CAIN 2024
Sun 14 - Mon 15 April 2024 Lisbon, Portugal
co-located with ICSE 2024

Context: The rise of Artificial Intelligence (AI) and Machine Learning (ML) applied in many software-intensive products and services introduces new opportunities but also new security challenges. Motivation: AI and ML will gain even more attention from industry in the future, but threats caused by already discovered attacks specifically targeting ML models are either overseen, ignored, or mishandled. Problem Statement: Current Software Engineering security practices and tools are insufficient to detect and mitigate ML Threats systematically. Contribution: We will develop and evaluate a threat modeling technique for non-security experts assessing ML-intensive systems in close collaboration with industry and academia.

Mon 15 Apr

Displayed time zone: Lisbon change

11:00 - 12:30
Doctoral Symposium and Energy-Aware AI EngineeringDoctoral Symposium / Research and Experience Papers at Pequeno Auditório
Chair(s): Justus Bogner Vrije Universiteit Amsterdam, Silverio Martínez-Fernández UPC-BarcelonaTech
11:00
6m
Talk
Software Design Decisions for Greener Machine Learning-based Systems
Doctoral Symposium
Santiago del Rey Universitat Politècnica de Catalunya (UPC)
11:06
6m
Talk
Energy-Efficient Development of ML-Enabled Systems: A Data-Centric Approach
Doctoral Symposium
11:12
6m
Talk
Optimizing Data Analytics Workflows through User-driven Experimentation
Doctoral Symposium
Keerthiga Rajenthiram Vrije Universiteit Amsterdam
11:18
6m
Talk
Component-based Approach to Software Engineering of Machine Learning-enabled Systems
Doctoral Symposium
Vladislav Indykov Chalmers | University of Gothenburg
11:24
6m
Talk
Threat Modeling of ML-intensive Systems: Research Proposal
Doctoral Symposium
Felix Viktor Jedrzejewski Blekinge Institute of Technology
11:30
6m
Talk
Continuous Quality Assurance ML Pipelines under the AI Act
Doctoral Symposium
Matthias Wagner Lund University
11:36
10m
Talk
Green Runner: A tool for efficient deep learning component selection
Research and Experience Papers
Jai Kannan Applied Artificial Intelligence Institute, Deakin University, Scott Barnett Applied Artificial Intelligence Institute, Deakin University, Anj Simmons , Taylan Selvi Applied Artificial Intelligence Institute, Deakin University, Luís Cruz Delft University of Technology
11:46
15m
Talk
Engineering Carbon Emission-aware Machine Learning Pipelines
Research and Experience Papers
Erik Johannes Husom SINTEF Digital, Sagar Sen , Arda Goknil SINTEF Digital
12:01
10m
Talk
Identifying architectural design decisions for achieving green ML serving
Research and Experience Papers
Francisco Durán Universitat Politècnica De Catalunya - Barcelona Tech, Silverio Martínez-Fernández UPC-BarcelonaTech, Matias Martinez Universitat Politècnica de Catalunya (UPC), Patricia Lago Vrije Universiteit Amsterdam
Pre-print
12:11
10m
Talk
Green AI: a Preliminary Empirical Study on Energy Consumption in DL Models Across Different Runtime Infrastructures
Research and Experience Papers
Negar Alizadeh Universiteit Utrecht, Fernando Castor University of Twente and Federal University of Pernambuco
12:21
9m
Live Q&A
Energy: Q&A Session
Research and Experience Papers