Cho-Jui Hsieh

【個人資訊】

學校名稱：加州大學洛杉磯分校

實驗室名稱：Computational Machine Learning Lab

研究領域：計算機器學習

職稱：副教授

個人網站：https://web.cs.ucla.edu/~chohsieh/

研究室簡述

實驗室目前的研究重點包括：

1. AI 模型訓練優化：提升模型訓練的效率與品質。
2. LLM的可解釋性與安全性：探討大型語言模型的內部機制與安全防護
3. LLM推理與後訓練：包含推理能力提升、獎勵模型(Reward model)及後訓練(Post-training)技術
4. 生物醫學應用：將LLM推理技術應用於生物醫學領域

與臺大教授合作經驗：

• 畢業於臺大林智仁教授實驗室，過去有許多深入合作
• 與臺大林守得教授、林軒田教授亦有machine learning相關的交流與合作經驗

【研究實習計畫】

研究內容簡介：來訪學生通常會在謝教授及博士生的指導與協助下，獨立領導機器學習相關的研究專案

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：

• 具備機器學習工具箱的使用經驗，例如PyTorch
• 具備機器學習的基礎背景知識（若曾在臺大修習過機器學習課程尤佳）

可接受期長：暑期

接受名額：3名。

簽證文件辦理：由PI透過系所申請，辦理時長約2-3個月。

相關費用：

• 學生需自行負擔簽證服務費用(DS-2019)
• 住宿由學生自行尋覓（無校內住宿或補助）
• 無額外之計畫或學雜費用。

學分與福利：

• 不提供學分
• 無額外獎學金或福利補助

‍

[ Personal Portfolio ]

University: University of California, Los Angeles (UCLA)

Laboratory: Computational Machine Learning Lab

Research Area: Computational Machine Learning

Position: Associate Professor

Personal Website: https://web.cs.ucla.edu/~chohsieh/

Lab Overview
The lab’s current research focuses include:

1. Optimization of AI Model Training: Improving the efficiency and quality of model training
2. Interpretability and Safety of LLMs: Investigating the internal mechanisms and safety protections of large language models
3. LLM Inference and Post-training: Including enhancement of reasoning capabilities, reward models, and post-training techniques
4. Biomedical Applications: Applying LLM reasoning techniques to the biomedical domain

Collaboration with NTU Faculty:

• Received Ph.D. training in the lab of Professor CJLin at NTU, with extensive past collaborations
• Has also engaged in machine learning–related exchanges and collaborations with Professor Shou-De Lin and ProfessorHsuan-Tien Lin at NTU

[ Research Internship Program ]

Research Overview: Visiting students are typically expected to independently lead machine learning research projects under the guidance and support of Professor Hsieh and Ph.D. students.

Target Student Level:

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

Expected Skills and Qualifications:

• Experience with machine learning toolkits, such as PyTorch
• Foundational knowledge in machine learning (having taken relevant ML courses at NTU is a plus)

Available Duration: Summer

Number of Positions: 3

Visa Processing: Processed by the PI through the department; estimated processing time is approximately 2–3 months

Costs:

• Students are responsible for the visa service fee (DS-2019)
• Students must arrange their own housing (no on-campus housing or subsidy provided)
• No additional program or tuition fees required

Academic Credit and Benefits:

• No academic credit provided
• No additional scholarships or financial support available