

Heiko Zimmermann


Heiko Zimmermann is a PhD student at Northeastern’s Khoury College of Computer Sciences focusing on machine learning, advised by Professor Jan-Willem van de Meent. He works on probabilistic modeling and Bayesian inference. He comes from Stuttgart, Germany.
Zimmermann earned his BS and MS from the University of Stuttgart, where he worked on Bayesian Optimization and its applications in Robotics and biomechanics. At Northeastern he is focusing on Bayesian methods for inference in probabilistic models, probabilistic programming, and efficient inference techniques.
My graduate research at the Machine Learning and Robotics Lab in Stuttgart focused on Bayesian Optimization including theoretical aspects, extension of Bayesian Optimization to functional domains, and applications in robotics and biomechanics. At Northeastern, I’ll be working on probabilistic modeling and Bayesian inference.
Our world involves uncertainties on many different levels. When modeling different aspects of the world, we need to capture these uncertainties to answer questions and quantify our confidence in terms of probabilities. Probabilistic modeling and inference are at the core of this.
In Machine Learning and AI, a lot of time is spent on finding the right model and inference/optimization technique for specific tasks. This involves the tuning of hyperparameters but also fundamental decisions regarding the model’s structure that are tightly coupled to the availability and choice of inference algorithms. Probabilistic Programming is a framework that enables domain experts to build models in a well understood way, in the form of a program, without in-depth knowledge about the inference. I would like to contribute to the ongoing effort to further automate probabilistic modeling and inference.
The principles that underpin Bayesian inference are remarkably simple, however, for a lot of interesting setting the simple application of these principles results in intractable problems. Working around these intractabilities is often a tricky task.
Establishing myself as a researcher.
Heiko Zimmermann is a PhD student at Northeastern’s Khoury College of Computer Sciences focusing on machine learning, advised by Professor Jan-Willem van de Meent. He works on probabilistic modeling and Bayesian inference. He comes from Stuttgart, Germany.
Zimmermann earned his BS and MS from the University of Stuttgart, where he worked on Bayesian Optimization and its applications in Robotics and biomechanics. At Northeastern he is focusing on Bayesian methods for inference in probabilistic models, probabilistic programming, and efficient inference techniques.
My graduate research at the Machine Learning and Robotics Lab in Stuttgart focused on Bayesian Optimization including theoretical aspects, extension of Bayesian Optimization to functional domains, and applications in robotics and biomechanics. At Northeastern, I’ll be working on probabilistic modeling and Bayesian inference.
Our world involves uncertainties on many different levels. When modeling different aspects of the world, we need to capture these uncertainties to answer questions and quantify our confidence in terms of probabilities. Probabilistic modeling and inference are at the core of this.
In Machine Learning and AI, a lot of time is spent on finding the right model and inference/optimization technique for specific tasks. This involves the tuning of hyperparameters but also fundamental decisions regarding the model’s structure that are tightly coupled to the availability and choice of inference algorithms. Probabilistic Programming is a framework that enables domain experts to build models in a well understood way, in the form of a program, without in-depth knowledge about the inference. I would like to contribute to the ongoing effort to further automate probabilistic modeling and inference.
The principles that underpin Bayesian inference are remarkably simple, however, for a lot of interesting setting the simple application of these principles results in intractable problems. Working around these intractabilities is often a tricky task.
Establishing myself as a researcher.