We are inviting applications from undergraduate and graduate students of Cybersecurity and related fields.
During our annual scientific event, students will have the opportunity to follow one week of scientific talks and workshops, present their own work during poster sessions and discuss relevant topics in trustworthy AI with fellow researchers and expert speakers. The program will be complemented by social activities.
Application Process: Please apply by filling in our application form and uploading your CV and a short Motivation Letter.
Application Deadline: (CEST) August 21, 2022.
Notification of Acceptance: by August 24, 2022 at the latest
Fee: 200,-€ (includes full program, food and beverages during the week and social activities)
Travel Grant: We are offering travel grants. With your application, you can apply for a grant of up to 500,-€ per person (actual travel cost spent, economy flight, train ticket 2nd class). Please do not book any travel arrangements before you have been selected by our jury and accepted to our Summer School. After acceptance, please send your travel arrangements to email@example.com for approval prior to booking anything. We will confirm if/that your expenses will be covered and you will be reimbursed after completing the Summer School successfully.
Presentations: There will be two sessions, during which participants can present their own work / scientific poster. The presentation is not mandatory to receive a certificate of attendance, but we are highly encouraging you to contribute your work to this session and as it will provide you with valuable feedback from an expert audience and might kindle interesting discussions.
Antti Honkela, University of Helsinki
Until December 2018, he was an Assistant Professor of Statistics at the Department of Mathematics and Statistics and the Department of Public Health, University of Helsinki. Before that, he was an Academy Research Fellow at the Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki.
His research interests include Bayesian machine learning and probabilistic modelling, privacy-preserving machine learning and differential privacy as well as computational systems biology. He is interested in modelling and analysis of quantitative genomic sequencing data and especially genomic time series data.
Deep learning with differential privacy
Differential privacy and Bayesian inference
Catuscia Palamidessi, INRIA
Short bio: Catuscia Palamidessi is Director of Research at INRIA Saclay (since 2002), where she leads the team COMETE. She has been Full Professor at the University of Genova, Italy (1994-1997) and at Penn State University, USA (1998-2002). Palamidessi's research interests include Privacy, Machine Learning, Fairness, Secure Information Flow, Formal Methods, and Concurrency. In 2019 she has obtained an ERC advanced grant to conduct research on Privacy and Machine Learning. She has been PC chair of various conferences including LICS and ICALP, and PC member of more than 120 international conferences. She is in the Editorial board of several journals, including the IEEE Transactions in Dependable and Secure Computing, Mathematical Structures in Computer Science, Theoretics, the Journal of Logical and Algebraic Methods in Programming and Acta Informatica. She is president of ACM SIGLOG and member of the steering committees of CONCUR and CSL.
Fairness notions in ML
Martin Jaggi, EPFL
Short bio: Martin Jaggi is a Tenure Track Assistant Professor at EPFL, heading the Machine Learning and Optimization Laboratory. Before that, he was a post-doctoral researcher at ETH Zurich, at the Simons Institute in Berkeley, and at École Polytechnique in Paris. He has earned his PhD in Machine Learning and Optimization from ETH Zurich in 2011, and a MSc in Mathematics also from ETH Zurich.
Training with adversaries, from Federated Learning to decentralized
Battista Biggio, PRA Lab
Short bio: Battista Biggio received the MSc degree in Electronic Engineering, with honors, and the PhD in Electronic Engineering and Computer Science, respectively in 2006 and 2010, from the University of Cagliari (Italy). Since 2007 he has been working for the Department of Electrical and Electronic Engineering of the same University, where he currently is an Assistant Professor. From May 12th, 2011 to November 12th, 2011, he visited the University of Tuebingen (Germany), and worked on the security of machine learning algorithms to contamination of training data.
His research interests currently include: secure / robust machine learning and pattern recognition methods (adversarial learning); multiple classifier systems; and kernel methods; with applications in biometric recognition, spam filtering, malware detection, and intrusion detection in computer networks.
