about
Hey :) I am a 4th-year Computer Science / Artificial Intelligence PhD student at MIT, advised by David Sontag, and an EWSC PhD fellow at the Broad Institute of MIT and Harvard. Previously, I spent two summers at Apple and Microsoft as a research intern. I received my bachelor's in electrical and electronics engineering from Bilkent University, Türkiye.
research
I am interested in representation learning, large language models (LLMs), sample-efficient learning, causal inference, and continual learning, often applied to healthcare.
LLMs + representation learning + sample-efficiency: How can LLMs help learn and design good representations of data—particularly in complex domains—that enable sample-efficient downstream learning?12
Causal inference + sample-efficiency: How can we integrate large-scale real-world evidence with small-scale experimental data to power valid and efficient causal inference?34567
During my PhD, I have also worked on AI for science, including learning representations for the adaptive immune system,8 and predictive modeling with metabolomic and proteomic data.910 Before my PhD, I worked on reinforcement learning for personalized type-1 diabetes management11 and dabbled in communication networks theory.1213
Always happy to chat about research—the best way to reach me is via email.
selected work
Using LLMs for Late Multimodal Sensor Fusion for Activity Recognition
NeurIPS, Time Series for Health Workshop, 2025 — Work done during a summer internship at Apple.
news
- May 2026 Started my summer research internship at Layer Health!
- Apr 2026 Uncovering bias mechanisms in observational studies is accepted at ICML 2026!
- Mar 2026 New preprint! We use LLMs to streamline building powerful input representations in complex datasets to enable sample-efficient downstream learning.
- Dec 2025 Presenting two papers at NeurIPS! Zero-shot activity prediction with multimodal data using LLMs from my summer internship at Apple, and prediction-powered causal inferences with R. Cadei and P.D. Bartolomeis.
- Oct 2025 Presenting Prediction-powered generalization of causal inferences at IMES retreat! Slides are here.
- Jul 2025 I gave a hands-on practical workshop on causal inference at Paris AI4Health school! The material is here.
- Jun 2025 New preprint! We propose a method for detecting the source of bias in observational data by evaluating it against experimental data.
- May 2025 Started my summer research internship at Apple! I'll work on reasoning over multimodal data with LLMs.
- Feb 2025 Serving as the discussant for Maggie Makar's talk at the causal inference seminar organized by the Harvard Data Science Initiative!
- Jan 2025 T-cell representation learning work from my Microsoft internship is accepted at ICLR! We developed an approximate linear-time method for scalable learning of T-cell representations.
- Jul 2024 Presenting Prediction-powered generalization of causal inferences at ICML in Vienna!
- May 2024 Started my summer research internship at Microsoft! I'll work on developing better representations & models of the human immune system.
- Apr 2024 Presenting Benchmarking observational studies with experimental data under right-censoring at AISTATS in Valencia!