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Embodied AI Researcher | Humanoids, RL, Scene Understanding
I am a final year PhD student at the University of Freiburg and 2025 RSS Pioneer. My research advances decision-making for embodied AI through reinforcement learning and hybrid learning, planning and control methods, integrated with scalable semantic representations. The drive to integrate robotics with AI propels our research, contributing to an ecosystem where machines understand and interact with their environments intelligently.
The University of Freiburg
Doctor of Philosophy - PhD, Reinforcement learning in robotics
January 1, 2020 – September 1, 2025
UCL
Master of Science - MS, Computational Statistics and Machine Learning
January 1, 2017 – January 1, 2018
University of Zurich
Bachelor of Arts (B.A.), Economics
January 1, 2013 – January 1, 2016
Alte Kantonsschule Aarau
Matura, Physics and applied mathematics major
January 1, 2008 – January 1, 2012
Flexion Robotics
AI Research Engineer
August 1, 2025 – Present
Zurich, Switzerland
The University of Freiburg
Doctoral Research Assistant
May 1, 2020 – April 1, 2025
Freiburg, Baden-Württemberg, Germany · On-site
Fetch.AI
Machine Learning Engineer
October 1, 2018 – April 1, 2020
Cambridge, Großbritannien
Swiss National Bank
Economic Analysis
July 1, 2016 – July 1, 2017
Zurich, Switzerland
University of Zurich
Tutor
September 1, 2015 – January 1, 2016
Zurich, Switzerland
University of Zurich
Research Assistant
October 1, 2014 – April 1, 2016
Zurich, Switzerland
Allianz Suisse Versicherungs-Gesellschaft AG
Teammember Health, Accident and Liability Insurance
January 1, 2014 – March 1, 2014
Zurich, Switzerland
Active Classifiers
May 1, 2018 – Present
Exploring new ways to teach models to actively infer believes about the states of our world, enabling a much more human like learning and planning. This is ongoing work based on my MSc thesis and heavily draws on the literature of Active Inference, Predictive Coding and Reinforcement Learning.
Recurrent Attention Model
May 1, 2018 – June 1, 2018
Tensorflow implementation of a Recurrent Attention Model (Mnih et al. 2014). The model learns to classify images by taking "glimpses", learning where to look next based on what it has already seen. This location policy is non-differentiable and therefore trained with reinforcement learning. The implementation is able to reproduce all results from the original paper.
Cultural Fit Analysis
The candidate's background includes diverse experiences from academic research (PhD, MSc), industry (Fetch.AI, Flexion Robotics), and even finance (Swiss National Bank). The personal projects demonstrate a proactive and curious approach to advanced ML topics. The focus on cutting-edge research in ML and robotics aligns well with an innovative and research-driven culture. The breadth of skills and project diversity suggest adaptability and a willingness to tackle complex, novel challenges. However, the lack of explicit team collaboration or leadership roles outside of co-supervision limits a deeper cultural fit assessment.
Soft Skills & Operational Fit
The candidate's project descriptions and work history suggest strong analytical and problem-solving skills. Experience in co-supervising students indicates leadership potential. The PhD research in robotics implies a capacity for long-term, complex problem-solving and independent research. However, without specific assessment data on communication, logical reasoning, or teamwork, a definitive assessment of soft skills and operational fit is limited.