Machine Learning Security: Adversarial Attacks and Defenses (Session I and II)
Emiliano De Cristofaro, UCL
Short bio: He is a (Full) Professor of Security and Privacy Enhancing Technologies at University College London (UCL). He is affiliated with the Computer Science Department and currently serves as Head of the Information Security Research Group and Director of the Academic Center of Excellence in Cyber Security Research (ACE-CSR). Before joining UCL in 2013, he was a Research Scientist at Xerox PARC.
He is also a Faculty Fellow at the Alan Turing Institute and on the Technology Advisory Panel at the UK Information Commissioner’s Office (ICO). In 2016, he co-founded the International Data-driven Research for Advanced Modeling and Analysis Lab (iDRAMA Lab).
What is privacy in ML? Defining attacks and case studies (e.g., FL)
Privacy-preserving Synthetic data: the good, the bad, the ugly
Short bio: Mario Fritz is a faculty member at the CISPA Helmholtz Center for
Information Security, an honorary professor at Saarland University, and a
fellow of the European Laboratory for Learning and Intelligent Systems
(ELLIS). Until 2018, he led a research group at the Max Planck Institute for
Computer Science. Previously, he was a PostDoc at the International
Computer Science Institute (ICSI) and UC Berkeley after receiving his PhD
from TU Darmstadt and studying computer science at FAU Erlangen-
Nuremberg. His research focuses on trustworthy artificial intelligence,
especially at the intersection of information security and machine learning.
He is Associate Editor of the journal "IEEE Transactions on Pattern Analysis
and Machine Intelligence (TPAMI)," coordinates the Helmholtz project
"Trustworthy Federated Data Analytics," and has published over 100 scientific
articles - 80 of them in top conferences and journals.
Session: Welcome and Introduction
Short bio: Sebastian Stich is a faculty at the CISPA Helmholtz center for
information security. He received his PhD in computer science from ETH
Zurich in 2014, did a postdoc with Prof. Yurii Nesterov at UCLouvain until 2016
and was working as research scientist at EPFL until 2021. His research focuses
on efficient training algorithms, in particular distributed and parallel training
of ML models over decentralized datasets. He is co-organizer of the
“Optimization for Machine Learning” workshop (opt-ml.org) at NeurIPS. Since
2020 he is a member of the European Lab for Learning and Intelligent
Introduction to Federated Learning and an Overview of Federated
Short bio: Prof. Thorsten Holz is a faculty member at the CISPA Helmholtz
Center for Information Security. Before joining CISPA in October 2021, he was
a full professor at the Faculty of Electrical Engineering and Information
Technology at Ruhr University Bochum, Germany. His research interests
include technical aspects of secure systems, with a specific focus on systems
security. Thorsten received a Dipl.-Inform. degree in Computer Science from
RWTH Aachen University (2005) and the Ph.D. degree from the University of
Mannheim (2009). In 2011, he was awarded the Heinz Maier-Leibnitz Prize
from the German Research Foundation (DFG) and in 2014 an ERC Starting
Grant. He was also co-spokesperson of the Cluster of Excellence "CASA - Cyber
Security in the Age of Large-Scale Adversaries" (with C. Paar and E. Kiltz) from
2019 to 2021. In recent years, he has served on many program committees and
has co-chaired the Program Committee (PC) for two of the leading computer
security conferences, namely the IEEE Symposium on Security and Privacy
(2021, 2022) and the USENIX Security Symposium (2016).
Short bio: Jilles Vreeken is tenured faculty at the Helmholtz Center on
Information Security, where he leads the Exploratory Data Analysis group. In
addition, he is both a Senior Researcher at the Max Planck Institute for
Informatics, as well as Honorary Professor at Saarland University.
His research interests include data mining, machine learning, and causal
inference. He is particularly interested in developing well-founded theory and
efficient methods for extracting informative causal models and patterns from
large data, and putting these to good use. He has authored 3 book chapters
and over 105 conference and journal papers. He received three best paper
awards, the ACM SIGKDD 2010 Doctoral Dissertation Runner-Up Award, and
the IEEE ICDM 2018 Tao Li Award.
More information on topics, sessions and a detailed schedule will be published during the course of July.
For any questions or support, please send an e-mail to firstname.lastname@example.org.