Biography

I am Khuaja Sediqi, pursuing a B.S. in Information Science + Data Science at the University of Illinois Urbana-Champaign. As a first-generation college student and immigrant my academic journey has been shaped by resilience, curiosity, and a strong desire to use data and technology to address real-world challenges. I have conducted research through the REU REMAT (NSF/DOE) Program at UIUC, where I simulated polymer reactions using FEniCS. I also completed High-Performance Computing and AI training at Argonne National Laboratory, where I gained hands-on experience with large-scale computational systems. As a Data Analytics Intern at Discovery Partners Institute, I led a team in analyzing datasets and generating actionable insights. My research interests lie at the intersection of data science, AI, and sustainability.I aspire to pursue graduate studies and continue coducting scientific research and contributing to projects that use AI and HPC to create sustainable, data-informed solutions that improve lives and promote innovation across disciplines.

Academic Status

Undergraduate Student - 3rd

Research Area/Department

Data Science

Major/Specialty

Information sciences and data science

Degrees Earned or in Progress

Bachelor’s in Information Sciences and Data Science, in progress (B.S./IS+DS/2027)

Academic Preparation

My coursework and hands-on experiences have provided me with a strong foundation for conducting research in data science, agriculture, and human-centered technology. Courses such as C++ Object-Oriented Programming (144 & 242), Special Topics in Data Science (ARC4), Database SQL (206), and Information Sciences Logic and Problem Solving (IS 203) have strengthened my programming, analytical, and problem-solving abilities. My mathematics and physics courses—College Algebra, Plane Trigonometry, Calculus I & II, and Engineering Physics (Mechanical and Wave)—enhanced my quantitative reasoning and ability to apply mathematical models to real-world data. In addition to coursework, I’ve gained valuable laboratory and research experience as a REU REMAT Research Intern at UIUC, where I simulated polymer reactions using FEniCS and managed data with Clowder. My High-Performance Computing (HPC) training at Argonne National Laboratory deepened my technical expertise in data-intensive simulations. I also served as a Data Analytics Intern at Discovery Partners Institute, where I led a team in data manipulation, visualization, and report generation using Python, pandas, NumPy, SciPy, Matplotlib, Seaborn, Scikit-learn, and SQL. Furthermore, I have experience with Tableau, Power BI, ParaView, and MATLAB for visualization and analysis, as well as C++ for algorithmic problem solving. These combined experiences have prepared me to conduct data-driven, interdisciplinary research that bridges technology with impactful real-world applications.

Research/Publications

University of Illinois Urbana-Champaign (UIUC) – As a REU REMAT Research Intern (NSF), I conducted computational materials science research focused on simulating polymerization reactions—specifically the Frontal Ring-Opening Metathesis Polymerization (FROMP) of Dicyclopentadiene (DCPD)—using the FEniCS finite element software package. My project involved developing and implementing mathematical models to simulate the heat transfer, chemical kinetics, and front propagation behavior during polymer curing. I worked with large-scale datasets, performed parameter tuning to achieve stable simulations, and analyzed the resulting data to better understand how reaction conditions influence polymer performance. I also used Clowder, a data management platform, to organize, visualize, and share simulation results within the research team. This experience strengthened my understanding of computational modeling, numerical simulation, and scientific programming, while also teaching me the importance of reproducibility and collaboration in research. Through mentorship from faculty and graduate researchers, I gained insight into the connection between data science, materials research, and high-performance computing, which inspired my interest in applying similar computational techniques to other domains such as agriculture and sustainability.

Research/Academic Interests

My research and academic interests lie at the intersection of data science, artificial intelligence, and sustainability. I am particularly interested in applying computational tools and machine learning to solve real-world challenges in agriculture, biological sciences, environmental systems, and materials science. My goal is to use data-driven approaches to make research more efficient, accurate, and impactful across these interdisciplinary fields.

Computational and Data Science Areas

Informatics, Analytics and Information Science

Motivation

I want to participate in the Sustainable Research Pathways Program because I want to keep learning and continue conducting meaningful scientific and data-driven research. My goal is to go to graduate school and build a career as a researcher, and I believe the best way to learn is through hands-on experience. Working directly on real projects helps me understand how what I learn in class connects to real-world challenges. Through my previous internships and research experiences, I discovered how much growth comes from collaboration and community. Those opportunities helped me build confidence, expand my technical skills, and gain a clearer vision of my career path. Programs like SRP give students like me the chance to keep learning by doing, while also connecting with mentors and other researchers who share the same goals. I want to contribute, learn, and grow as part of that community.

Lightning Talk Title

Advancing Scientific Discovery Through Data Science, AI, and HPC Simulations

Keywords (Maximum 20 words)

Data Science; AI; Machine Learning; HPC; Scientific Simulations; Computational Modeling; Predictive Analytics.