Hi! I am a fourth-year PhD candidate at the Language Technologies Institute at Carnegie Mellon University. I am advised by Graham Neubig and have wonderful friends and collaborators at Neulab :) I will be on the industry job market later this year / early next year — please reach out if you think there's a good fit!

Broadly, I am passionate about making AI genuinely useful for real-world applications and a diverse set of users, drawing inspiration from what people actually need but these systems lack. I am always excited to explore new directions which serve this grander goal, but my work thus far has focused on three main areas:

(1) LLM/Multimodal Evaluation — evaluation, benchmarks, multilingual and multicultural NLP.
Evaluation is what truly drives progress, and it should be grounded in real-world needs. I've built benchmarks and metrics inspired by what people use (or could use) these models for — culturally translating images for applications like ads, education, and TV/film (image transcreation, Best Paper, EMNLP 2024); speech understanding across a long-tail set of languages (FLEURS, Best Paper, SLT 2022); and code-mixed inputs, common in multilingual communities (GLUECoS).

(2) Models/Methodological Interventions — interpretability, alignment, post-training, personalization.
I probe and improve model representations for broader linguistic and cultural coverage — MuRIL, Pangea and Cultural Pangea, and inference-time steering for controllable, culturally aware generation.

(3) Data Selection & Synthetic Data — data-efficient training, synthetic data.
Data has consistently been a bottleneck in my research — whether in the long tail of languages and cultures, or in applications like advertising and education where much relevant data is copyrighted. I've worked on data-efficient fine-tuning (DeMuX), and my proposed work builds synthetic data pipelines for diversity sampling in text-to-image models and for training multimodal generative models in these domains.

I've been fortunate to have my work recognized through fellowships and awards including MIT EECS Rising Star, Rising Star in AI (UMich), BITS 30 Under 30 (Research), CMU Waibel Presidential Fellowship, and two Best Paper Awards at EMNLP 2024 and SLT 2022.

I'm deeply grateful to the brilliant researchers whose mentorship has shaped my growth: Graham Neubig (CMU), Partha Talukdar (Google DeepMind), Sebastian Ruder (Google DeepMind), Alexis Conneau (Google DeepMind), Sunayana Sitaram (Microsoft Research), Monojit Choudhury (Microsoft Research), and Dr. Sreejith V (BITS Pilani).

For more information, check out my CV or reach out via email :)