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= Master and Bachelor Thesis Supervision = = Master and Bachelor Thesis Supervision (public landing page) =
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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 (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.		 == Finding a supervisor ==
Interested students can contact a group member listed below with our [[https://www.dropbox.com/s/qcx71xtx6a0vw4u/thesis_request_form.docx?dl=0|thesis request 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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Though we encourage thesis supervision, there is no obligation to supervise every interested student, e.g., in case no appropriate topic/supervisor is available. '''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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== 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.
 Our senior researchers can either supervise directly or suggest internally advertised topics and suitable supervisors:
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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)
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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.		 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: Sidney Bender (s.bender@tu-berlin.de)

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]].

=== 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.'''
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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 ||		 || - || - || -|| - || - ||

== 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 our thesis request 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.

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 either supervise directly or suggest internally advertised topics and suitable supervisors:

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: Sidney Bender (s.bender@tu-berlin.de)

You can find further information about our group members 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.

IDA Wiki: IDA/ThesisTopicsNew (last edited 2023-08-23 09:14:47 by JannikWolff)