Thesis Worker 30 hp - Conformal Prediction for Counterfactual Generation

Job description** The thesis will investigate methods for generating counterfactual explanations with conformal guarantees. The work will include: 1. Implement a baseline anomaly detection framework: Implement or adapt an existing anomaly detection model and establish datasets and evaluation procedures for the thesis. 2. Implement baseline counterfactual methods: Generate counterfactual explanations using one or more established approaches and evaluate their ability to produce valid, proximal, and plausible counterfactuals. 3. Develop conformal counterfactual methods: Investigate how conformal prediction can be incorporated into counterfactual generation or filtering. Explore alternative nonconformity measures and strategies for determining whether generated counterfactuals are supported by the observed data distribution. 4. Evaluate counterfactual quality and conformal guarantees: Systematically evaluate the generated counterfactuals in terms of properties such as validity, proximity, sparsity, plausibility and conformal coverage/validity. Study the trade-offs between producing counterfactuals close to the original observation and counterfactuals that are well supported by representative data. 5. Investigate robustness and generalization: Evaluate the proposed methods across different anomaly detection models, datasets, and/or data distributions to understand when the conformal guarantees and generated explanations remain reliable. The thesis provides an opportunity to work at the intersection of explainable AI, anomaly detection, optimization and uncertainty quantification, while addressing the fundamental question: How can counterfactual explanations be generated that are statistically supported by the data? **Reference** \[1\] Maalej, Aicha, Cecilia Sönströd, and Ulf Johansson. "Counterfactual explanations for conformal prediction sets." Proceedings of Machine Learning Research 266 (2025): 1-20. **Education/program/focus** Assign education, line or direction: Masters programmes in Machine Learning, Data Science, Computer Science, Complex Adaptive Systems or similar. Number of students: 1-2 Start date for the Thesis project: Spring 2027 Estimated timescale: 20 weeks **Contact person and supervisor** Abhishek Srinivasan, Data Scientist, 08-553 816 96, [abhishek.srinivasan@scania.com](mailto:abhishek.srinivasan@scania.com) Juan Carlos Andresen,Group Manager, 08-553 835 16, [juan-carlos.andresen@scania.com](mailto:juan-carlos.andresen@scania.com) **Application** Your application should contain the following: - CV, - personal letter, - and copies of grades. Date of publication, as from – through Until 2026-10-31. Applicants will be assessed on a continuous basis until the position is filled. **A background check might be conducted for this position. We are conducting interviews continuously and may close the recruitment earlier than the date specified.** Requisition ID: 33386 Number of Openings: 1.0 Part-time / Full-time: Full-time Permanent / Temporary: Temporary Country / Region: SE Location(s): Södertälje, SE, 151 38 Required Travel: 0% Workplace: On-site [Apply now »](https://jobs.scania.com/talentcommunity/apply/1442715133/?locale=en_US)