Algorithms & Society

My research studies foundations of machine learning with a focus on social questions. I am broadly working towards developing theoretical as well as practical tools to support safe, reliable and trustworthy machine learning with a positive impact on society. This encompasses technical challenges around interactive machine learning, optimization in dynamic environments, and resource-efficient learning, as well as interdisciplinary questions on understanding social dynamics around algorithms, quantifying their impact on digital economies, and developing tools to responsible use machine learning models in social science research.

Projects Publications Patents

Featured Research Projects

• Performative Prediction

  Predictions when deployed in societal systems can trigger actions and reactions of individuals. Thereby they can change the way the broader system behaves -- a dynamic effect that traditional machine learning fails to account for. To formalize and reason about performativity in the context of machine learning, we have developed the framework of performative prediction. It extends the classical risk minimization framework in that it allows the data distribution to depend on the deployed model. This inherently dynamic viewpoint leads to new solution concepts and optimization challenges, and it brings forth interesting connections to concepts from causality and game theory.

  

  Resources

  • Seminal work: https://proceedings.mlr.press/perdomo20
  • A survey: https://arxiv.org/abs/2310.16608

• Measuring Power in Digital Economies

  Algorithmic predictions play an increasingly important role in digital economies as they mediate services, platforms, and markets at societal scale. The fact that such services can steer consumption and behavior is a central concern in modern anti-trust. I am exploring how the concept of performativity can help quantify economic power in digital economies and support digital market investigations. Intuitively, the more powerful a firm the more performative their predictions, a causal effect strength we can measure from observational and experimental data.

  

  Powermeter

  A Chrome extension that measure how algorithmic decisions of search engines impact user click behavior by collecting experimental data.

  → Sign up at https://powermeter.is.tue.mpg.de/

• Scalable Learning Algorithms

  When building societal-scale machine learning systems, training time and resource constraints can be a critical bottleneck for dynamic system optimization. Efficient training algorithms that are aware of distributed architectures, interconnect topologies, memory constraints and accelerator units form an important bilding block towards using available resources most efficiently. As part of my PhD research, we demonstrated that system-aware algroithms can lead to several orders of magnitude reduction in training time compared to standard system-agnostic methods. Today, the innovations of my PhD research form the backbone of the IBM Snap ML library that has been integrated with several of IBM's core AI products.

  

  IBM Snap Machine Learning

  Snap ML is a library that provides resource efficient and fast training of popular machine learning models on modern computing systems.
  >400k downloads on PyPi
  https://www.zurich.ibm.com/snapml/