relAI Collab Accelerator Workshop

Students

Students

The recent relAI Collab Accelerator Workshop brought together researchers to share their work, explore new ideas, and identify potential collaborations. Here's a brief overview of the event:

The day began with the participants pitching their research topic from 9:00 to 11:00, followed by a coffee break until 11:15. After the break, participants engaged in one-to-one sessions until 14:00, followed by lunch, discussion, and feedback.

Participants contributed diverse topics in the field of Machine Learning and Artificial Intelligence. Mohamed Amine Ketata discussed Generative AI for Graphs, Max Beier presented on Learning Operator of Dynamical Systems, Richard Schwank explored Robust Aggregation through the Geometric Median, and Yurou Liang delved into Differentiable Learning for Causal Discovery.

The workshop was fertile ground for generating new research ideas and possible collaborations. During the one-to-one discussions, participants identified several projects for cooperation, such as principled modifications of loss functions to enhance robustness against outlier data rows.

Participants gained new insights into their research during the event. For example, one participant discovered a probabilistic approach to their forecasting issue without relying on a model. Another learned about structure learning as it applies to tabular data, which provided a temporal interpretation of the data. One researcher was challenged about the convexity of their problem. Discussions highlighted intriguing applications of median aggregation techniques to abstract spaces, connecting concentration inequalities with uncertainty quantification.

The relAI Collab Accelerator Workshop was an enriching experience, offering a platform for researchers to connect, share insights, and pave the way for future collaborations. The feedback during this first iteration will help refine the format and make it even more engaging. We are looking forward to the next iteration!

relAI thanks Max Beier and Richard Schwank for their initiative and the organization of the event.

Congratulations!

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 Günnemann. 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.

Explore this outstanding paper here.

Our sincerest congratulations to Lukas and his co-authors on this achievement!

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;
  • certified proficiency in English.

📆 Application Deadline: January 13th, 2025

🔗 Apply now: www.zuseschoolrelai.de/application

Please help us in spreading the word, especially to excellent international candidates.

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!  

Image Copyright (c): Thomas Abé/Studienstiftung

Congratulations to relAI student Maria Matveev! 

The German National Scholarship Foundation (Studienstiftung) has awarded Maria the Civic Engagement Award 2024 for her exceptional volunteering work with Lern-Fair. Maria co-founded and chairs Lern-Fair e.V., a non-profit organization dedicated to providing free educational opportunities for underprivileged pupils. Since the start of the online platform in 2020 during the Covid pandemic, more than 15.000 pupils were supported by free tutoring or group courses. 

Maria is a relAI PhD student at the chair for Mathematical Foundations of Artificial Intelligence at LMU and the Munich Center for Machine Learning. Her PhD research, advised by the relAI director Gitta Kutyniok, focuses on the mathematical description and understanding of training dynamics related to generalization, a crucial factor for ensuring the reliability of neural networks. 

Learn more about Marias volunteer work in the video portrait (in German): https://youtu.be/EUZdm--sqmc?feature=shared 

Are you interested in frontier AI systems, their astonishing capabilities and risks for humanity? Then join us for a thought-provoking deep dive and exclusive OpenAI Live Q&A on AI safety. 

  • Date: Wednesday, May 8th, 2024 | 19:00 – 20:30 
  • Location: Room B006, Department of Mathematics (Theresienstr. 39) or online 
  • Language: English 

Agenda: 

  • 19:00 – 19:05: Doors open 
  • 19:05 – 19:30: Introduction to AI Safety 
  • 19:30 – 20:15: Presentation & Live Q&A with OpenAI researcher Jan H. Kirchner, co-author of weak-to-strong generalization paper 
  • 20:15 – 20:30: Closing talk – What can we do? 
  • 20:30 – onward: Optional socializing and small group discussions with free drinks and snacks. 

Please register on the following webpage and prepare your questions! 

Last week, our relAI students presented their research to the relAI industry partners in a series of industry workshops. Four events took place, each centered around one of  the four relAI’s research areas: Mathematical & Algorithmic foundations, Algorithmic Decision-Making, Medicine & Healthcare and Robotics & Interacting Systems. 

We are thrilled that this event was so well received both by the students and the industry partners! Following short lightning talks, intriguing discussions around reliability of AI took place in smaller breakout groups.  

The industry workshops are part of relAI´s cross-sectional training and aim to facilitate the exchange of insights and expertise between academia and industry. The engagement from both our students and industry fellows emphasized the significance of bridging academic excellence with real-world applications, particularly when addressing the evolving challenges in AI reliability. 

We are excited to announce that our call for applications to the relAI MSc program is now open! 

The novel, innovative relAI MSc program is an addition to a regular MSc program at Technical University of Munich (TUM) or Ludwig Maximilians University (LMU), offering comprehensive cross-sectional training in reliable AI, including scientific knowledge, professional development courses, and industrial exposure. Funded applicants receive a scholarship of up to 934€ and additional support such as travel grants for home travel.   

relAI, funded by the German Academic Exchange Service (DAAD), is embedded in the unique transdisciplinary Munich AI ecosystem, combining the expertise of the two Universities of Excellence TUM and LMU of Munich.  

We highly encourage you to apply if you:   

  • hold an excellent Bachelor’s degree in computer science, mathematics, engineering, natural sciences or other data science/machine learning/AI related disciplines,  
  • are accepted to a MSc program in said disciplines at either TUM or LMU starting in spring or fall 2024, or have applied there (Acceptance necessary before joining relAI) 
  • have a genuine interest to study 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, and
  • can certify proficiency in English on C1 or higher level.  

📆 Application Deadline: June 17th, 2024  

🔗 Apply now: www.zuseschoolrelai.de/application 

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, 2024 here.