Peter has a pure mathematics PhD from Cambridge University and has a special interest in the mathematical foundations of deep learning. Myrtle accelerates performance critical workloads on FPGAs devices that are currently being deployed at scale in data centres around the world. Myrtle has realized multiple proprietary deep learning networks as silicon designs so that they execute at a latency and power point that makes them usable in real-world situations. Myrtle is currently targeting its technology at inference workloads in data centres and is involved in a major collaboration to address the safety and verification challenges that currently preventing sophisticated deep learning networks being used in road vehicles.
- Co-Founder, CEO, Prowler.io
Vishal Chatrath is CEO and co-founder of PROWLER.io, a Cambridge-based developer of principled AI decision-making technologies that help customers understand, guide and optimise the millions of micro-decisions that occur in complex, dynamic environments. Its world-class researchers and engineers are transforming fields like systems engineering, autonomous vehicles, game design, and smart city planning. Vishal was previously head of automotive at Nokia, founder of Chleon Automotive and Chief Business Officer of VocalIQ, which was acquired by Apple in 2015. He focuses on turning ground-breaking research into workable solutions.
Phil Claridge is a ‘virtual CTO’ for hire within Mandrel Systems covering end-to-end systems. Currently having fun and helping others with large-scale AI systems integration, country-wide large scale big-data processing, hands-on IoT technology (from sensor hardware design, through LoRa integration to back end systems), and advanced city information modelling. Supporting companies with M&A ‘exit readiness’, due-diligence and on advisory boards. Past roles include: CTO, Chief Architect, Labs Director, and Technical Evangelist for Geneva/Convergys (telco), Arieso/Viavi (geolocation), and Madge (networking). Phil’s early career was in electronics, and still finds it irresistible to swap from Powerpoint to a soldering iron and a compiler to produce proof-of-concepts when required.
- VP, GM and Fellow, Media Processing Group, Arm
Jem is an Arm Fellow and the General Manager for Arm’s recently formed Machine Learning business, focusing on Machine Learning and Artificial Intelligence solutions. Jem was previously GM and vice-president of technology for the Media Processing Group and Imaging and Vision Groups. In addition to setting the future technology roadmaps for graphics, video, display and imaging, he was also responsible for technological investigations for several acquisitions leading to formation of the Media Processing Group, and most recently Apical, forming the Imaging and Vision Group. Based in Cambridge, Jem has previously been a member of ARM’s Architecture Review Board and he holds four patents in the fields of CPU and GPU design. He has a degree from the University of Cambridge.
Sobia is founder of Cambridge Data Insights, helping organisations to improve performance through the application of AI and machine learning. Also founder of CancerPDX; a ‘Genetic Risk and Refer’ platform for the early identification and management of inherited cancers, Sobia previously worked as a Senior Investment Associate at Invoke Capital. She holds a PhD in Epigenetics from the University of Cambridge, and an MSc in Cognitive Neuroscience from Imperial College London.
- Co-founder & CTO, Graphcore
Simon is co-founder & CTO of Graphcore. Simon has a strong track record as both engineer and entrepreneur, having co-founded and exited two highly successful fabless semiconductor companies, Element14 and Icera, for a combined value of over $1billion. Before that he headed the microprocessor development group at ST Micro. He is well known as a leading microprocessor designer, responsible for ground-breaking designs at STMicro, Element14, and Icera. He holds an MA in Electrical Science from Cambridge University and is the author of 14 issued patents.
Neil Lawrence is the DeepMind Professor of Machine Learning at the University of Cambridge in the Department of Computer Science and Technology. He is co-host of “Talking Machines” podcast and a Visiting Professor at the University of Sheffield. Neil was previously Director of Machine Learning at Amazon in Cambridge.
He received his bachelor’s degree in Mechanical Engineering from the University of Southampton in 1994. Following a period as an field engineer on oil rigs in the North Sea he returned to academia to complete his PhD in 2000 at the Computer Lab in Cambridge University. He spent a year at Microsoft Research in Cambridge before leaving to take up a Lectureship at the University of Sheffield, where he was subsequently appointed Senior Lecturer in 2005. In January 2007 he took up a post as a Senior Research Fellow at the School of Computer Science in the University of Manchester where he worked in the Machine Learning and Optimisation research group. In August 2010 he returned to Sheffield to take up a collaborative Chair in Neuroscience and Computer Science. From 2016 to 2019 he was Director of Machine Learning at Amazon where he world on deploying machine learning solutions for Prime Air, Alexa and in the Amazon supply chain.
Neil’s main research interest is machine learning through probabilistic models. He focuses on both the algorithmic side of these models and their application. He has a particular focus on applications in personalized health and computational biology, but happily dabbles in other areas such as speech, vision and graphics.
