Mentions
Collections of links that mention my work
Conversation with an (ex-DeepMind) fractional CAIO about agentic coding
Hugo’s post on simulation and our early work on Alphafold
Discussions of 2024 Nobel Prize work
Further mentions of NCBR Scientific Council
InstaDeep’s impact on African AI
On emotional component of facial recognition
New Scientist discusses facial recognition in TV shows.
About me
I am the Executive Director of Machine Learning Engineering at Generate:Biomedicines, leading teams that combine generative AI, high-performance computing, and experimental automation to design new medicines. Previously, I led ML Engineering at Isomorphic Labs, reporting to the CTO and managing three tech leads across drug discovery, core ML research, and large-scale cloud deployment.
I helped initiate DeepMind’s AlphaFold, whose breakthroughs in protein structure prediction earned the 2024 Nobel Prize in Chemistry for its scientific leads. That project showed how machine learning could close a fifty-year gap in biology and set the path for my career by demonstrating how to translate frontier AI into real biomedical impact.
At GSK.AI, I built the company’s first large-scale knowledge graph learning systems to help scientists identify relationships across biomedical literature. As Head of Machine Learning at InstaDeep, I partnered with BioNTech to design ML systems for personalized mRNA cancer vaccines and led distributed engineering teams across Europe and Africa.
I have been an early employee and key contributor in three AI-first startups:
2014 DeepMind -> Google (400–650M USD)
2019 Cortexica -> Zebra Technologies (undisclosed)
2021 InstaDeep -> BioNTech (500M EUR)
These experiences taught me how to build organizations where deep research and engineering discipline coexist, where abstract models become production systems with measurable scientific and commercial outcomes. My focus is on scalable infrastructure for scientific discovery and on turning conceptual advances into platforms that scientists and partners can actually use.
I have also served on the Scientific Board of IDEAS NCBR, advising Poland’s National Centre for Research and Development on AI strategy and helping emerging ecosystems avoid the barriers I once faced starting from the scientific periphery.
My academic path began in pre-EU Poland, where I earned an M.Sc. in Physics from the University of Warsaw, researching biomedical signal processing and spending time at Vrije Universiteit Amsterdam in medical physics. I later completed a Ph.D. in Neuroscience at Ruhr University Bochum in Germany, working with early deep learning models such as LSTMs and manifold learning systems for memory and face recognition.
That formative journey continues to shape how I think about innovation, policy, and talent. I believe the next breakthroughs will arise from combining human ingenuity, experimental feedback, and AI at scale.