We are a skilled team of researchers, engineers and entrepreneurs with a background in Mathematics, Machine Learning and Software Engineering, spanning experience from an array of organisations, including Imperial College London, University College London, Jaguar Land Rover, Volkswagen Group, Lufthansa Industry Solutions, and a number of tech startups.
Hami has expertise in product development and management as well as business strategy and partnerships. Prior to co-founding Wluper, he worked for a variety of tech startups, including Seedcamp-alumni Minubo and cloud-security provider Secucloud. Hami holds a BSc degree in Economics from the University of Hamburg, Germany and studied Computer Science on a Masters level at the University of Birmingham.
Maurice is experienced in software engineering and IT project management, building and maintaining enterprise solutions. He worked for organisations like Lufthansa Industry Solutions, Lufthansa Technik, Volkswagen Group, and Hermes Logistics Group. Maurice holds a BSc in Technology and Shipping Management and went to the University of Birmingham for a postgraduate course in Computer Science.
Nikolai is leading the development and research of Wluper's stack and infrastructure by working on novel approaches in machine learning for conversational systems. He earned his BSc in Mathematics from Imperial College London and did his MSc in Machine Learning at University College London. Currently he is doing his PhD in Mathematics in a co-organised programme at Imperial College and UCL, where he is awarded studentship for the Engineering and Physical Sciences Research Council.
Ed is working on neural architecture with attention mechanisms and carrying out research in the field of NLU, where he is leading the machine learning team at Wluper. He received a first class MEng in Computer Science from University College London and has published the work from his thesis on the automatic summarisation of scientific papers at CoNLL 2017, for which he was advised by Dr Isabelle Augenstein and Dr Sebastian Riedel from UCL's Machine Reading department.
Bingbing is focusing on Wluper's question answering capabilities as well as neural network optimisation methods for natural language processing. He completed his BSc degree in Computing from Dundalk Institute of Technology, Ireland and an MSc in Artificial Intelligence from the renowned AI department of the University of Edinburgh. He wrote his thesis in speech tracking and synthesis of weather forecaster in TV programmes using neural networks.
Amanda is involved end-to-end in our software life-cycle, where she is responsible for testing, code review, implementation, and maintenance. She has professional experience in vehicle architecture using parametric modelling, surface modelling and Knowledgeware. She holds a first class honours MEng in Biomedical Engineering from King's College London (University of London).
Dan is responsible for the back-end and infrastructure for machine learning models and deployment. He has professional experience in Computer-aided engineering at Jaguar Land Rover and has worked for its Special Vehicles Operations division, including in project management for the "XE SV Project 8". He gained an upper second-class honours MPhys degree in Theoretical Physics from the University of Leeds.
Daniel is an experienced executive with a demonstrated history of incubating and scaling startup software companies in the academic and research support space. He is the CEO at Digital Science since joining the business as co-founder of Symplectic. Most recently, he has served as Director of Research Metrics, whilst also acting as interim COO of portfolio company, Figshare. Daniel was a PhD student in Theoretical Physics at Imperial College London before he joined Digital Science. In addition to Daniel's programming and corporate experience he continues to play an active role in theoretical physics research.
Ben is an Assistant Professor/Lecturer at the Department of Mathematics at Imperial College London. His research is in the field of Bayesian uncertainty quantification and computational statistics, where he focuses on solving complex problems by developing state-of-the-art, computationally efficient, statistical methodology. Ben has published papers in top journals (such as RSS-B, PNAS, NIPS) on many topics including Markov chain Monte Carlo methodology, single ion channel modelling, and statistical inference using differential equations, with around 1000 citations to date.
If you are looking to make a difference in a rapidly evolving field, then have a look at our