Biography

I am Sree Sai Charan Vaitla, a Computer Science undergraduate at Youngstown State University with a focus on data science, machine learning, and computational sustainability. My research and internship experiences have strengthened my ability to translate complex data into actionable insights. At First National Bank, I engineered automated SQL pipelines and Power BI dashboards that optimized reporting efficiency, while at UR2PhD I developed deep-learning models for particle-track reconstruction using GNNs and transformers to improve scientific data analysis. My goal is to apply these skills to sustainability-driven domains-leveraging AI and predictive modeling to enhance energy efficiency, optimize resource allocation, and support data-informed environmental decisions. I am especially interested in using machine-learning techniques to analyze large-scale climate and sensor datasets, enabling more accurate forecasts and resilient infrastructure planning. The Sustainable Research Pathways program offers an ideal platform to collaborate with scientists tackling climate, energy, and environmental challenges through computational innovation. I hope to contribute to projects that use AI to accelerate sustainability research while expanding my expertise in data modeling, scientific collaboration, and real-world impact.

Academic Status

Undergraduate Student - 3rd

Research Area/Department

Computer Science; Data Science; Machine Learning/AI

Major/Specialty

Computer Science

Degrees Earned or in Progress

Bachelor's Degree in Computer Science/ Anticipated Graduation : May 2027

Academic Preparation

I have completed key Computer Science and Mathematics courses including Advanced Object-Oriented Programming, Programming and Problem Solving, Data Structures and Objects, Discrete Structures, Information Assurance, and Data Analytics Project, along with Multivariable Calculus. These courses provided a solid foundation in programming, algorithms, data analysis, and problem-solving, while also building my understanding of data security and mathematical reasoning-skills that directly prepare me for computational and data-driven research in sustainability and scientific innovation.

Research/Publications

I have conducted research and technical projects across both academic and professional settings. At Youngstown State University, I served as a Data Science Research Intern, leading over 25 projects involving Python, SQL, and R for data validation and retrieval optimization. I also interned at UR2PhD, where I co-authored the research project “Advancing Particle Track Reconstruction: Evaluating Hit Embedding Methods on the TrackML Dataset” (Spring 2025, with Aliza Khan). This work compared supervised, graph-neural-network, and transformer-based deep-learning models to improve particle-track reconstruction efficiency in high-energy-physics datasets. Additionally, during my Data Engineering Internship at First National Bank, I developed automated SQL pipelines and Power BI dashboards to optimize large-scale data processing-experience that strengthened my applied research skills in data engineering and computational modeling.

Research/Academic Interests

My research interests lie at the intersection of artificial intelligence, computational sustainability, and data-driven science. I am particularly passionate about using machine-learning and deep-learning methods to address large-scale environmental and scientific challenges-such as modeling climate dynamics, optimizing renewable-energy systems, and improving data efficiency for sustainable infrastructure. My recent work in deep learning for particle-track reconstruction sparked my interest in extending these computational methods beyond physics into sustainability domains that rely on complex, high-dimensional data. I aim to develop models that enhance the accuracy, scalability, and interpretability of data analysis for climate and energy applications. Through the Sustainable Research Pathways program, I hope to collaborate with scientists applying AI to real-world sustainability research and to contribute innovative computational approaches that advance data-driven environmental solutions.

Computational and Data Science Areas

Applied Computer Science; Artificial Intelligence and Intelligent Systems; Computer Science; Informatics, Analytics and Information Science; Other Computer and Information Sciences; Particle and High-Energy Physics; Visualization and Human-Computer Systems

Motivation

I want to participate in the Sustainable Research Pathways program because it represents everything I value in research collaboration, mentorship, and using technology to make a meaningful impact. My journey in computer science and data science has shown me how powerful data can be when applied to real-world problems. Through my current research on deep learning for scientific data, I have seen how computational tools can accelerate discovery. I now want to direct those skills toward sustainability an area where innovation can directly improve lives and the environment. I’m also drawn to the community that Sustainable Horizons Institute is building. I come from a background where mentorship and access to opportunities made a big difference for me, and I want to be part of a program that values inclusion and shared growth. I hope to contribute my technical perspective, learn from diverse researchers, and build long-term collaborations that help create more sustainable, data-driven solutions for our world.

Lightning Talk Title

Machine Learning for Computational Sustainability and Scientific Discovery

Keywords (Maximum 20 words)

Artificial Intelligence; Machine Learning; Graph Neural Networks; Computational Sustainability; Data Science; Scientific Computing