New York University Abu Dhabi
Undergraduate researcher studying learning and decision-making in structured systems, spanning human–AI interaction and biological sequence modeling.
I study how learning systems extract and use structure in complex environments. My work spans human–AI interaction, where I analyze cooperation and coordination in multi-agent settings, and biological sequence modeling, where I work on protein representation learning. I use machine learning, simulation, and theory to understand and guide behavior in structured systems.
About
I am a B.S. Computer Science student at New York University Abu Dhabi, with a minor in Economics and an expected graduation date of May 2027. My research focuses on learning and decision-making in structured systems. I study how models extract structure from complex data and how that structure shapes behavior, with applications in multi-agent social systems and protein sequence modeling.
My current research focuses on mixed human–AI populations: settings where people, institutions, and artificial agents jointly shape outcomes through coordination, monitoring, advice, and commitment. I approach these questions through simulation, game-theoretic modeling, and empirical evaluation.
Across projects in social simulation, multilingual NLP, and protein representation learning, I am interested in how models learn structure from heterogeneous data and how that learning should be evaluated. This has led me to work with multi-agent simulations, transformer fine-tuning, parameter-efficient adaptation, benchmark design, and model interpretation.
I am developing a capstone research project on AI-mediated coordination in multi-party supply chain social dilemmas, advised by Prof. Hanan Salam and Prof. Benjamin Rosche. I am also working with eBRAIN Lab on biologically informed LoRA methods for protein language models.
Research Agenda
My research sits at the intersection of machine learning and the empirical study of structured systems — both social and biological.
Multi-party social dilemmas, cooperation, and AI-mediated intervention mechanisms.
Protein language models, parameter-efficient fine-tuning, and biologically informed evaluation.
Transformer evaluation for Hindi–English named entity recognition and low-resource settings.