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| = Master and Bachelor Thesis Supervision = | = Master and Bachelor Thesis Supervision (public landing page) = |
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| This page is targeted at both students and staff. | == Finding a supervisor == Interested students can contact us with the [[https://1drv.ms/w/s!Ahi16FpIWOkRkttoYVVe5krTQqpBTg?e=XRslcE|thesis application form]], a curriculum vitae/resume, and an optional cover letter. Prior knowledge of machine learning is mandatory, e.g., as acquired through the courses offered by our group. Many theses are connected to ongoing research in our group. However, it is also possible for students to suggest own topics/ideas. |
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| == General procedure == After the topic and supervisor have been found (see below), the student must write a proposal with '''preliminary''' results. After Klaus approves the proposal, the student can register the thesis, and the writing period begins (typically three months for BA and six months for MA). This implies that a thesis often requires more time than the official writing period. It might be possible to hand in the thesis early (check with the respective examination regulations for the student’s degree program). However, we encourage significant time between registration and submission. |
There are two complementary ways to find a supervisor: 1. Top-down: write a mail to teachingteam@ml.tu-berlin.de. We will match you with suitable internally advertised topics if possible. 2. Unsolicited: you can contact the researchers below if they work in a research area that suits you. |
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| Though we encourage thesis supervision, there is no obligation to supervise every interested student, e.g., in case no appropriate topic/supervisor is available. | '''Our group tries to supervise as many students as possible, but we typically do not have the capacity to supervise every interested student.''' |
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| == Unsolicited applications == Students can proactively contact researchers if none of the open topics below are suitable. It is possible to suggest your own topics or ideas. In general, being friendly, motivated, and skillful is helpful. Though our group tries to supervise as many students as possible, we do not always have the capacity to supervise every interested student. The senior researchers have a broad overview of current research in our group: |
Researchers: |
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| * Probabilistic/Bayesian models: Shinichi Nakajima (nakajima@tu-berlin.de) | * Probabilistic modeling and inference: Shinichi Nakajima (nakajima@tu-berlin.de) |
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| * ML for Security & Privacy: Daniel Arp (d.arp@tu-berlin.de) * Explainable AI & Natural Language Processing: Oliver Eberle (oliver.eberle@tu-berlin.de) * Learning with multiple modalities: Jannik Wolff (wolff [dot] jannik [at] icloud.com) * Robustness against spurious correlations in DNNs, neural cellular automata: Lorenz Linhardt (l.linhardt@campus.tu-berlin.de) * Combining Explainable AI & Deep Generative Models like Diffusion Models, Large Language Models, GANs, VAEs and Normalizing Flows in different modalities: Sidney Bender (s.bender@tu-berlin.de) * ML for Quantum Chemistry: Jonas Lederer (jonas.lederer@tu-berlin.de) |
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| The junior researchers are typically more specialized: * Jannik Wolff (wolff [dot] jannik [at] icloud.com): Learning with multiple modalities |
You can find further information about our group members on [[https://doc.ml.tu-berlin.de/publications/|our group's publication list]] and the [[https://www.bifold.berlin/research/people|BIFOLD website]]. |
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| == Open topics == * Please delete topics that are not available anymore. * Please include the entry's date. This can help identify outdated topics that should have been deleted. * The last column can entail '''short''' additional information helpful to students to assess their interest in a topic without contacting the person. For example, you may include a short description or a link to a relevant paper. |
=== Publicly advertised open topics === We advertise most topics internally ([[https://wiki.ml.tu-berlin.de/wiki/IDA/ThesisTopicsNewInternal|link to our internal wiki]]) because they are related to ongoing and unpublished research. |
