Leonard Papenmeier Email & Phone Number

I am a PhD student at WASP, the Wallenberg AI, Autonomous Systems and Software Program, where I conduct cutting-edge research in machine learning and data science. My research focuses on developing novel methods for Bayesian optimization, a powerful technique for optimizing expensive-to-evaluate black-box functions, such as those arising in neural architecture search, robotics, and life sciences. With over 8 years of experience in software engineering and deep learning, I have a strong background in applying machine learning to various domains, such as image processing, natural language processing, and computer vision. I have worked as a deep learning engineer at img.ly and a working student at adesso AG, where I developed and deployed multiple machine learning solutions using Python, TensorFlow, PyTorch, and other tools. I have also contributed to the scientific community by publishing a paper at NeurIPS, one of the top conferences in machine learning, where I presented a novel method for adaptive and robust high-dimensional Bayesian optimization. I am passionate about advancing the state-of-the-art in machine learning and data science, and applying them to real-world challenges. I value collaboration, innovation, and impact, and I seek to join a team that shares these values and goals.