Netherlands
At Innatera, I lead the Business Development team, driving go-to-market and partner strategy for AI-driven semiconductor solutions. I work closely with customers and strategic partners to turn emerging edge AI capabilities into commercially viable products and scale their adoption. My background spans engineering and commercial execution. Previously, as a Staff Neuromorphic Engineer and Program Manager, I led application development, contributed to hardware–software co-design, and worked closely with customers, aligning technical direction with market needs. Alongside my industry work, I contribute to the broader research and innovation ecosystem as an Honorary Lecturer at University College London and as a Programme Committee Member of the International Society for Artificial Life. I hold a PhD in Artificial Intelligence from University College London, specialising in spiking neural networks and evolutionary algorithms, and have taught across machine learning, robotics, and programming. Earlier in my career, I built a strong foundation in life sciences, with hands-on experience spanning neuroscience, pharmacology, and bioengineering. My multidisciplinary background allows me to bridge domains, connect stakeholders, and unlock opportunities.
Teaching modules on AI, including explainable AI, reinforcement learning, Bayesian networks, and heuristic optimization.
- Won a Postgraduate Teaching Assistant Award based on student nominations and faculty feedback. - Led tutorials on AI and Machine Learning theory (heuristic algorithms, supervised and unsupervised learning) and practice (Scikit-learn, Tensorflow) and on programming (C, Python) for undergraduate and postgraduate students in five modules. - Developed teaching materials (lecture slides, Python and Machine Learning tutorials and exercises) and assessments. - Selected and presented AI research papers and led in-class discussion. - Delivered lectures as a part of the “Machine Learning for Domain Specialists” module.
- Provided guidance for data and model selection for fraud detection using anomaly detection techniques to identify suspicious patterns in data access - Advised on and contributed to a white paper and the Mitacs Accelerate Proposal, supporting the project's development and securing funding.
- Contributed to the cleaning, analysis, and visualisation of nationwide U.S. data of mobile phone locations over time to provide actionable insights to legislators and medical organisations during the COVID-19 pandemic. - Developed models identifying different population segments, location types (e.g., home, work), and spatio-temporal patterns of travel and socialising of different groups.
- Led the product discovery and development of an AI-based employee interaction simulator utilising agent-based modelling, swarm intelligence and genetic algorithms. - Secured a Letter of Intent from a leading Big Five consultancy firm. - Designed and implemented key features and constraints based on client requirements.