Selvaprabu Nadarajah
Email Address: selvan@uic.eduCollege: Business Administration Department: Information and Decision Sciences
Title: Associate Professor
Webpage: https://www.selva-nadarajah.com/
Participating in the Chancellor’s Undergraduate Research Awards program: Yes
Research Interest:
My research develops computational and AI methods for decision-making in large-scale energy and computing systems. On the energy side, this includes planning and operating the power grid as it integrates more renewables, corporate renewable energy procurement, and the growing interactions between energy, water, and data-center computing. Methodologically, I work on approximate dynamic programming and Markov decision processes (MDPs), optimization, and machine learning, as well as on how large language models (LLMs) can support decision-making and extract insights from corporate and policy documents (for example, sustainability and biodiversity disclosures). I lead the IDIATER research group and collaborate closely with Argonne National Laboratory. Minimum time commitment in hours per week: 10 Qualifications of a Student:
Good catch — yes, the Qualifications box should be adjusted to match the broadened framing. The Expectations box is mostly fine but worth a small tweak. Here are the updated versions: Qualifications of a Student (updated) Seeking one motivated undergraduate with strong quantitative and programming skills. Required: GPA 3.5 or higher; solid Python proficiency (numpy, pandas); completed coursework in linear algebra, probability or statistics, and at least one optimization, operations research, or machine learning course. Preferred: exposure to one or more of the following — dynamic programming or reinforcement learning, mathematical optimization (Gurobi or similar solvers), PyTorch or Hugging Face, LaTeX. Interest in energy systems, power grids, or sustainability is a plus. Preferred majors include Information and Decision Sciences, Computer Science, Mathematics, Statistics, Industrial Engineering, Electrical Engineering, or a related quantitative field. Honors College students and those with prior research experience are especially encouraged to apply. Students in their sophomore or junior year are preferred so that the engagement can extend over multiple semesters. Brief Summary of what is expected from the student:
The student will contribute to one of two active research directions, matched to their background and interest: (1) computational experiments on decision-making and optimization methods for energy and computing systems, including implementing and benchmarking algorithms on problems such as power grid planning or resource allocation; or (2) using large language models to extract and analyze structured information from corporate and policy documents, with applications in sustainability and infrastructure decision-making. Typical responsibilities include writing and debugging Python code, running and documenting computational experiments on university or cloud compute, producing clear plots and result summaries, conducting targeted literature reviews, and participating in weekly meetings. High-performing students will have opportunities to co-author conference or journal submissions. Interested applicants should email a brief note with a CV, unofficial transcript, and one paragraph indicating why at least one the two directions interests them. Contact researcher via URE Email Webform
