Institution profile

Arizona State University

Academic institutionnorthamerica · us
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Research library1,225linked papers
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Selected work

Representative Papers

A Comprehensive Survey on Vector Database: Storage and Retrieval Technique, Challenge

Oct 18, 2023arXiv.org

Managing and retrieving high-dimensional vector data poses significant challenges, particularly as traditional databases fail to meet performance requirements and the need for tight integration with large language models (LLMs) intensifies. Method: This paper systematically surveys four major approximate nearest neighbor search (ANNS) paradigms—hashing, tree-based indexing, graph-based methods (e.g., HNSW), and quantization (PQ/SQ)—and integrates hybrid optimization strategies. Contribution/Results: It introduces, for the first time, a “Four-Dimensional Methodology” framework tailored for industrial deployment of vector databases, analyzing trade-offs among accuracy, latency, memory footprint, and scalability. The work constructs a structured knowledge graph covering 200+ ANNS algorithms and proposes a novel paradigm for deep synergy between vector databases and LLMs. Collectively, these contributions provide both theoretical foundations and practical guidelines for system selection, architectural design, and development of AI-native database systems.

62 citations3 influentialRead paper

LPMLN, Weak Constraints, and P-log

Feb 12, 2017AAAI Conference on Artificial Intelligence

This work addresses the semantic and computational fragmentation among three nonmonotonic probabilistic logics: LPMLN, weakly constrained answer set programs (WCPs), and P-log. Methodologically, it introduces bidirectional, semantics-preserving translations: the first sound and complete translation from LPMLN to WCPs, and from P-log to LPMLN. It rigorously establishes their equivalence in both model-theoretic semantics and probabilistic inference. Technically, LPMLN’s MAP inference is reduced to a weak-constraint ASP problem, enabling direct execution on standard ASP solvers (e.g., clingo); conversely, P-log’s probabilistic reasoning is encoded into LPMLN or Markov Logic Network (MLN) form, facilitating joint computation via ASP/MLN solvers. The framework unifies probabilistic nonmonotonicity with quantitative preference handling, providing a foundational basis for extended systems—such as causal modeling—that require integrated logical and probabilistic reasoning.

37 citations6 influentialRead paper

System aspmt2smt: Computing ASPMT Theories by SMT Solvers

Sep 24, 2014European Conference on Logics in Artificial Intelligence

This work addresses the low computational efficiency of stable model solving for Answer Set Programming Modulo Theories (ASPMT) under real-number constraints. We propose the first semantics-preserving, compact fragment-to-SMT automatic compilation method. Our approach leverages *gringo* for partial grounding and uniformly encodes the remaining logical variables and real arithmetic constraints into SMT-LIB format, enabling stable model computation via general-purpose SMT solvers such as Z3. The key innovation lies in defining translation rules grounded in the functional stable model semantics, thereby establishing the first formal semantic bridge between ASP and SMT—supporting nonmonotonic reasoning and modeling of continuous change. Experimental evaluation demonstrates substantial improvements in expressiveness and solving efficiency for real-arithmetic reasoning and dynamic system modeling. The method provides a scalable, automated foundation for formal verification of physical processes.

30 citations4 influentialRead paper

Application-Driven Innovation in Machine Learning

Mar 26, 2024International Conference on Machine Learning

Application-driven machine learning (ML) research has been systematically undervalued in academia, leading to a growing disconnect between algorithmic innovation and real-world needs; this marginalization is reinforced by structural biases in peer review, faculty hiring, and pedagogy. Method: This paper introduces, for the first time, a formally defined “application-driven ML research paradigm,” elucidating its complementary relationship with the dominant methodology-driven paradigm. Drawing on interdisciplinary frameworks from education theory, research governance, and ML practice—and substantiated by empirical case studies and institutional critique—it diagnoses three systemic barriers hindering such research. Contribution/Results: The core contribution is a set of actionable, process-level interventions to reform academic evaluation systems, grounded in both theoretical analysis and pragmatic implementation pathways. These proposals have already catalyzed curricular reforms in AI education and adjustments to national funding review criteria across multiple universities, fostering cross-domain collaboration and methodological feedback loops between application domains and core ML research.

22 citations2 influentialRead paper

Social Media for Mental Health: Data, Methods, and Findings

Nov 11, 2025Lecture Notes in Social Networks

This study addresses the challenge of early detection of mental health conditions—including depression, anxiety, and suicidal ideation—by proposing a non-invasive, multimodal psychological risk assessment framework grounded in social media data. Methodologically, it introduces the first systematic integration of textual, visual, and affective representations, leveraging natural language processing, machine learning, and feature engineering, and validates findings through large-scale survey-based annotation and empirical testing. Key contributions include: (1) development of an interpretable, cross-platform model for mental state identification, achieving robust detection of depressive and anxious states on real-world social media data; (2) empirical validation of social media analytics for clinical auxiliary screening, real-time crisis intervention, and public health policy responsiveness; and (3) advancement of a paradigm shift from individual-level monitoring to population-level mental health governance, thereby providing both methodological foundations and translational pathways for digital mental health services.

7 citationsRead paper
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