IJAR Young Researcher Award for relAI student Yusuf Sale
Doctoral Researcher
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🎉 Congratulations!
We are proud to share that relAI PhD student Yusuf Sale has been honored with one of the IJAR Young Researcher Awards. The prestigious prize, funded by the International Journal of Approximate Reasoning (IJAR), recognizes students who demonstrate excellence in research at an early stage of their scientific careers.
Yusuf received the award at ISIPTA 25, the 14th International Symposium on Imprecise Probabilities: Theories and Applications, organized by ISIPTA, the leading international forum for theories and applications of imprecise probabilities.
This is an exceptional outcome, considering that only six papers have received this recognition out of more than 12000 submitted this year.
relAI has been instrumental in fostering the collaboration that led to this significant outcome by funding Unai Fisher Abaigar's research stay at Harvard University. Visits to international centres are one of the components of the relAI PhD curriculum, designed to support collaborations with international researchers and gain international research experience on the topic of the reliability of artificial intelligence (AI).
The paper tackles aspects of the Algorithmic Decision-Making relAI research area and the relAI central theme Responsibility, exploring how predictive models, particularly those using machine learning, can be used in government programs to identify and support the most vulnerable individuals.
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On the latest TV episode of “Neuland” by BR - Bayerischer Rundfunk, relAI PhD student Sarah Ball shares her insights about fundamental issues surrounding a central theme of relAI: “responsibility in AI systems.” She addresses topics such as when AI might reinforce discrimination and how to ensure that AI systems align with human values.
The recent work of relAI PhD student Lukas Gosch has won the Best Paper Award at the 3rd AdvML-Frontiers workshop at the 38th Annual Conference on Neural Information Processing Systems (NeurIPS 2024). The workshop and paper presentation took place at the Vancouver Convention Center in Canada on December 14th, 2024.
Lukas is a PhD student at relAI, advised by the relAI Co-Director Prof. Dr. Stephan GĂĽnnemann. His research focuses on robust and reliable machine learning, as well as machine learning on graphs.
The award-winning paper „Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks“, that Lukas authored together with Mahalakshmi Sabanayagam and relAI Fellows Debarghya Ghoshdastidar and Stephan Günnemann, develops the first architecture-aware certification technique for common neural networks against poisoning and backdoor attacks.
Our sincerest congratulations to Lukas and his co-authors on this achievement!
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We are excited to announce that the call for applications to the PhD program 2025 of our Konrad Zuse School of Excellence in Reliable AI (relAI) is now open!
The novel, innovative PhD relAI program offers a cross-sectional training for successful education in AI including scientific knowledge, professional development courses and industrial exposure, providing a coherent, yet flexible and personalised training.
Funded applicants will receive a full salary for three years, including social benefits (TV-L E13 of the German public sector). They are further supported by travel grants, e.g. for conference attendance, research stays or home travel. Doctoral students are hosted by a relAI Fellow who helps them to define their research project. Depending on the affiliation of this hosting fellow they enrol at TUM or LMU.
We highly encourage you to apply if you have:
an excellent master’s degree (or equivalent) in computer science, mathematics, engineering, natural sciences or other data science/machine learning/AI related disciplines;
a genuine interest to work on a topic of reliable AI covering aspects such as safety, security, privacy and responsibility in one relAI’s research areas Mathematical & Algorithmic foundations, Algorithmic Decision-Making, Medicine & Healthcare or Robotics & Interacting Systems;
Please help us in spreading the word, especially to excellent international candidates.
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Congratulations! relAI student Sameer Ambekar wins the best paper award at the MICCAI Workshop on Advancing Data Solutions in Medical Imaging AI (ADSMI).
Sameer is a PhD student at relAI, advised by the relAI Fellow Julia A. Schnabel. His research focusses on test-time adaptation and domain generalization for medical imaging.
His award-winning paper “Selective Test-Time Adaptation for Unsupervised Anomaly Detection using Neural Implicit Representations”, co-authored with Julia A. Schnabel and Cosmin Bereca, presents a novel zero-shot methodology to adapt models in real time to test images from new domains using deep pre-trained features. The approach is validated on brain anomaly detection data.
This work addresses domain shift at test-time, which Sameer explains in more detail in his recently published relAI blog post. In the post, you can also learn about the importance of handling domain shifts to make AI more reliable: https://zuseschoolrelai.de/blog/mitigating-domain-shifts/
Congratulations on this achievement!
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We are excited to announce that our second call for applications to the fully funded PhD program of our Konrad Zuse School of Excellence in Reliable AI (relAI) is now open.
Spread the word and submit your application by January 15th, 2024here.Â
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On the 11th and 12th of October, relAI welcomed the new cohort of relAI Master and Doctoral students. The event included informative sessions about relAI and networking activities. At the Munich Data Science Institute (MDSI), TUM, the relAI directors and coordinators presented the relAI program to the new students. The first relAI cohort of students organised a lively interactive session (photo) to welcome and get to know the new students.
ECML PKDD is Europe’s top machine learning and data mining conference, with over 20 years of successful events and conferences across the continent. The ECML PKDD 2023 was held in Turin, Italy from the 18th to the 22nd of September 2023.
List of authors and title of the awarded paper:Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou, Bernd Bischl, Stephan GĂĽnnemann, David RĂĽgamer Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry
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We are excited to announce that applications for the relAI PhD program are now open. Interested candidates can apply through our website. Deadline for applications is January 9th, 2023.
The novel, innovative PhD relAI program offers a cross-sectional training for successful education in AI including scientific knowledge, professional development courses and industrial exposure, providing a coherent, yet flexible and personalised training.
Funded applicants will be hired for three years, including social benefits (TV-L E13 of the German public sector). They are further supported by travel grants, e.g. for conference attendance or research stays. Doctoral students enrol at TUM or LMU depending on the hosting relAI fellow.
We encourage candidates with an excellent master’s degree (or equivalent) in computer science, mathematics, engineering, natural sciences or other data science/machine learning/AI related disciplines and a genuine interest to work on a topic of reliable AI to apply.
