Kuan-Hao Huang

【個人資訊】

學校名稱：德州農工大學

實驗室名稱：Frontier Language AI Research Lab

研究領域：自然語言處理 (NLP)、大型語言模型 (LLMs)、視覺語言模型 (VLMs)

職稱：助理教授

個人網站：https://khhuang.me/

 實驗室網站：https://khhuang.me/group.html

與臺大教授合作經驗：

• 資訊系林軒田教授：透過國科會千里馬計畫與林教授的博士生合作，並有3篇研究論文產出。
• 電機系孫紹華副教授：與孫教授的學生合作，並有2篇論文產出。
• 資訊系陳尚澤副教授：與陳教授學生合作，並有1篇論文產出。

【研究實習計畫】

研究內容簡介：開發語言AI Agent，教導其理解人類概念、使用人類語言及進行邏輯推理。目前核心方向包括：

1. LLMs與VLMs的穩健性（Robustness）： 探討導致模型行為不可預測的挑戰（如對抗性輸入、跨領域不一致性），提升其在真實應用的穩定性。
2. 多模態推理(Multimodal Reasoning)：整合文本、影像與影片資訊，達成對跨模態概念的深度理解與推理。
3. 多語系理解(Multilingual Understanding)：研究模型如何習得跨語言的共享知識，以支持更公平、全球化的語言理解環境。

期待學生教育程度：‍

• 大三（含）以上大學生
• 碩士生
• 博士生

期待學生技能與特質：

• 對研究具有強烈熱忱、具備自信，且願意投入至少一年時間完成專題
• 修習過機器學習(ML)及自然語言處理(NLP)相關課程
• 具備半年以上AI相關研究經驗或大學專題生經驗
• 未來有申請國外博士學位之打算。
•  (加分項目)具備以英文進行研究討論與口頭報告的能力。‍

可接受期長：暑期

接受名額：2名。‍

簽證文件辦理：由PI透過學校國際處申請，辦理時長約 1-2 個月。

相關費用：

• ‍‍PI負擔簽證服務費用(DS-2019)
• 學生需自行尋覓住宿（無補助）‍

學分提供：否。

‍

[ Personal Portfolio ]

University: Texas A&M University

Laboratory: Frontier Language AI Research Lab

Research Areas: Natural Language Processing (NLP), Large Language Models (LLMs), Vision-Language Models (VLMs)

Position: Assistant Professor

Personal Website: https://khhuang.me/

Lab Website: https://khhuang.me/group.html

Collaboration with NTU Faculty:

• Professor Hsuan-Tien Lin, Department of Computer Science and Information Engineering: Collaborated with Professor Lin’s Ph.D. students through the Graduate Student Study Abroad Program by NSTC, resulting in three research papers.
• Associate Professor Shao-Hua Sun, Department of Electrical Engineering: Collaborated with Professor Sun’s students, resulting in two research papers.
• Associate Professor Shang-Tse Chen, Department of Computer Science and Information Engineering: Collaborated with Professor Chen’s students, resulting in one research paper.

[ Research Internship Program ]

Research Overview: The lab focuses on developing language AI agents capable of understanding human concepts, using natural language, and performing logical reasoning. Current core research directions include:

1. Robustness of LLMs and VLMs: Investigating challenges that lead to unpredictable model behavior (e.g.,adversarial inputs, cross-domain inconsistencies), with the goal of improvingreliability in real-world applications.
2. Multimodal Reasoning: Integrating text, images, and video to enable deep understanding and reasoningacross modalities.
3. Multilingual Understanding: Studying how models acquire shared knowledge across languages to support a moreequitable and globally inclusive language understanding environment.

Target Student Level:

• Undergraduate students (junior year and above)
• Master’s students
• Ph.D. students

Expected Skills and Qualifications:

• Strong passion for research, confidence, andwillingness to commit at least one year to a research project
• Completion of coursework in Machine Learning (ML) and Natural Language Processing (NLP)
• At least six months of experience in AI-relatedresearch or undergraduate research projects
• Intention to pursue a Ph.D. degree abroad in thefuture ‍
• (Preferred)Ability to conduct research discussions and oral presentations in English

Available Duration: Summer

Number of Positions: 2

Visa Processing: The PI will apply through the university’s Office of International Affairs; processing time is approximately 1–2 months

Costs:

• The PI will cover the visa processing fee (DS-2019)
• Students are responsible for arranging their own accommodation (no subsidy provided)

Academic Credit: Not offered