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Alice Wu

dblp:82/5099 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2025
—ORCID · conflict

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Databases, data management, data science and information retrieval · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Generative modeling · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational science and engineering · 100%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational science and engineering
chemistry
0.912025
An Instructible Chemist-AI Alignment Framework for Generating Quaternary Ammonium Compound Structures · KDD (2) 2025

Methods — techniques the papers use, named apart from their topics

hierarchical generative model · 1.7chemist feedback · 1.7
YearPublicationVenuePosition
2025 An Instructible Chemist-AI Alignment Framework for Generating Quaternary Ammonium Compound Structures
abstract
This paper presents a novel Chemist-AI Alignment framework for generating novel structures of quaternary ammonium compounds (QACs), a crucial class of antimicrobial agents.The framework uniquely integrates AI-driven small molecule generation with iterative feedback from chemist experts, leveraging both rapid assessments and comprehensive wet-lab validations to optimize for biological potency and synthetic feasibility.Central to the framework is a hierarchical generative model that captures the QAC hierarchical topology.Extensive experiments highlight the efficacy of the framework in identifying promising QAC candidates, many
Bo Pan 0009, Shiva Ghaemi, Amanda J. Consylman, Ashley Ann Petersen, Alice Wu, Gabriel Chang, Diana McDonough, Mark A. Forman, Elise L. Bezold, William M. Wuest, Kevin Minbiole, Liang Zhao 0002, Amarda Shehu
KDD (2)6
2025 A Tracking Prior to Localization Workflow for Ultrasound Localization Microscopy
abstract
Ultrasound Localization Microscopy (ULM) has proven effective in resolving microvascular structures and local mean velocities at sub-diffraction-limited scales, offering high-resolution imaging capabilities. Dynamic ULM (DULM) enables the creation of angiography or velocity movies throughout cardiac cycles. Currently, these techniques rely on a Localization-and-Tracking (LAT) workflow consisting in detecting microbubbles (MB) in the frames before pairing them to generate tracks. While conventional LAT methods perform well at low concentrations, they suffer from longer acquisition times and degraded localization and tracking accuracy at higher concentrations, leading to biased angiogram reconstruction and velocity estimation. In this study, we propose a novel approach to address these challenges by reversing the current workflow. The proposed method, Tracking-and-Localization (TAL), relies on first tracking the MB and then performing localization. Through comprehensive benchmarking using both in silico and in vivo experiments and employing various metrics to quantify ULM angiography and velocity maps, we demonstrate that the TAL method consistently outperforms the reference LAT workflow. Moreover, when applied to DULM, TAL successfully extracts velocity variations along the cardiac cycle with improved repeatability. The findings of this work highlight the effectiveness of the TAL approach in overcoming the limitations of conventional LAT methods, providing enhanced ULM angiography and velocity imaging.
Alexis Leconte, Jonathan Porée, Brice Rauby, Alice Wu, Nin Ghigo, Paul Xing, Stephen A. Lee, Chloé Bourquin, Gerardo Ramos-Palacios, Abbas F. Sadikot, Jean Provost
IEEE Trans. Medical Imaging4