PROJECT FEED

We are looking for a highly motivated Summer Research Intern to join an exciting project at the intersection of optimization, natural language processing, and machine learning. This opportunity involves close collaboration with Prof. Garud Iyengar and offers the potential for long-term impact and product development. The goal of the internship is to develop a proof-of-concept system that can: Interpret natural language descriptions of optimization problems. Automatically formulate these problems. Solve them using industry-standard optimizers such as Gurobi, CVXPY, etc. The prototype will be developed primarily in Python. Ideal Candidate Profile We're looking for candidates who are eager to learn, build, and iterate quickly. You should be comfortable working independently and motivated to take ownership of the project.

LABS's interests are in developing and applying statistical, machine learning, and artificial intelligence (ML/AI) methodologies to “big data” in multi-omics and medical data for aging and diseases such as Alzheimer’s disease. We emphasize utilizing advanced ML/AI techniques and multi-omics data, including MRI, genetics, transcriptomics, and proteomics, to solve clinically relevant problems for precision medicine. The applicant will learn to process medical imaging data (e.g., brain MRI) using AI/ML and related computational tools for this project. All relevant directions can be pursued based on discussions, interests, and expertise.