Published in ACM CCS 2021 PPML Workshop, 2021
A secure multi-label tumor classification method using homomorphic encryption, whereby two different machine learning algorithms, SVM and XGBoost, are used to classify the encrypted genome data of different tumor types.
Published in IEEE International Conference on E-health Networking, Application & Services (HealthCom), 2024
We propose a privacy-preserving data imputation framework using secure multi-party computation (MPC) to address missing values in medical datasets, ensuring regulatory compliance and maintaining model accuracy.
Published in IEEE Access, 2025
This paper presents a privacy-preserving inference framework for clinically actionable machine learning models, using homomorphic encryption to enable secure and accurate predictions.
Published in IEEE Access, 2025
Privacy-preserving approach for logistic regression that integrates secret sharing-based multi-party computation (MPC) with differential privacy (DP).
Published in Journal of Biomedical Informatics, 2025
A novel and efficient Privacy-Preserving Record Linkage method based on a secure 3-party (MPC)computaion framework
Published in International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB), 2026
A secure multi-party computation approach for identifying rare disease-associated cell subsets across distributed single-cell datasets, without revealing any party's private data.
Semesters: Fall 2017–2018, Fall 2018–2019
TA in Fall 2017–2018 & Fall 2018–2019. Provided support for coursework and student projects.
Semesters: Spring 2017-2018
Assisted weekly labs in Spring 2017–2018 for a class of 30 students.
Semesters: Spring 2018-2019
Held weekly recitations for a class of 30 students in Spring 2018–2019.
Semesters: Spring 2019-2020, Spring 2021-2022
TA for Distributed Systems in Spring 2019–2020. Held weekly recitation sessions and office hours for ~35 students.
Semesters: Fall 2019-2020, Spring 2020-2021
TA in Fall 2019–2020 & Spring 2020–2021. Led labs and recitation sessions.
Semesters: Fall 2020–2021, Fall 2021–2022
TA for Cryptography in Fall 2020–2021 & Fall 2021–2022. Supported coursework and guided students in problem-solving.
Semesters: March 2024, March 2025, March 2026
Tutor for the practical course on privacy-enhancing technologies for medical data at the University of Tübingen, held annually as a March block course since 2024. Guides students through protocol design and implementation.
Semesters: Summer 2023, Summer 2024, Summer 2025, Summer 2026
Tutor for Intro to Cryptography at University of Tübingen. Led exercise sessions and supported students with problem sets.