CV

Curriculum Vitae / Resume

Contact Information

Name Kuan-Chieh Lo
Professional Title Ph.D. Student in Computer Science & Engineering
Email lo.311@osu.edu
Location Columbus, Ohio

Professional Summary

Ph.D. student at The Ohio State University working on LLM & Agentic AI, misinformation detection, fairness in federated learning, and LLM safety.

Education

  • 2022 - 2027

    Columbus, Ohio

    Ph.D.
    The Ohio State University
    Computer Science and Engineering
    • Advisor: Dr. Srinivasan Parthasarathy
    • Research: LLM & Agentic AI, Misinformation, Fairness, LLM Safety
  • 2012 - 2014

    Taiwan

    M.S.
    National Taipei University of Technology
    Electrical Engineering
  • 2008 - 2012

    Taiwan

    B.S.
    National Changhua University of Education
    Mechatronics Engineering

Research Experience

  • 2022 - present

    Columbus, Ohio

    Graduate Student Researcher
    The Ohio State University
    • Multi-Agent Adversarial Claim Verification — Proposed a multi-agent LLM framework organizing heterogeneous agents across multiple foundation models, incorporating claim decomposition, multi-hop knowledge retrieval, and adversarial verification. (ICWSM 2026)
    • Agentic AI for Crisis Response — Developed Crisis Observatory, a multi-agent LLM system for crisis response that extracts credible signals from social media by integrating topic modeling, geolocation extraction, and a RAG pipeline. (ICDM 2025)
    • Fairness in Federated Graph Learning — Proposed FairWAG, a fairness-aware federated learning framework that applies Shapley Values to quantify client contributions, enabling adaptive aggregation weights. (EAAMO 2025)
    • LLM Safety: Jailbreak Attacks on Large Reasoning Models — Uncovered a prompt injection vulnerability in Large Reasoning Models (LRMs), where adversarially crafted prompts inject a spoofed chain-of-thought block to bypass safety alignment. (Under Review)
    • Compositionality Evaluation of VLMs — Assessed compositional reasoning capabilities of state-of-the-art vision-language models (VLMs), exposing critical skill gaps across object detection, relational extraction, and attribute binding tasks.
  • 2019 - 2022

    Taipei, Taiwan

    NLP Research Scientist
    Academia Sinica
    Mentor: Dr. Lun-Wei Ku
    • Misinformation Mitigation via Recommendation Systems — Developed VICTOR, a reinforcement learning-based module that implicitly re-ranks news recommendations to surface verified articles. (WWW 2022, WSDM 2021)
    • Echo Chamber Reduction — Built a news-analysis platform that applies a stance classification model to present multi-source perspectives on events, reducing filter bubble effects. (WWW 2021)
    • Visual Storytelling Evaluation — Developed Vrank, a reference-free automatic evaluation metric for visual storytelling (VIST), achieving ~30% higher accuracy than existing metrics. (ACL 2022)

Work Experience

  • 2014 - 2018

    Taiwan

    Software and Firmware Engineer
    ASUSTeK Computer Inc.
    • Developed system firmware for ASUS products, including gaming desktops, business and consumer laptops.

Skills

LLM & Agentic AI (Expert): LangChain, LangGraph, RAG, Multi-agent Orchestration, MCP, Agent Skills
Machine Learning / Data Science (Expert): PyTorch, TensorFlow, Scikit-learn, NLTK, Pandas, NumPy, Matplotlib
Data & Vector Databases (Advanced): ChromaDB, FAISS, MongoDB, MySQL
Programming Languages (Expert): Python, Java, JavaScript, C/C++
Web Development & Tools (Advanced): Flask, Django, Node.js, Vue, Docker, Git, Nginx

Academic Services

  • -

    Reviewer
    Conference Reviewer
    • ICML (2026), ICWSM (2026), NeurIPS (2024), ACL (2024, 2022), AAAI (2022), IAAI (2022, 2021, 2020), NLPCC (2021), EMNLP (2020)

Teaching

  • 2023 - present

    Columbus, Ohio

    Graduate Teaching Associate
    The Ohio State University
    • Database Systems (Autumn 2023, Spring 2024)
    • Data Mining (Autumn 2024, Autumn 2025, Spring 2026)
    • Network Science (Spring 2025)

Projects

  • Creativity in Large Language and Vision-Language Models

    Investigated the compositional factors influencing creative output in LLMs and vision-language models (VLMs), analyzing how individual components (e.g., concept, style, structure) interact to drive creativity.

  • LLM-Powered Agentic Chatbot for Bridal Consultation

    Designed and developed a multi-agent AI system for a bridal company integrating a conversational LLM chatbot, a personalized recommender system agent, and a meeting scheduling agent into a unified pipeline.

  • LLM-Assisted Legal Document Processing System for Law Firms

    Built an NLP pipeline for law firms leveraging LLMs and retrieval-augmented generation (RAG) to automatically retrieve semantically relevant past legal cases and populate structured legal documents.