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| = Master and Bachelor Thesis Supervision = | #acl IdaGroup:read,write,delete,revert All:read |
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| == General information == 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. This implies that a thesis often requires more time than the official writing period (typically three months for BA and six months for MA). 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. |
= Master and Bachelor Thesis Supervision (public landing page) = |
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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. | == Finding a supervisor == Interested students can contact a group member listed below with a curriculum vitae/resume and an optional cover letter. Among other relevant aspects, these documents should inform about the grades in relevant modules. Prior knowledge of machine learning is mandatory, e.g., as acquired through the courses offered by our group. Students should also describe their research interests/ideas, including '''suitable and proven''' skills, e.g., in Python programming, mathematics, or scientific writing. 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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| == Senior researchers == Our senior researchers often have a good overview of the research interests in our group. It can be helpful to contact one of the researchers below if you cannot supervise an interested student yourself and none of the open topics below are appropriate. |
'''Though our group tries to supervise as many students as possible, we typically do not have the capacity to supervise every interested student.''' |
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| Our senior researchers can identify suitable internally advertised topics or suggest suitable supervisors within our group: | |
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| * Explainable AI: Pan Kessel (pan.kessel@gmail.com) | * Explainable AI: Grégoire Montavon (gregoire.montavon@tu-berlin.de) |
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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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| * 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) |
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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 additional information helpful to students to assess their interest in a topic without contacting the person. For example, you may include a short abstract or links to relevant papers. |
Alternatively, it is possible to contact junior researchers directly with '''specific''' ideas or inquiries: * 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 (s.bender@tu-berlin.de) |
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| || '''MS/BS''' || '''Topic''' || '''Supervisor + email address'' || '''Date of entry''' || '''Additional information'' || || BS || Assessment of data quality in open source wind turbine SCADA data sets || Simon Letzgus (simon.letzgus@tu-berlin.de)|| Jan 2023 || - || |
You can find further information about our group members, including their research, on [[https://doc.ml.tu-berlin.de/publications/|our group's publication list]] and the [[https://www.bifold.berlin/research/people|BIFOLD website]]. === Open topics === '''Our [[https://wiki.ml.tu-berlin.de/wiki/IDA/ThesisTopicsNewInternal|internal wiki]] contains additional open topics. The group members listed above can match interested students with internally advertised topics.''' || '''MS/BS''' || '''Topic''' || '''Supervisor + email address''' || '''Date of entry''' || '''Additional information'' || || - || - || -|| - || - || == General procedure == 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 a group member listed below with a curriculum vitae/resume and an optional cover letter. Among other relevant aspects, these documents should inform about the grades in relevant modules. Prior knowledge of machine learning is mandatory, e.g., as acquired through the courses offered by our group. Students should also describe their research interests/ideas, including suitable and proven skills, e.g., in Python programming, mathematics, or scientific writing. Many theses are connected to ongoing research in our group. However, it is also possible for students to suggest own topics/ideas.
Though our group tries to supervise as many students as possible, we typically do not have the capacity to supervise every interested student.
Our senior researchers can identify suitable internally advertised topics or suggest suitable supervisors within our group:
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)
Alternatively, it is possible to contact junior researchers directly with specific ideas or inquiries:
- 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 (s.bender@tu-berlin.de)
You can find further information about our group members, including their research, on our group's publication list and the BIFOLD website.
Open topics
Our internal wiki contains additional open topics. The group members listed above can match interested students with internally advertised topics.
MS/BS |
Topic |
Supervisor + email address |
Date of entry |
Additional information |
- |
- |
- |
- |
- |
General procedure
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.