About me
I am an ELLIS Postdoctoral Researcher at the ELLIS Unit Alicante, working on human-centric AI research in the team of Dr. Nuria Oliver. My research focuses on the societal and ethical aspects of generative AI and contributes to building aligned, fair, safe, and secure large language models. Within the ELLIS Postdoc exchange program, I also collaborate with the Machine Learning team, led by Prof. Robert Babuška, at the Czech Institute of Informatics, Robotics, and Cybernetics (CIIRC). My areas of interest comprise human-centric artificial intelligence, large language models, robotics, computer vision, reinforcement learning, and genetic algorithms. I am a member of the ELLIS network.
Education
I hold a Ph.D. degree in Robotics and Machine Learning from the Czech Technical University in Prague. My Ph.D. thesis, defended in 2022, was honored with the Werner von Siemens Award in the Industry 4.0 category and the CTU FEE Dean's Award for a Prestigious Dissertation. Prior to my doctoral studies, I completed the selective B.Sc. and M.Sc. study program Open Informatics at the Faculty of Electrical Engineering, Czech Technical University in Prague, which gave me a solid background in machine learning and mathematics.
International experience
I find it very inspiring to work in an international environment. I have taken the opportunity to go for several study and research stays:
- ELLIS Unit Alicante, Alicante, Spain — Institute of Human-Centered AI (2022, 2 months)
- Carlos III University of Madrid (UC3M), Leganés (Madrid), Spain — Robotics Lab, Escuela Politécnica Superior (2018–2019 & 2021, 10 months in total)
- TU Delft, Delft, The Netherlands — Cognitive Robotics, Faculty of 3mE (2017, 3 months)
- University of Ljubljana, Ljubljana, Slovenia — ViCoS Lab, Faculty of Computer and Information Science (2015, 1 semester)
- Technical University of Denmark (DTU), Lyngby (Copenhagen), Denmark (2013–2014, 1 semester)
Research
Projects
Currently, I am involved in the VIVES project, which forms a part of the PERTE of New Language Economy, and aims at building AI tools supporting the Spanish language as well as the co-official languages. I joined this project in September 2023. Furthermore, I collaborate on developing safe and secure large language models within the Intel Research Center for Responsible Human-AI Systems (RESUMAIS).
Previously, I worked on the Robotics for Industry 4.0 (R4I) project, led by Prof. Robert Babuška. This project ran from June 2017 to June 2023. I contributed mostly to tasks from WP1 focused on data-efficient model learning methods and long-term autonomy of mobile robots.
I have also contributed to the project Symbolic Regression for Reinforcement Learning (SR4RL).
Publications
Derner, E., Batistič, K., Zahálka, J., & Babuška, R. (2024). A Security Risk Taxonomy for Large Language Models. IEEE Access (12), August 2024, 126176–126187.
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Derner, E., Kučera, D., Oliver, N., & Zahálka, J. (2024). Can ChatGPT Read Who You Are? Computers in Human Behavior: Artificial Humans 2 (2), August 2024, 100088.
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Derner, E., Sansalvador de la Fuente, S., Gutiérrez, Y., Moreda, P., & Oliver, N. (2024). Leveraging Large Language Models to Measure Gender Bias in Gendered Languages. arXiv preprint arXiv:2406.13677, June 2024.
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Vastl, M., Kulhánek, J., Kubalík, J., Derner, E., & Babuška, R. (2024). SymFormer: End-to-End Symbolic Regression Using Transformer-Based Architecture. IEEE Access (12), March 2024, 37840–37849.
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Kubalík, J., Derner, E., & Babuška, R. (2023). Toward Physically Plausible Data-Driven Models: A Novel Neural Network Approach to Symbolic Regression. IEEE Access (11), June 2023, 61481–61501.
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Derner, E., & Batistič, K. (2023). Beyond the Safeguards: Exploring the Security Risks of ChatGPT. arXiv preprint arXiv:2305.08005, May 2023.
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Derner, E., & Zahálka, J. (2022). Benefits and Risks of AI Companions. Poster presented at the ELLIS Doctoral Symposium 2022, Alicante, Spain, September 2022.
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Kulhánek, J., Derner, E., Sattler, T., & Babuška, R. (2022). ViewFormer: NeRF-Free Neural Rendering from Few Images Using Transformers. In 17th European Conference on Computer Vision, ECCV 2022, 198–216, Tel Aviv, Israel.
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Derner, E. (2022). Data-Efficient Methods for Model Learning and Control in Robotics. Doctoral Thesis. Czech Technical University in Prague. Defended in May 2022.
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Derner, E., Kubalík, J., & Babuška, R. (2021). Guiding Robot Model Construction with Prior Features. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 7112–7118, Prague, Czech Republic.
