Biography

I am a Ph.D. candidate in the Computer Science department at the University of Iowa. Currently, I focus on energy-efficient machine learning and explainable AI. My research centers on developing sustainable neural networks by pruning overparameterized models to reduce their energy use without sacrificing accuracy or interpretability. I’ve had the opportunity to conduct research at national labs like the National Renewable Energy Laboratory (NREL) and Los Alamos National Laboratory (LANL), as well as at universities such as Cornell and Princeton. My projects range from sustainable AI and genomics to high-performance computing and scientific simulation. I’m passionate about contributing to open-source software and have published my work in venues such as PEARC, Bioinformatics, and arXiv. Beyond research, I’m dedicated to creating diverse, inclusive, and collaborative environments. I actively engage with communities like the US Research Software Engineer organization to help build stronger connections across academia and national labs. Over the past three years, I have volunteered in various capacities, including serving on the Education and Training affinity group and co-running the student mentorship program for conferences. Ultimately, I aim to bridge cutting-edge AI research with real-world impact that promotes safety, equity, and sustainability.

Academic Status

PhD Student - 5th

Research Area/Department

Computer Science; Data Science; Machine Learning/AI; Mathematics

Major/Specialty

Computer Science/Machine Learning/Energy Efficient Machine Learning + Explainable Machine Learning

Degrees Earned or in Progress

Associate in CS Bachelors in CS Masters in CS Ph.D. in CS (Currently Enrolled)

Academic Preparation

CS:4700 – High Performance and Parallel Computing Foundational course in parallel programming, distributed computing, and performance optimization. CS:4720 – Optimization Techniques Explored algorithms and numerical methods for solving optimization problems relevant to AI and modeling. CS:5800 – Fundamentals of Software Engineering Covered software architecture, design patterns, and development methodologies. CS:5810 – Formal Methods in Software Engineering Studied logic-based techniques for software verification and system correctness. CS:5350 – Design and Analysis of Algorithms Deep dive into algorithmic complexity, design paradigms, and theoretical foundations. STAT:7500 – Statistical Machine Learning Advanced machine learning course focusing on statistical modeling, inference, and real-world data applications. CEE:4511 – Scientific Computing & Machine Learning Cross-disciplinary course on applying ML in scientific domains, including simulation and modeling. CS:5630 – Cloud Computing Technology Introduced modern cloud platforms, containerization (Docker, Kubernetes), and scalable system design.

Research/Publications

Conference - PEARC24 (2024) – “Adversarial Robustness and Explainability of Machine Learning Models” Journal - Bioinformatics (2025) – “Adversarial Attack of Sequence-Free Enhancer Prediction Identifies Chromatin Architecture” arXiv (2024) – “A Beginner’s Guide to Power and Energy Measurement and Estimation for Computing and Machine Learning” Technical Report (2024) – “Measuring the Energy Consumption and Efficiency of Deep Neural Networks” Technical Report (2022) – “The BUTTER Zone: An Empirical Study of Training Dynamics in Fully Connected Neural Networks”

Research/Academic Interests

My doctoral research is focused on the deployment of machine learning models. Specifically, I aim to build models and tools that maintain high accuracy while being computationally sustainable. A large part of my work investigates the overparameterization of modern neural networks and the unnecessary energy consumption that results from it. By identifying and processing redundant components within these models, we can significantly reduce their size, improve efficiency, and lower their overall resource utilization. This research is driven by a broader goal: to ensure that the growth of AI is aligned with principles of environmental responsibility and equitable access to technology. I’m particularly interested in methods that make AI systems both more sustainable and more transparent, enabling their deployment in real-world scientific and industrial settings where energy use and interpretability matter.

Computational and Data Science Areas

Applied Computer Science; Applied Mathematics; Artificial Intelligence and Intelligent Systems; Computer Science; Informatics, Analytics and Information Science; Other Computer and Information Sciences; Performance Evaluation and Benchmarking; Statistics and Probability; Training

Motivation

Over the last few months, I have had the privilege of working closely with the SHI to plan the student program for the US-RSE conference. During this time, I have witnessed the care and dedication SHI puts into building meaningful connections and fostering a community of collaboration. As someone who has worked across both academic settings and various national labs, I deeply value the importance of creating a welcoming research environment. I have always tried to create an environment I wish I had when I was starting my own research journey. My doctoral research focuses on machine learning, explainable AI, and energy-efficient network architecture design. I have pursued this work even before the recent AI boom because I believe in developing solutions that directly impact safety, equity, and environmental sustainability. This aligns with my passion for conducting research that bridges theory with practical, societal benefits. Through this program, I hope to further hone my research skills and gain exposure to more advanced and diverse techniques within AI, especially as they relate to the NSF National Artificial Intelligence Research Resource (NAIRR) projects. In particular, I look forward to learning how to apply these techniques in more impactful, real-world contexts, particularly around energy-efficient AI systems. The combination of hands-on project work and community development activities will be an invaluable opportunity for me to expand my research capabilities while building meaningful relationships with mentors and peers who share similar values. Ultimately, my goal is to contribute to the development of sustainable and equitable AI solutions while continuing to grow in an environment that prioritizes inclusion and collaboration. I’m excited for the opportunity to learn, mentor, and build lasting connections through the Sustainable Research Pathways program.

Lightning Talk Title

Resource Efficient and Safe AI for Deployed models

Keywords (Maximum 20 words)

Resource Efficient AI; Adversarial Attack; Machine Learning; RSE