My CV is available here.

Education

• DPhil in Computer Science, University of Oxford (2021-present)
• Master of Informatics with Honours, First Class, The University of Edinburgh (2014-2019)

Scholarships and Grants

• EPSRC Scholarship for doctoral studies (October 2021 - March 2025)
• G-Research Women in Quant Finance Grant (February 2025)
• St Hilda’s College Travel for Research Grant (May 2023, February 2024)
• IJCAI-AIJ 2024 Travel Grant Program (2024)

Experience

• Research Intern at FiveAI, Oxford, UK (2020 - 2021)
  • Topic: Detecting Reflective Symmetries in 3D Models, Computer Vision
  • Paper: Recurrently Estimating Reflective Symmetry Planes from Partial Pointclouds (CVPR 2021 Workshop on 3D Vision and Robotics)
• Research Assistant at The University of Edinburgh, UK (2019)
  • Topic: Speech-to-Text Machine Translation
  • Paper: Analyzing ASR pretraining for low-resource speech-to-text translation (in Proc. of ICASSP 2020).
• Student Summer Research Fellow at ETH Zurich, Switzerland (2018)
  • Topic: Program Behaviour Synthesis
  • Supervisors: Professor Martin Vechev, Assistant Professor Dana Drachsler Cohen

Recent Talks

• Dagstuhl Seminar on Logic and Neural Networks (February 2025)
• IJCAI Doctoral Consortium (August 2024)
• Oxford Computer Science Conference (June 2024)
• University of Luxembourg (June 2024)
• Sony AI (April 2024)

Teaching

• Class Tutor at The University of Edinburgh, UK (2017 - 2019)
  • Processing Formal and Natural Languages
  • Discrete Mathematics
  • Algorithms, Data Structures and Learning

Publications

Beyond the Convexity Assumption: Realistic Tabular Data Generation under Quantifier-Free Real Linear Constraints

Mihaela C. Stoian and Eleonora Giunchiglia. Beyond the Convexity Assumption: Realistic Tabular Data Generation under Quantifier-Free Real Linear Constraints. In Proceedings of International Conference on Learning Representations (ICLR) 2025.

A Survey on Tabular Data Generation: Utility, Alignment, Fidelity, Privacy, and Beyond

Mihaela C. Stoian, Eleonora Giunchiglia, Thomas Lukasiewicz. A Survey on Tabular Data Generation: Utility, Alignment, Fidelity, Privacy, and Beyond. arXiv preprint 2503.05954, 2025

How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Data

Mihaela C. Stoian, Salijona Dyrmishi, Maxime Cordy, Thomas Lukasiewicz, Eleonora Giunchiglia. How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Data. In Proceedings of International Conference on Learning Representations (ICLR) 2024.

PiShield: A PyTorch Package for Learning with Requirements

Mihaela C. Stoian, Alex Tatomir, Thomas Lukasiewicz, Eleonora Giunchiglia. PiShield: A PyTorch Package for Learning with Requirements. In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), 2024.

Deep generative models as an adversarial attack strategy for tabular machine learning

Salijona Dyrmishi, Mihaela C. Stoian, Eleonora Giunchiglia, Maxime Cordy. Deep generative models as an adversarial attack strategy for tabular machine learning. In Proceedings of International Conference on Machine Learning and Cybernetics 2024.

CCN+: A neuro-symbolic framework for deep learning with requirements

Eleonora Giunchiglia‚ Alex Tatomir‚ Mihaela C. Stoian, Thomas Lukasiewicz. CCN+: A neuro-symbolic framework for deep learning with requirements. International Journal of Approximate Reasoning, 171, 109-124 (2024).

Deep Learning with Requirements in the Real World

Mihaela C. Stoian. Deep Learning with Requirements in the Real World. In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), 2024, Doctoral Consortium.

ROAD-Waymo: Action Awareness at Scale for Autonomous Driving

Salman Khan, Izzeddin Teeti, Reza Javanmard Alitappeh, Mihaela C. Stoian, Eleonora Giunchiglia, Gurkirt Singh, Andrew Bradley, Fabio Cuzzolin. ROAD-Waymo: Action Awareness at Scale for Autonomous Driving. arXiv preprint 2411.01683, 2024

Exploiting T-norms for Deep Learning in Autonomous Driving

Mihaela C. Stoian, Eleonora Giunchiglia, Thomas Lukasiewicz. Exploiting T-norms for Deep Learning in Autonomous Driving. In Proceedings of the 17th International Workshop on Neural-Symbolic Learning and Reasoning (NeSy), 2023.

ROAD-R: The Autonomous Driving Dataset with Logical Requirements

Eleonora Giunchiglia, Mihaela C. Stoian, Salman Khan, Fabio Cuzzolin, Thomas Lukasiewicz. ROAD-R: The Autonomous Driving Dataset with Logical Requirements. Machine Learning, 112, 3261–3291 (2023).

Deep Learning with Logical Constraints

Eleonora Giunchiglia, Mihaela C. Stoian, Thomas Lukasiewicz. Deep Learning with Logical Constraints. In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), 2022.

Recurrently Estimating Reflective Symmetry Planes from Partial Pointclouds

Mihaela C. Stoian, Tommaso Cavallari. Recurrently Estimating Reflective Symmetry Planes from Partial Pointclouds. In Conference on Computer Vision and Pattern Recognition (CVPR) Workshop on 3D Vision and Robotics, 2021.

Analyzing ASR pretraining for low-resource speech-to-text translation

Mihaela C. Stoian, Sameer Bansal, Sharon Goldwater. Analyzing ASR pretraining for low-resource speech-to-text translation. In Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2020.