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| || BS || Assessment of data quality in open source wind turbine SCADA data sets || Simon Letzgus (simon.letzgus@tu-berlin.de)|| Jan 2023 || - || || MS || Dynamic visualization of XAI for regression models || Simon Letzgus (simon.letzgus@tu-berlin.de)|| Jan 2023 || - || || MS || Adaptation of generic to subject specific headmodels using ultrasound and photogrammetry || Alexander von Lühmann (vonluehmann@tu-berlin.de) || Feb 2023 || - || || MS || Automatic naturalistic stimulus detection using combined eye-tracking and computer vision || Alexander von Lühmann (vonluehmann@tu-berlin.de) || Feb 2023 || - || || MS || XAI for distribution shift in malware detection || Daniel Arp (d.arp@tu-berlin.de), Lorenz Linhardt (l.linhardt@campus.tu-berlin.de) || Feb 2023 || https://www.overleaf.com/read/hfgvhwsnjpxk || |
|| - || - || -|| - || - || == After having found a supervisor == Students prepare a proposal that includes * the research question and its context/motivation, * related work, * preliminary methodological and/or experimental results, * and formalities such as the number of ECTS credits and the writing time as specified in the student’s examination regulations. The supervisor can help the student with writing the proposal. Students can register the thesis with the examination office after Prof. Müller approves the proposal. We encourage students not to underestimate the time required for writing the proposal. Furthermore, consider that we may require some time to review the proposal. Therefore, it is helpful to contact our group early. |
Master and Bachelor Thesis Supervision (public landing page)
Finding a supervisor
Interested students can contact us with the thesis application form, a curriculum vitae/resume, and an optional cover letter. Prior knowledge of machine learning is mandatory, e.g., as acquired through the courses offered by our group. Many theses are connected to ongoing research in our group. However, it is also possible for students to suggest own topics/ideas.
There are two complementary ways to find a supervisor:
Top-down: write a mail to teachingteam@ml.tu-berlin.de. We will match you with suitable internally advertised topics if possible.
- Unsolicited: you can contact the researchers below if they work in a research area that suits you.
Our group tries to supervise as many students as possible, but we typically do not have the capacity to supervise every interested student.
Researchers:
ML for Quantum Chemistry: Stefan Chmiela (stefan@chmiela.com)
Kernel Learning: Stefan Chmiela (stefan@chmiela.com)
Explainable AI: Grégoire Montavon (gregoire.montavon@tu-berlin.de)
Anomaly detection/Unsupervised learning: Robert Vandermeulen (vandermeulen@tu-berlin.de)
Probabilistic modeling and inference: Shinichi Nakajima (nakajima@tu-berlin.de)
Time series analysis and signal processing: Andreas Ziehe (andreas.ziehe@tu-berlin.de)
Multimodal signal acquisition and analysis - neurotechnology + biomedical sensing: Alexander von Lühmann (vonluehmann@tu-berlin.de)
ML for Security & Privacy: Daniel Arp (d.arp@tu-berlin.de)
Explainable AI & Natural Language Processing: Oliver Eberle (oliver.eberle@tu-berlin.de)
- Learning with multiple modalities: Jannik Wolff (wolff [dot] jannik [at] icloud.com)
Robustness against spurious correlations in DNNs, neural cellular automata: Lorenz Linhardt (l.linhardt@campus.tu-berlin.de)
Combining Explainable AI & Deep Generative Models like Diffusion Models, Large Language Models, GANs, VAEs and Normalizing Flows in different modalities: Sidney Bender (s.bender@tu-berlin.de)
ML for Quantum Chemistry: Jonas Lederer (jonas.lederer@tu-berlin.de)
You can find further information about our group members on our group's publication list and the BIFOLD website.
Publicly advertised open topics
We advertise most topics internally (link to our internal wiki) because they are related to ongoing and unpublished research.
MS/BS |
Topic |
Supervisor + email address |
Date of entry |
Additional information |
- |
- |
- |
- |
- |
After having found a supervisor
Students prepare a proposal that includes
- the research question and its context/motivation,
- related work,
- preliminary methodological and/or experimental results,
- and formalities such as the number of ECTS credits and the writing time as specified in the student’s examination regulations.
The supervisor can help the student with writing the proposal. Students can register the thesis with the examination office after Prof. Müller approves the proposal. We encourage students not to underestimate the time required for writing the proposal. Furthermore, consider that we may require some time to review the proposal. Therefore, it is helpful to contact our group early.