Research Opportunities
I am always looking for motivated students interested in machine learning, privacy-preserving technologies,
and security analysis. These projects offer opportunities to work at the intersection of theory and practice,
contributing to both academic research and real-world applications.
Research Focus Areas
Privacy-Preserving Machine Learning
Projects focusing on developing machine learning algorithms that protect individual privacy through differential privacy, federated learning, and secure computation techniques. Applications include medical data analysis and distributed learning systems.
Security and Compliance Analysis
Automated analysis of web technologies for GDPR compliance, cookie tracking detection, and privacy policy analysis. These projects combine machine learning with legal requirements to develop practical tools for privacy protection.
Access Control and Policy Mining
Development of universal methods for mining access control policies from organizational data. Projects involve designing algorithms that can work across different policy languages and organizational structures.
Robust Machine Learning
Creating machine learning systems that maintain performance under distribution shifts and adversarial conditions. Projects explore causal inference, domain adaptation, and invariant feature learning.
How to Apply
For ETH Zurich Students: If you are interested in any of these projects, please send me an email with your CV, transcript, and a brief description of your interests and relevant background. Include "Research Project Application" in the subject line.
Prerequisites: Strong mathematical background, programming experience (preferably Python), and genuine interest in machine learning or security research. Prior coursework in machine learning, statistics, or security is advantageous but not strictly required for motivated students.
What to Expect: Projects typically last 6-12 months and involve literature review, algorithm development, implementation, and experimental evaluation. Students will have regular meetings, access to computational resources, and opportunities to present their work at conferences or workshops.
Timeline: Projects can be started at any time of the year, with coordination based on student availability and project requirements. Most thesis projects align with the standard ETH academic calendar.
Contact
Get in Touch
Email: ccarlos (at) inf.ethz.ch
Office: ETH Zurich, Department of Computer Science
Institute of Machine Learning
I welcome inquiries from motivated students and look forward to discussing potential research opportunities. Please feel free to reach out even if you don't see a project that exactly matches your interests – we can often adapt projects or develop new ones based on mutual interests and current research directions.