Our AI experts

''I feel privileged to be working with such a diverse group of talented individuals from different backgrounds, professional paths and academic fields, all united by our passion for machine learning technology and driven by the opportunity to learn about our clients’ technology every day. The combined commercial, legal and technological know-how of our AI group is second to none in the industry.'' - Alexander Korenberg, AI Lead at Kilburn & Strode

Alexander Korenberg

Alexander Korenberg is a partner in our Tech team and leads the firm’s AI practice. With over 20 years in the profession, Alexander is a Tech maestro with a solid grip on the legal aspects of patent prosecution and extremely technically savvy, who builds futureproofed patent strategies in cutting-edge areas such as AI (in the words of industry publications). His passion for everything AI stems from his time gaining a PhD at the Gatsby Computational Neuroscience Unit, then led by Geoff Hinton. His work there included early studies into the use of unsupervised learning for inference and artificial neural network models of human motor control.
When Alexander is not busy drafting and prosecuting AI cases, he is keen to pass on his knowledge. In addition to training his junior colleagues, he has taught a short course on AI for patent attorneys and helped the EPO and UKIPO develop training examples of AI claims for examiners and the public. Alexander is regularly invited to speak at events and to lead panel discussions related to AI.
Alexander publishes a monthly AI patenting newsletter (A patent attorney’s AI musings) looking at the latest developments, or considering fundamental questions about the protection of AI by IP when there are little news to report.

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Tom Hamer

Tom, a partner in our Tech team currently on a stint in San Francisco, is an expert when it comes to the EPO's approach to algorithms, mathematical methods, and AI/ML. Tom regularly talks in the Bay Area and beyond, advising some of the biggest tech companies in the world on how to patent AI in Europe, including how to make reliable filing decisions that are guided by the latest EPO case law and how to draft AI applications to optimise their chances of success in Europe. He likes to talk under the heading 'It's easy to patent AI in Europe', and enjoys helping companies push the eligibility boundaries to achieve commercially useful protection.
Tom's AI practice focuses on automated medical image segmentation and modality conversion using neural nets, the intelligent automation of industrial methods, such as predictive maintenance, and remote diagnostics, as well as the analysis of healthcare data using statistical techniques to make diagnoses. He is particularly proud to have helped a major medical device manufacturer build its predictive maintenance patent portfolio in the UK and is excited to be assisting a spin-out company protect their novel 'lab-on-a-chip' device and associated ML-powered software for rapid DNA and RNA detection, including the development of a disposable cartridge to detect SARS-CoV-2.

Stefanie Lewis

Stefanie Lewis is an exceptionally experienced attorney in our Tech team based in the Netherlands. Prior to joining the patent profession, she completed her PhD in nuclear physics Her work included developing Bayesian inference program to maximise information yield from limited experimental data. Her PhD project also involved the use of computational techniques, including parallel programming, which adds to the deep AI knowledge she brings to the team. As a patent attorney, Stefanie has substantial experience in drafting and prosecuting AI-based inventions for a wide variety of applications, such as autonomous vehicles, anomaly detection and data security.
Stefanie regularly talks on how AI inventions can be protected in Europe, including key differences with other jurisdictions. She has a passion for sharing her knowledge with the next generation of patent attorneys and teaches an annual course on how to pass the European Qualifying Examination before the European Patent Office.

Nathalie Richards

Nathalie is a trainee patent attorney in our Tech team, who has joined the firm after a Master's degree in Financial computing and time working at a data company as project lead for developing and releasing an automation suite for testing finance systems such as Oracle. In addition to her practical computer engineering experience, Nathalie brings academic excellence,  having graduated her Master’s with distinction. Her studies included Machine Learning and Data Analytics, as well as projects involving statistical pattern recognition methods, neural networks, and clustering, which she is now putting to good use as part of the firm’s AI team. Nathalie is fluent in several programming languages, including C++, Python, SQL, R, and Matlab. Nathalie has taken the Stanford Certificate Courses in Machine Learning and Deep Learning.


Orla Murphy

Orla is a trainee patent attorney with the Tech team and currently focuses on AI related to medical physics, in particular medical imaging. Before joining the firm, Orla obtained a Master’s degree in Medical Physics and Biomedical Engineering from University College London. Her undergraduate degree in Theoretical Physics (University College Dublin, first-class honours) saw her win the prestigious and highly competitive Ad Astra and Naughton scholarships, as part of which she spent a year at UCSD, California. Orla also spent a summer doing an internship with Huawei in China as part of the "Seeds for the Future" programme and was a summer intern with PWC working on a project involving robotic process automation. In addition, Orla is a recipient of the Stanford University certificates in Machine Learning and Deep Learning.    


Aaron Lam

Aaron is a trainee in our Tech team based in London. Prior to joining the firm he graduated from Imperial College London with a first-class bachelor’s degree in Materials Science and Engineering and a distinction master’s degree in Computing. During his bachelor’s degree, he was awarded the Morgan Advanced Ceramics Undergraduate Prize for his excellent experimental analysis and written work. His master’s studies included machine learning and computer vision modules, where he worked on coding projects involving decision trees, neural networks, image classification and image filtering. His experience in computing, including working in languages from C++, Python and Java to Matlab and Golang, gives him up-to-date, cutting edge insight into a range of technologies, including machine learning, blockchain and software. Aaron’s MSc dissertation involved creating an interactive application, from the ground up, using OpenGL graphics to visualise electron transport through organic molecules. During his master’s degree, Aaron was also involved in a blockchain-based project involving a de-fi aggregator website for liquidity providers.


Yasmin Razzaque

Yasmin is a trainee in our Life Sciences and Chemistry team with degrees in Biochemistry from Leeds and Berlin Free University. Yasmin has practical experience in the application of AI in bioinformatics research from her time in Berlin, at the Berlin Institute for Medical Systems Biology. Specifically, she worked on cancer regulatory networks and her research involved the acquisition, integration and analysis of large proteomic and metabolomic datasets. As part of her research, Yasmin used ML-based computational biology software to identify target compounds and R-based algorithms including clustering and principle component analysis to interrogate large data sets in order to identify diagnostic and therapeutic targets for colorectal cancer. With her inter-disciplinary background, Yasmin is currently working with clients in the fields of high throughput screening technologies and is a great addition to any team looking to protect AI innovation in the biomedical sector.


James Cochrane

James is a trainee patent attorney in our Life Science and Chemistry team, with both undergraduate and postgraduate degrees in Biotech from Imperial College London and practical research experience with computational methods. For example, James has used convolutional neural networks to implement object tracking for quantitative behavioural studies of Drosophila models and ran molecular dynamics simulations for ligand pocket predictions and protein docking in the context of drug discovery, to identify potential inhibitors of an intrinsically disordered cancer protein. Using his background and experience, James works with clients to protect their innovation in computational biology.


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