEEE/ACM 18th Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS). 4. "CONFIGURABILITY CHALLENGES OF HEALTHCARE RECOMMENDER SYSTEMS", INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY AND MANAGEMENT INFORMATION SYSTEM 5. An Evaluation of Self-Adaptive Mechanisms for Misconfigurations in Small Uncrewed Aerial Systems, ACM Transactions on Autonomous and Adaptive Systems 6. "Event-Based Data Pipelines in Recommender Systems: The Data Engineering Perspective", International Conference for Emerging Technologies in Computing

Research/Academic Interests

My research interests lie at the intersection of software testing and optimization, high-performance computing (HPC), and sustainable data management. I am deeply interested in applying AI and different testing techniques (metamorphic testing, differential testing) to enhance the reliability and scalability of scientific software, particularly within computational biology and HPC workflows. My work at Lawrence Berkeley National Lab and Iowa State University has focused on configurability and optimization of data-driven scientific software, enabling more reliable and reproducible research across domains such as biosciences, physics, and material science. I am deeply motivated by the challenge of improving AI model reliability, interpretability, and resource efficiency, especially within NSF’s vision for the National Artificial Intelligence Research Resource (NAIRR). Ultimately, I aim to advance research that bridges software engineering, sustainability, and scientific discovery, creating scalable methods that support equitable access to computational science.

Computational and Data Science Areas

Biochemistry and Molecular Biology; Computer Science; Informatics, Analytics and Information Science

Motivation

I am eager to participate in the Sustainable Research Pathways program because it uniquely integrates research collaboration, mentorship, and inclusion which are values that have shaped my own career. Over the past few years, working in diverse, interdisciplinary teams across DOE laboratories and universities, I have seen firsthand how access to mentorship and resources transforms early-stage research careers. SRP’s mission to build sustainable connections deeply resonates with my belief that innovation thrives in inclusive ecosystems. Through this program, I hope to collaborate with NAIRR researchers to apply my existing skillset related to testing and optimization of data-driven scientific software, in particular leveraging optimization algorithms and LLM-based testing frameworks, and explore how I can contribute to the sustainable AI infrastructure. I also look forward to engaging with peers and mentors in the SRP community to discuss long-term pathways for diversity, accessibility, and responsible AI in HPC research. Beyond technical growth, I seek to contribute as both a mentee and mentor, sharing my experiences in database optimization, open science, and community leadership to strengthen the program’s collaborative spirit.

Lightning Talk Title

Software testing of highly configurable data-driven scientific tools

Keywords (Maximum 20 words)

testing, data-driven, optimization, LLM, scientific tool, software configurability, metamorphic testing, software reliability