Secure Social Computing Lab
Research Field
Dr. Chun-Ming (Tim) Lai is a distinguished figure in the realm of computer security and social media research, with a focus on practical applications. His expertise lies in the development of information security(ethical hacking) and the identification of misinformation on platforms like Facebook. Presently, he holds the position of Associate Professor in the Department of Computer Science and Cloud Innovation School at Tunghai University, a role supported by Amazon Web Services.
Dr. Lai's academic journey culminated in earning his Ph.D. in Computer Science from UC Davis in June 2019, under the mentorship of Professor Shyhtsun Felix Wu. Known for his fervor for interdisciplinary collaboration, Dr. Lai frequently engages in research integrating other disciplines, particularly utilizing data analysis to corroborate social science theories. He is a staunch believer in the synergy of social science and computer science, advocating for the creation of systems that are not only secure but also resonate more profoundly with human values.
His primary research aim is to contribute to the evolution of information technology in ways that emphasize equitable distribution and trust. Dr. Lai's scholarly output includes over 10 peer-reviewed journal articles and more than 30 papers presented at prestigious conferences such as KDD, ICLR, CNSM, and ASONAM. His research endeavors have received support from both academic and industrial entities. Additionally, he is an active participant in the academic community, serving as a reviewer for various journals and conferences, including ICWSM, IEEE Access, and ASONAM.
In his native culture, Dr. Lai is known by his Chinese name, 賴俊鳴.
Beyond academia, Dr. Lai has a penchant for travel, adeptly navigating the world of points and miles. He also indulges in golf as an amateur player and has a keen interest in oenology, certified by the Wine & Spirit Education Trust (WSET).
The Secure Social Computing Lab is composed of master's students and project students, with approximately 20 members in total. The lab primarily focuses on cutting-edge applications in information security and artificial intelligence.
Our approach involves using information systems to address practical industry problems. Our main projects are categorized into research-based and practical implementations.
Research-based topics include:
- Blockchain combined with IoT for monitoring carbon emissions using smart contracts.
- Applying multimodal artificial intelligence models to track memes and trace the origins of rumors.
- Utilizing large language models to read threat intelligence and generate defense rules.
Practical implementation topics include:
- Smart warehousing.
- AGV (Automated Guided Vehicle) systems.
- Setting up CTFd (Capture The Flag daemon) for cybersecurity competitions and data analysis. Students are also encouraged to develop their own research topics.
The lab maintains a liberal atmosphere with American-style management. We offer snacks and beverages, and hold group meetings approximately every 1-2 weeks. Master's students meet weekly to discuss research papers.
Information Security and Blockchain; Penetration Testing; Applied AI; Social Media Data Mining; Disinformation Diffusion
- Implementing Multimodal Fact-Checking Across Countries and Model Hallucination Research through Containerization Combined with Large Language Models, 2024-2027(3y) NSTC funded, NTD: $2,498,000
- Penetration Test 2023. Chunghwa Information Security International Co., Ltd funded. NTD:105,000
- Research and Implementation of Intelligent Warehouse Management System 2023. JTX Co. LTD funded: NTD: 276,000
- A Study of Deep Learning Algorithms for Defect Feature Extraction and Recognition 2022. III funded. NTD:98,000
- AI Sprouting Project-Integrated Image Recognition for Automated Guided Vehicles in Smart Factory 2022. Project facilitator. NTD:500,000
- 2023 National Information Security Conference Best Presentation Award and Best Student Paper
- AWS Educate Global Faculty Cloud Ambassador. 2020
- Graduate Program Travel Grant, UC Davis 2018, 2017
- Graduate Student Fellowship, UC Davis 2013
- Ph.D. Department of Computer Science, UC Davis, 2019
- M.S Department of Computer Science, UC Davis, 2017
- B.S. Department of Computer Science and Information Engineering, National Taiwan University, 2013
1 Vacancy
Job Description
- Collaborate with the research team to design and implement AI models for syslog analysis.
- Develop algorithms to detect anomalies indicative of DDoS attacks.
- Enhance existing models inspired by the DeepLog framework for improved accuracy and efficiency.
- Analyze large-scale network data to identify patterns and anomalies.
- Document research findings and methodologies clearly and effectively.
Preferred Intern Education Level
Bachelor's or Master's students in Computer Science, Information Technology, Data Science, or related fields.
Skill sets or Qualities
- Programming Proficiency: Experience with Python; familiarity with machine learning libraries such as TensorFlow or PyTorch is advantageous.
- Machine Learning Knowledge: Understanding of basic machine learning concepts; experience with anomaly detection models is a plus.
- Data Analysis: Ability to preprocess and analyze large datasets; experience with log data is beneficial.
- Cybersecurity Awareness: Basic understanding of network security principles and DDoS attack mechanisms.
- Problem-Solving Skills: Strong analytical and critical thinking abilities.
- Communication: Effective verbal and written communication skills; ability to document research findings clearly.
- Team Collaboration: Ability to work collaboratively in a research environment.
1 Vacancy
Job Description
- Conduct comprehensive literature reviews on existing LLM jailbreak techniques, such as prompt injection, role-playing prompts, and adversarial attacks.
- Design and execute experiments to test the effectiveness of different jailbreak methods on various LLMs.
- Analyze vulnerabilities in LLMs and assess the impact of jailbreaks on model behavior and output.
- Collaborate with the team to develop strategies and tools to mitigate identified vulnerabilities.
- Document research findings and contribute to academic papers or technical reports.
- Stay updated with the latest advancements in AI safety and security.
Preferred Intern Education Level
Bachelor's or Master's students in Computer Science, Artificial Intelligence, Cybersecurity, or related fields.
Skill sets or Qualities
- Technical Proficiency: Understanding of machine learning concepts and natural language processing.
- Research Skills: Ability to conduct thorough literature reviews and synthesize information from various sources.
- Analytical Abilities: Strong problem-solving skills and attention to detail in experimental design and data analysis.
- Programming Skills: Experience with programming languages such as Python; familiarity with machine learning libraries like TensorFlow or PyTorch is advantageous.
- Communication: Ability to clearly document and present research findings to both technical and non-technical audiences.