3 citations · 8 across the 14 of their papers we have counts for
14 papers
What LLM Agents Say When No One Is Watching: Social Structure and Latent Objective Emergence in Multi-Agent Debates
Arman Ghaffarizadeh, Danyal Mohaddes, Aliakbar Izadkhah +1
LLM agents will increasingly act in socially structured settings where role, audience, and relational context can shape what is advantageous or costly to say. We study whether such…
Text Knows What, Tables Know When: Clinical Timeline Reconstruction via Retrieval-Augmented Multimodal Alignment
Sayantan Kumar, Shahriar Noroozizadeh, Juyong Kim +1
Clinical language models increasingly operate over electronic health records (EHRs), yet patient records are not stored as temporally grounded trajectories. Clinical notes describe…
SurvHTE-Bench: A Benchmark for Heterogeneous Treatment Effect Estimation in Survival Analysis
Shahriar Noroozizadeh, Xiaobin Shen, Jeremy C. Weiss +1
Estimating heterogeneous treatment effects (HTEs) from right-censored survival data is critical in high-stakes applications such as precision medicine and individualized policy-mak…
Reflections from Research Roundtables at the Conference on Health, Inference, and Learning (CHIL) 2025
Emily Alsentzer, Marie-Laure Charpignon, Bill Chen +90
The 6th Annual Conference on Health, Inference, and Learning (CHIL 2025), hosted by the Association for Health Learning and Inference (AHLI), was held in person on June 25-27, 2025…
Deep sequence models tend to memorize geometrically; it is unclear why
Shahriar Noroozizadeh, Vaishnavh Nagarajan, Elan Rosenfeld +1
Deep sequence models are said to store atomic facts predominantly in the form of associative memory: a brute-force lookup of co-occurring entities. We identify a dramatically diffe…
The Impact of Medication Non-adherence on Adverse Outcomes: Evidence from Schizophrenia Patients via Survival Analysis
Shahriar Noroozizadeh, Pim Welle, Jeremy C. Weiss +1
This study quantifies the association between non-adherence to antipsychotic medications and adverse outcomes in individuals with schizophrenia. We frame the problem using survival…
PMOA-TTS: Introducing the PubMed Open Access Textual Times Series Corpus
Shahriar Noroozizadeh, Sayantan Kumar, George H. Chen +1
Clinical narratives encode temporal dynamics essential for modeling patient trajectories, yet large-scale temporally annotated resources are scarce. We introduce PMOA-TTS, a corpus…
Reconstructing Sepsis Trajectories from Clinical Case Reports using LLMs: the Textual Time Series Corpus for Sepsis
Shahriar Noroozizadeh, Jeremy C. Weiss
Clinical case reports and discharge summaries may be the most complete and accurate summarization of patient encounters, yet they are finalized, i.e., timestamped after the encount…
Forecasting Clinical Risk from Textual Time Series: Structuring Narratives for Temporal AI in Healthcare
Shahriar Noroozizadeh, Sayantan Kumar, Jeremy C. Weiss
Clinical case reports encode temporal patient trajectories that are often underexploited by traditional machine learning methods relying on structured data. In this work, we introd…
Many-Shot In-Context Learning for Molecular Inverse Design
Saeed Moayedpour, Alejandro Corrochano-Navarro, Faryad Sahneh +14
Large Language Models (LLMs) have demonstrated great performance in few-shot In-Context Learning (ICL) for a variety of generative and discriminative chemical design tasks. The new…
ET tu, CLIP? Addressing Common Object Errors for Unseen Environments
Ye Won Byun, Cathy Jiao, Shahriar Noroozizadeh +2
We introduce a simple method that employs pre-trained CLIP encoders to enhance model generalization in the ALFRED task. In contrast to previous literature where CLIP replaces the v…
TLDR at SemEval-2024 Task 2: T5-generated clinical-Language summaries for DeBERTa Report Analysis
Spandan Das, Vinay Samuel, Shahriar Noroozizadeh
This paper introduces novel methodologies for the Natural Language Inference for Clinical Trials (NLI4CT) task. We present TLDR (T5-generated clinical-Language summaries for DeBERT…
Temporal Supervised Contrastive Learning for Modeling Patient Risk Progression
Shahriar Noroozizadeh, Jeremy C. Weiss, George H. Chen
We consider the problem of predicting how the likelihood of an outcome of interest for a patient changes over time as we observe more of the patient data. To solve this problem, we…
Distributionally Robust Survival Analysis: A Novel Fairness Loss Without Demographics
Shu Hu, George H. Chen
We propose a general approach for training survival analysis models that minimizes a worst-case error across all subpopulations that are large enough (occurring with at least a use…