For patents please see my Google scholar profile

Papers:

2024

  1. On Calibration of Object Detectors: Pitfalls, Evaluation and Baselines Kuzucu, Selim, Oksuz, Kemal, Sadeghi, Jonathan, and Dokania, Puneet K. ECCV 2024 [Download] [Code]

2023

  1. Attacking Motion Planners Using Adversarial Perception Errors Sadeghi, Jonathan, Lord, Nicholas A., Redford, John, and Mueller, Romain 2023 [Download]

2022

  1. An Active Learning Reliability Method for Systems with Partially Defined Performance Functions Sadeghi, Jonathan, Mueller, Romain, and Redford, John NeurIPS 2022 Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems (GPSMDMS) 2022 [Download] [Code] [Video]
  2. Query-based Hard-Image Retrieval for Object Detection at Test Time Ayers, Edward, Sadeghi, Jonathan, Redford, John, Mueller, Romain, and Dokania, Puneet K. Thirty-Seventh AAAI Conference on Artificial Intelligence 2022 [Download] [Code]
  3. Perspectives on the System-level Design of a Safe Autonomous Driving Stack Hawasly, Majd, Sadeghi, Jonathan, Antonello, Morris, Albrecht, Stefano V., Redford, John, and Ramamoorthy, Subramanian AI Communications 2022 [Download]

2021

  1. A Step Towards Efficient Evaluation of Complex Perception Tasks in Simulation Sadeghi, Jonathan, Rogers, Blaine, Gunn, James, Saunders, Thomas, Samangooei, Sina, Dokania, Puneet Kumar, and Redford, John NeurIPS 2021 Workshop on Machine Learning for Autonomous Driving (ML4AD) 2021 [Download] [Video]

2019

  1. Robust propagation of probability boxes by Interval Predictor Models Sadeghi, Jonathan, de Angelis, Marco, and Patelli, Edoardo Structural Safety 2019 [Download]
  2. Efficient Training of Interval Neural Networks for Imprecise Training Data Sadeghi, Jonathan, de Angelis, Marco, and Patelli, Edoardo Neural Networks 2019 [Download] [Code]
  3. Analytic Probabilistic Safety Analysis Under Severe Uncertainty Sadeghi, Jonathan, de Angelis, Marco, and Patelli, Edoardo ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 2019 [Download]
  4. On the robust estimation of small failure probabilities for strong non-linear models Faes, Matthias, Sadeghi, Jonathan, Broggi, Matteo, de Angelis, Marco, Patelli, Edoardo, Beer, Michael, and Moens, David ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B: Mechanical Engineering 2019 [Download]

2016

  1. Structural reliability of pre-stressed concrete containments Prinja, Nawal K., Ogunbadejo, Azeezat, Sadeghi, Jonathan, and Patelli, Edoardo Nuclear Engineering and Design 2016 [Download]

Thesis:

2020

  1. Uncertainty Modelling for Scarce and Imprecise Data in Engineering Applications Sadeghi, Jonathan University of Liverpool 2020 [Download]

Technical Reports:

2019

  1. Data Study Group Final Report: Global bank Data Study Group team, Alan Turing Institute 2019 [Download]

Conference Papers/Talks:

2018

  1. Robust propagation of probability boxes by Interval Predictor Models Sadeghi, Jonathan, de Angelis, Marco, and Patelli, Edoardo In Proceedings of the joint ASCE ICVRAM ISUMA UNCERTAINTIES conference 2018
  2. OpenCossan 2.0: an efficient computational toolbox for risk, reliability and resilience analysis Patelli, Edoardo, Tolo, Silvia, George-Williams, Hindolo, Sadeghi, Jonathan, Rocchetta, Roberto, de Angelis, Marco, and Broggi, Matteo In Proceedings of the joint ASCE ICVRAM ISUMA UNCERTAINTIES conference 2018 [Download]
  3. Probability Box Propagation: Benchmarking Challenge Problems Patelli, Edoardo, Sadeghi, Jonathan, de Angelis, Marco, Fetz, Thomas, and Oberguggenberger, Micheal In 19th working conference of the IFIP Working Group 7.5 on Reliability and Optimization of Structural Systems 2018

2017

  1. History Matching with Robust Predictive Metamodels Sadeghi, Jonathan, and Patelli, Edoardo In 12th International Conference on Structural Safety & Reliability 2017
  2. Cossan Software: A Multidisciplinary And Collaborative Software For Uncertainty Quantification Patelli, Edoardo, Broggi, Matteo, Tolo, Silvia, and Sadeghi, Jonathan In 2nd ECCOMAS Thematic Conference on Uncertainty Quantification in Computational Sciences and Engineering 2017 [Download]