The Alan Turing Institute
British Library, 96 Euston Road, London
Social distancing somewhere in London
- Information leakage estimation for security&privacy
- Theory, foundations, and privacy-security-fairness properties of Machine Learning
- Methods for distribution-free confident prediction in supervised learning and anomaly detection (e.g., Conformal Predictors)
Have a look at a list of recent projects.
I co-founded and play with the CTF team TU6PM.
I am a (happy) OpenBSD user.
|May 14, 2021||Our paper “Exact Optimization of Conformal Predictors via Incremental and Decremental Learning has been accepted for presentation&publication at ICML ‘21. This work has also been accepted as a spotlight talk at the DFUQ ‘21 ICML workshop.|
|May 11, 2020||From October 2020, I will join the Turing Institute as a Research Fellow in Safe & Ethical AI.|
|Jan 13, 2020||I will be co-chairing this year’s symposium on Conformal and Probabilistic Prediction with Applications (COPA2020). Please consider submitting your works.|
|Jul 25, 2019||My PhD thesis is now available online. Highlights here.|
|Feb 28, 2019||Our paper, “F-BLEAU: Fast Black-box Leakage Estimation”, has been accepted by the IEEE Symposium on Security and Privacy, 2019. It shows how to use ML methods for measuring the information leakage of a black-box system in a practical yet theoretically sound manner.|
|Dec 16, 2018||The code of fbleau for measuring the leakage of black box systems is now online and available for installation via crates.io.|
|Nov 6, 2018||A list of semester projects for EPFL MSc/PhD students is available at https://spring.epfl.ch/en/projects.|
Some recent projects.
Exact Optimization of Conformal Predictors via Incremental and Decremental Learning to appear: ICML ’21 2021
Disparate vulnerability: On the unfairness of privacy attacks against machine learning arXiv preprint arXiv:1906.00389 2020
Black-box Security: Measuring Black-box Information Leakage via Machine Learning PhD thesis 2019
F-BLEAU: Fast Black-box Leakage Estimation In IEEE Symposium on Security and Privacy (S&P) 2019
Exchangeability martingales for selecting features in anomaly detection In Proceedings of the Seventh Workshop on Conformal and Probabilistic Prediction and Applications 2018
Majority vote ensembles of conformal predictors Machine Learning 2018
Website Fingerprinting Defenses at the Application Layer Proceedings on Privacy Enhancing Technologies 2017
Bayes, not Naïve: Security Bounds on Website Fingerprinting Defenses Proceedings on Privacy Enhancing Technologies 2017 Best student paper
Conformal Clustering and Its Application to Botnet Traffic In Statistical Learning and Data Sciences (SLDS) 2015 Best student paper
Bots detection by Conformal Clustering MSc thesis, Royal Holloway University of London 2014
Selected invited talks.
Measuring the Security of Machine Learning models 2019 Third ITU/WHO Workshop on "Artificial Intelligence for Health"
Measuring the Leakage of a Black-box using Machine Learning 2018 Alan Turing Institute, London
Bayes, not Naïve: Provable Security of Website Fingerprinting Defenses 2017 ISG Seminar, Royal Holloway University of London, UK
Bayes, not Naïve: Security Bounds on Website Fingerprinting Defenses 2017 Security Seminar, University of Cambridge
On the Security Against Machine Learning-based Attacks 2017 CDT Showcase, Evelyn Sharp Centre, Sunningdale Park
Applications of Conformal Prediction in Information Security Problems 2016 CDT Showcase, Windsor Great Park, UK
Conformal Clustering and Bots Traffic 2015 CPRML Workshop 2015, Hyderabad, India
Research Engineer, HP Labs Security Lab, Bristol (August-November 2017)
Supervisors: Jonathan Griffin, Adrian Baldwin
Research Visitor, École Polytechnique, Paris (May; November 2017)
Supervisors: Prof. Catuscia Palamidessi, Kostas Chatzikokolakis
Research Intern, Cornell Tech (June-September 2016)
Supervisor: Prof. Thomas Ristenpart
PC chair of the annual conference on conformal prediction, COPA 2020, COPA 2021. PC member: IEEE S&P 2022, ACM CCS 2021, IEEE Euro S&P 2021-22, PETS (2019-2021), COPA 2018, and I have been reviewer for ML&security conferences and journals (e.g., Neurocomputing, PETS, Financial Cryptography).
I was teaching assistant for the courses: Machine Learning and Data Analysis at Royal Holloway University of London (2014-17). I was teaching assistant for the courses on C programming and Linear Algebra and Geometry at University of Pavia (2011-12).
- 2017, Best Paper: Andreas Pfitzmann Best Student Paper Award at PETS: “Bayes, not Naïve: Security Bounds on Website Fingerprinting Defenses”
- 2017, First place at Capture The Flag (CTF) security challenge organised by NCC Group at the Cambridge2Cambridge event
- 2015, Best Paper: Best student paper award sponsored by HP at SLDS conference: “Conformal Clustering and Its Application to Botnet Traffic”
- 2014, Best Finalist: Best MSc in Big Data finalist in memory of Prof. Alexey Chervonenkis (Royal Holloway University of London)
I am a Research Fellow on Safe&Ethical AI at the Turing Institute in London. Before I was a postdoctoral fellow at EPFL (Switzerland) with an EcoCloud grant, collaborating with Carmela Troncoso at the SPRING lab and Martin Jaggi at the MLO lab. I have a PhD in Machine Learning and Information Security from Royal Holloway University of London with the Centre of Doctoral Training (CDT), where I was supervised by Alex Gammerman and advised by Kenny Paterson. I received an MSc in Machine Learning from Royal Holloway University of London in 2014, and a BSc in Mechatronics and Computer Engineering from University of Pavia in 2013.
My current research aims at measuring systems’ leakage by using methods from the Machine Learning theory; I applied this to side channel attacks (e.g., traffic analysis). I also work on distribution-free learning methods (e.g., Conformal Predictors), particularly in the context of clustering, anomaly detection, and classifiers ensembling.