Dr. Mareike Picklum
Tel: | –49 -421 218 64010 | ||
Fax: | –49 -421 218 64047 | ||
Room: | 1.77 | ||
Mail: | mareikep(at)cs[dot]uni-bremen[dot]de | ||
About
I am a postdoctoral researcher at the IAI, specializing in the development of probabilistic models for various applicatoins. I earned my PhD (Dr.rer.nat.) from the University of Bremen in May 2024. Before, I was a PhD student in the IAI group, supervised by Prof. Michael Beetz.
I studied Computer Science at the University of Bremen (UoB) and the University of New South Wales (UNSW) and received my Master's degree in 2015. I wrote my Master's thesis under the supervision of Prof. Michael Beetz at the Institute for Artificial Intelligence at the UoB addressing the problem of recognizing objects based on natural-language descriptions using graphical probabilistic models.
I am a contributor in the projects pracmln and PRAC. In particular, I developed the publicly available web services PRACWeb, inviting users to have a look at the PRAC system in operation and WebMLN allowing users to try out inference and learning algorithms of the pracmln system.
From 2017-2019 I was part of the initiative 'Farbige Zustände' (CRC 1232) which aims at the development of a novel experimental method for the development of materials. Within this collaborative research centre, I worked on a prototype and feasibility study for an intelligent cognitive software assistant that supports the work of material scientists in designing new materials.
My PhD thesis is centered around the development of probabilistic models to anticipate the outcomes of robotic agents performing actions in various contexts.
Teaching
- AI: Knowledge Acquisition and Representation (WS2017/18) (Tutorial/Co-Lecturer)
- Foundations of Artificial Intelligence (SS2017) (Tutorial)
- AI: Knowledge Acquisition and Representation (WS2016/17) (Tutorial)
- Foundations of Artificial Intelligence (SS2016) (Tutorial)
- AI: Knowledge Acquisition and Representation (WS2015/16) (Tutorial)
- Foundations of Artificial Intelligence (SS2015) (Tutorial)
Supervised Theses
- Crowdsourcing Instruction Data for Statistical Relational Learning (Bachelor's Thesis, Kevin Scheck)
Publications
[1] | Mareike Picklum and Michael Beetz, "MatCALO: Knowledge-enabled machine learning in materials science", In Computational Materials Science, vol. 163, pp. 50 - 62, 2019. |
[2] | Daniel Nyga, Mareike Picklum and Michael Beetz, "What No Robot Has Seen Before – Probabilistic Interpretation of Natural-language Object Descriptions", In International Conference on Robotics and Automation (ICRA), Singapore, 2017. |
[3] | Daniel Nyga, Mareike Picklum, Sebastian Koralewski and Michael Beetz, "Instruction Completion through Instance-based Learning and Semantic Analogical Reasoning", In International Conference on Robotics and Automation (ICRA), Singapore, 2017. |
[4] | Pomarlan, Mihai, Nyga, Daniel, Picklum, Mareike, Koralewski, Sebastian and Beetz, Michael, "Deeper Understanding of Vague Instructions through Simulated Execution (Extended Abstract)", In Proceedings of the 2017 International Conference on Autonomous Agents & Multiagent Systems, International Foundation for Autonomous Agents and Multiagent Systems, 2017. |
[5] | Daniel Nyga, Mareike Picklum, Tom Schierenbeck and Michael Beetz, "Joint Probability Trees", In Arxiv.org, 2023. Preprint |
Prof. Dr. hc. Michael Beetz PhD
Head of Institute
Contact via
Andrea Cowley
assistant to Prof. Beetz
ai-office@cs.uni-bremen.de
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