Team Members

I lead the Relational Machine Learning Group at the Helmholtz Center CISPA since 2021. Our research on sparse deep learning is funded by an ERC starting grant since December 2023 and by Apple Research since August 2025. From 2019-2021, I was a PostDoc at the Biostatistics Department of the Harvard T.H. Chan School of Public Health working with John Quackenbush, from 2017-2018 at the Institute for Machine Learning at ETH Zurich with Joachim Buhmann, and from 2016-2017 at the Chair of Systems Design at ETH Zurich with Frank Schweitzer. My PhD research at the ETH Risk Center was supervised by Frank Schweitzer and co-supervised by Hans J. Herrmann from 2013-2016. My thesis on systemic risk won the Zurich Dissertation Prize and our work on international maize trade received the CSF Best Contribution Award. I studied Mathematics and Physics at TU Darmstadt.

I focus on understanding the intricate dynamics of training and fine-tuning in machine learning models, with the goal of developing more efficient and effective learning algorithms. My research explores how optimization processes evolve and how we can refine these methods to improve performance. Currently, I am particularly interested in gradient compression techniques. I obtained my PhD under the supervision of Prof. Miguel Rodrigues at University College London, UK.

I work on research problems at the intersection of machine learning and causality, focusing on modeling, inference, and interpreting machine learning models from a causal perspective to enhance their robustness and trustworthiness. I received my Ph.D. at the Indian Institute of Technology Hyderabad, where I was advised by Prof. Vineeth N Balasubramanian. During my Ph.D., I was awarded the prestigious Prime Minister’s Research Fellowship (PMRF).

I focus on graph learning and similarity measures for graphs, with the aim of improving efficiency, expressivity, and accuracy. I completed my doctorate with distinction in the Kriege group at University of Vienna, and received the Award of Excellence from the Austrian Ministry of Women, Science and Research for my thesis.

I work on understanding the training dynamics of graph neural networks. Specifically, I am interested in developing an attention-based message passing formulation that would allow the model to utilize the input graph as data and a computational structure in potentially different ways to adapt to the graph learning task. Previously, I completed my Master’s in Computer Science at LUMS (Pakistan).

My research focuses on machine learning for graph and structured data. I am particularly interested in how input structures shape both data representation and computation, with broader interests in generalization trade-offs and architectural efficiency. I was awarded a ”la Caixa” Foundation Postgraduate Fellowship, which supported my master’s studies at the University of Strathclyde in the UK, where I was awarded the Departmental Best Overall Performance Prize. I hold degrees in Mathematics and Computer Science from Universidad Complutense de Madrid.

My research interests lie at the intersection of understanding neural network training dynamics and designing efficient deep learning methods. Concretely, I work with theoretical tools such as mirror flow, regularization techniques, and mean field descriptions to study the effect of overparameterization and improve model efficiency. I completed my Master’s and Bachelor’s degrees in Applied Mathematics at Eindhoven University of Technology.

I am developing algorithms to reduce the size of neural networks by increasing parameter sparsity and decreasing the storage required for each parameter. My current focus is on sparse topologies that enhance the performance of sparse networks. Additionally, I am working on efficient quantization techniques to minimize the effective size of large language models (LLMs). I studied a master’s degree at Universität des Saarlandes.

I study how multiple agents interact, learn, and how these processes can be optimized. Currently, I’m developing a general framework that enables a wide range of agents—from simple nodes to complex models—to interact efficiently. My past work focused on optimizing multi-agent reinforcement learning using ideas from game theory. I recently completed a Data Science and AI master at Saarland University. Previously, I did a bachelor in Electrical and Electronics Engineering at University of Khartoum.