About Me

I’m an applied AI/ML engineer and technical problem solver. My strengths is bringing order, clarity and calm into ambiguous technical problems: the requirements are unclear, the technology is unfamiliar, several disciplines collide, people disagree, and someone needs to figure out what is actually technically possible and create a path forward in the noise.

I’ve done that across very different environments, both in academia and industry. During my PhD in Biomedical Engineering, I worked with European hospitals on liver-cancer imaging research, producing peer-reviewed work and contributing to technology that became part of a commercial clinical product. In industry, I’ve moved between signal processing, data science, machine learning, medical imaging, computer vision, generative AI, cloud systems — often learning a new domain while already having to deliver in it.

That is probably the common thread in my work: I learn quickly, bring structure to ambiguity, ask for evidence, communicate across disciplines and keep digging until there is a workable solution. I like being deep enough technically to work with engineers, broad enough to see the whole system, and close enough to the problem to ask whether what we are building is actually useful.

What I offer people I collaborate with is the ability to bring clarity, transparency, and practical measurable outcomes to complex problems. Drawing on my experience across AI/ML, generative AI, cloud systems and technical leadership, I can quickly understand a new domain, identify what is technically realistic, align stakeholders and motivate the team to turn an ambiguous idea into a solution that works smoothly in demos or production lines.