Seeking a challenging role in Privacy-Preserving Applications of Deep Learning, where I can leverage my expertise in bridging cryptography to develop practical solutions. Committed to simplifying the application of Secure Multiparty Computation and Homomorphic Encryption to Deep Learning for end-users. Additionally, adept at exploring and implementing cutting-edge technologies such as Kubernetes, Docker, and HPC through my Home Lab research.
Privacy-Preserving Machine Learning Researcher
CERN, CERN openlabGeneva, SwitzerlandNov. 2019 - Oct. 2022
Geneva, Switzerland- Developed an innovative homomorphic encryption compiler using symbolic execution techniques that achieved a remarkable 80% reduction in code lines required for privacy-preserving Deep Learning Inference.
- Integrated expert systems, leveraging fuzzy logic and linear programming techniques to automatically generate optimized parameters for homomorphic encryption.
- Designed novel algorithms specifically tailored for convolutional neural networks with packed homomorphic encryption that achieved a 2x performance improvement.
- Took a main role in scientific writing and supervision and guidance to other students and interns.
Homomorphic Encryption
Deep Learning
Symbolic Execution
Research Management
Embedded Systems Software Intern
CERN, Detector Technologies DepartmentGeneva, SwitzerlandJun. 2018 - Sep. 2018
Geneva, Switzerland- Developed a minimal platform for data extraction, database storage, and result visualization on an Arduino Yun, achieving a minimal memory footprint of 64 MB.
Embedded Systems
Arduino
Data Visualization
Compiler Development Intern
ARCOS group, Universidad Carlos III de MadridMadrid, SpainSep. 2017 - Jun. 2018
Madrid, Spain- Integrated contract-based programming into Clang compiler, enabling enforcement of preconditions, postconditions, and assertions in C++ code, resulting in up to 40% performance improvement.
C++
Clang
Compiler Development