I am an assistant manager at Deloitte Zurich.

My core skills are machine learning (deep learning), scientific computing, software engineering and I have experience through my engagements at Deloitte in financial risk management.

🎓 My deep learning-related work is on Graph Neural Networks (1), Bayesian Deep Learning for Generative modeling (e.g., (3, 4) ) and their fusion (e.g., such as VAEs for graph structured data 5, and Bayesian predictive models for time-series on graphs 6). I have also developed Bayesian deep learning models for high frequency time-series in Structural Health Monitoring (SHM) (7), and co-authored papers on using Neural ODEs for SHM (8).

These are some of the techniques I employed during my PhD at ETH Zurich.

💼 At Deloitte, I’ve worked in business development for energy market analytics, I have contributed to the assessment of liquidity and credit risk systems, and created cryptocurrency transaction analytics prototypes. I have also been involved in defining the requirements, coordinating efforts, and contributing to early asset development of retrieval augmented generation (RAG) LLM-prototypes, introduced DevOps processes, and provided several internal seminars on RAG and LLM technology.

🎹 On my free time, I play music and hack-around with microcontrollers, software engineering and automation, and keep up with deep learning research.