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Kubalík, J., Derner, E., & Babuška, R. (2021). Multi-Objective Symbolic Regression for Physics-Aware Dynamic Modeling. Expert Systems with Applications (182), November 2021, 115210.
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Kubalík, J., Derner, E., Žegklitz, J., & Babuška, R. (2021). Symbolic Regression Methods for Reinforcement Learning. IEEE Access (9), October 2021, 139697–139711.
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Kulhánek, J., Derner, E., & Babuška, R. (2021). Visual Navigation in Real-World Indoor Environments Using End-to-End Deep Reinforcement Learning. IEEE Robotics and Automation Letters 6(3), 4345-4352.
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Derner, E., Kubalík, J., & Babuška, R. (2021). Selecting Informative Data Samples for Model Learning Through Symbolic Regression. IEEE Access (9), January 2021, 14148–14158.
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VIDEO
Derner, E., Gómez, C., Hernández, A. C., Barber, R., & Babuška, R. (2021). Change Detection Using Weighted Features for Image-Based Localization. Robotics and Autonomous Systems (135), January 2021, 103676.
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Hernández, A. C., Derner, E., Gómez, C., Barber, R., Babuška, R. (2020). Efficient Object Search Through Probability-Based Viewpoint Selection. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 6172–6179, Las Vegas, NV, USA.
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Gómez, C., Hernández, A. C., Derner, E., Barber, R., & Babuška, R. (2020). Object-Based Pose Graph for Dynamic Indoor Environments. IEEE Robotics and Automation Letters 5(4), 5401–5408.
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Derner, E., Kubalík, J., Ancona, N., & Babuška, R. (2020). Constructing Parsimonious Analytic Models for Dynamic Systems via Symbolic Regression. Applied Soft Computing (94), September 2020, 106432.
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Kubalík, J., Derner, E., & Babuška, R. (2020). Symbolic Regression Driven by Training Data and Prior Knowledge. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '20), 958–966, Association for Computing Machinery, New York, NY, USA.
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Derner, E., Gómez, C., Hernández, A. C., Barber, R., & Babuška, R. (2019). Towards Life-Long Autonomy of Mobile Robots Through Feature-Based Change Detection. In 2019 European Conference on Mobile Robots (ECMR), 1–6, Prague, Czech Republic.
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Kulhánek, J., Derner, E., de Bruin, T., & Babuška, R. (2019). Vision-based Navigation Using Deep Reinforcement Learning. In 2019 European Conference on Mobile Robots (ECMR), 1–8, Prague, Czech Republic.
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Gómez, C., Hernández, A. C., Derner, E., & Barber, R. (2019). Semantic Localization Through Propagation of Scene Information in a Hierarchical Model. In 2019 European Conference on Mobile Robots (ECMR), 1–6, Prague, Czech Republic.
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Hernández, A. C., Gómez, C., Derner, E., Barber, R. (2019). Indoor Scene Recognition Based on Weighted Voting Schemes. In 2019 European Conference on Mobile Robots (ECMR), 1–6, Prague, Czech Republic.
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Derner, E., Kubalík, J., & Babuška, R. (2018). Reinforcement Learning with Symbolic Input-Output Models. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 3004–3009, Madrid, Spain.
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Teaching
Courses
2017, 2021–2022 — lab sessions of the course Dynamics and Control of Networks at the Faculty of Electrical Engineering, Czech Technical University in Prague.
Student works
Feel most welcome to contact me if you are interested in working on topics that fall within the scope of my research. We can identify together a suitable student project, thesis, internship, etc.
Finished works:
- Contextual Bias Detection Through Semantic Analysis Using Large Language Models — Elena Maestre Hernández, internship (2024)
- Automated Output Classification for Evaluating the Safeguards of Large Language Models — Taya Prymak, volunteering project (2024)
- Red-Teaming Large Language Models for Toxicity Evaluation in Low-Resource Languages — Juan José Bayona Reig, internship (2024)
- Automated Evaluation of Biases in Text Using Large Language Models — Sara Sansalvador de la Fuente, internship (2024)
- Co-Evolutionary Approach to Symbolic Regression — Přemysl Pilař, B.Sc. thesis (2023)
- Real-Time Assistance for Visually Impaired Individuals — Ernesto Iván Ochoa Hidalgo, M.Sc. thesis (2023)
- Visual Navigation Using Deep Reinforcement Learning — Jonáš Kulhánek, B.Sc. thesis (2019)
- Life-long Visual Localization of a Mobile Robot in Changing Environments — Kristýna Kumpánová, M.Sc. thesis (2018)
- Using Model Learning Actor-Critic (MLAC) for Reinforcement Learning with Symbolic Regression — Loi Do, summer internship (2017)