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publications
iSMOD: an integrative browser for image-based single-cell multi-omics data
Published in Nucleic Acids Research, 2023
This paper presents iSMOD, which is the first integrative browser for collecting and analyzing FISH and nucleus proteomics data. By integrating multi-omics studies from 20,000+ published papers, it provides powerful tools for exploring cellular mechanisms and advancing biological research through comprehensive data visualization and analysis.
STRUX: An LLM for Decision-Making with Structured Explanations
Published in NAACL, 2025
This paper presents STRUX, which is a novel LLM framework that enhances decision-making through structured explanations. By distilling complex information into key facts and employing self-reflection steps, it categorizes and prioritizes factors affecting decisions. We demonstrate its effectiveness in stock investment decisions based on earnings call transcripts, making LLM decision-making more transparent and practical.
DeFine: Enhancing LLM Decision-Making with Factor Profiles and Analogical Reasoning
Published in ACL Findings, 2025
This paper presents DeFine, a novel approach that bolsters LLM-based decision-making through factor profiles and analogical reasoning. By systematically identifying and comparing key factors across contexts, DeFine promotes deeper context comprehension and more robust inferences. Our experiments on finance-related reasoning tasks demonstrate its ability to deliver both higher accuracy and enhanced interpretability, with broader applications to various decision-making scenarios.
Communication to Completion: Modeling Collaborative Workflows with Intelligent Multi-Agent Communication
Published in Under Review, 2025
In this work, we introduce Communication to Completion (C2C), a scalable multi-agent framework that enhances task oriented collaboration through structured communication. C2C features the Alignment Factor (AF), a novel metric quantifying task understanding, and a Sequential Action Framework that enables cost aware communication decisions. Evaluated on realistic coding workflows across varying team sizes and complexity tiers, C2C reduces task completion time by 40%, establishing both theoretical foundations and practical utilities for communication efficient multi-agent systems.
Large Language Models at Population Scale: A Survey and Taxonomy of Public Health Applications
Published in Under Review, 2026
This paper presents the first comprehensive survey of LLMs in public health through a two-dimensional taxonomy, which maps six core public health tasks against five functional LLM roles. Our analysis reveals a critical gap: while current LLMs excel at extracting health signals from text, they struggle with the complex reasoning required for population-level challenges. We argue that LLMs must evolve from individual-level clinical assistants into societal-scale reasoning engines capable of modeling disease dynamics, simulating policy interventions, and ensuring health equity on a global scale.
EpiEvolve: Self-Evolving Agents for Streaming Pandemic Forecasting under Regime Shifts
Published in Under Review, 2026
This paper introduces EpiEvolve, a self-evolving agent that wraps a frozen LLM forecaster for streaming pandemic forecasting under regime shifts. EpiEvolve adapts through hierarchical episodic memory, regime-conditioned retrieval, and outcome-informed lesson distillation, without updating model parameters. On weekly COVID-19 hospitalization forecasting across five variant regimes, EpiEvolve reaches 0.629 average accuracy (vs. 0.561 for the static backbone and 0.325 for the CDC ensemble) and cuts recovery lag after regime shifts from 5 to 2 weeks.
talks
Talk 1 on Relevant Topic in Your Field
Published:
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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