Neil was Associate Editor in Chief for IEEE Transactions on Pattern Analysis and Machine Intelligence (from 2011-2013) and is an Action Editor for the Journal of Machine Learning Research. He was the founding editor of the Proceedings of Machine Learning Research (2006) and is currently series editor. He was an area chair for the NIPS conference in 2005, 2006, 2012 and 2013, Workshops Chair in 2010 and Tutorials Chair in 2013. He was General Chair of AISTATS in 2010 and AISTATS Programme Chair in 2012. He was Program Chair of NIPS in 2014 and was General Chair for 2015. He is one of the founders of the Gaussian Process Summer Schoo, the DALI Meeting and Data Science Africa and is a member of the UK’s AI Council.
- Founder & CEO, dividiti
Anton Lokhmotov is CEO and founder of dividiti. The main of focus of dividiti is on Collective Knowledge (CK), an open technology, platform and initiative for accelerating AI R&D by crowdsourcing interdisciplinary design and optimisation knowledge. CK is contributed to by engineers and researchers working on AI applications, software and hardware, across industry and academia in 2010-2015, Anton led development of GPU Compute programming technologies for the ARM Mali GPUs, including production and research compilers, libraries and performance analysis tools. In 2008-2009, Anton was a post-doctoral research associate at Imperial College London. Anton obtained a PhD in Computer Science from the University of Cambridge Computer Laboratory in 2007, and an MSc in Applied Mathematics and Physics from the Moscow Institute of Physics and Technology in 2004.
- Artificial Intelligence Developer Relations, Nvidia
After spending her first 18 months with NVIDIA as a Deep Learning Solutions Architect, Alison is now responsible for NVIDIA's Artificial Intelligence Developer Relations across the EMEA region. She is a mature graduate in Artificial Intelligence combining technical and theoretical computer science with a physics background & over 20 years of experience in international project management, entrepreneurial activities and the internet. She consults on a wide range of AI applications, including planetary defence with NASA, ESA & the SETI Institute and continues to manage the community of AI & Machine Learning researchers around the world, remaining knowledgeable in state of the art across all areas of research. She also travels, advises on & teaches NVIDIA’s GPU Computing platform, around the globe.
- Lead Machine Learning Researcher, BenevolentAI
Daniel Neil is a lead machine learning researcher at BenevolentAI which has key research areas around NLP and machine reading, generative models, and human-centric AI development for drug discovery and chemistry applications. Formerly, Daniel was a research assistant in Kwabena Boahen’s Brains in Silicon Laboratory at Stanford, helping to build the lowest-power neuron supercomputer Neurogrid, and worked as a technical strategy consultant in the San Francisco Bay Area. He was also a co-founder of Ponder, a site to discover intellectual events. Daniel received his B.S. degree in Biomedical Computation from Stanford with a thesis on protein simulation. He subsequently completed his master’s degree in Neural Systems and Computation at ETH Zurich, and received his doctorate there codeveloping hardware and optimized machine learning algorithms. His highest-impact publications have combined neuroscience, hardware, and algorithms to produce novel recurrent neural network algorithms in biologically plausible computing substrates. Currently, his research interests focus on applying deep models to hard problems in generative modelling for chemistry and biology. Specifically, he focuses on analyzing and building deep neural networks designed to cope with small or imbalanced data, combining semi-supervised and generative techniques, and exploring discrete and continuous spaces.
David has a background in mathematics and theoretical physics. He completed a PhD at Durham University and postdoctoral research at the University of Toronto. After a long period in industry managing Quantitative Analysis teams, he returned to research in the areas of machine learning and theoretical neuroscience. His interests are in understanding the type of algorithms that can allow intelligent agents to operate safely and effectively in complex, dynamic environments. This will require a deeper understanding of the statistical properties of learning algorithms, in order to guarantee robustness and safety, and also algorithmic advances to handle the rich problem solving capabilities needed for this kind of behaviour.
- Director, Innovation Development, Magna International
David Paul has served as Director, Corporate Engineering and R&D since 2014. In this role David is responsible for identifying innovation opportunities for Magna from startups, SME’s, Universities, etc.
He has worked in the automotive industry for 38 years, beginning his career with an Engineering Apprenticeship then studied Mechanical Engineering at the University of Loughborough (sponsored by Jaguar Cars Ltd) and later became a core member of Jaguar’s design team for their first V8 engine. Upon leaving Jaguar in 1995, David joined Magna International’s Powertrain division and in 2007 set up his own Consultancy to support SME’s and Innovate UK in the low carbon vehicle sector, before rejoining Magna in 2014.
Carl Edward Rasmussen
- Chairman, Prowler.io, Professor of Machine Learning, University of Cambridge
Prof. Carl Edward Rasmussen is a professor of Machine Learning and head of the Computational and Biological Learning Lab at the Department of Engineering of the University of Cambridge. He is also the Chairman at PROWLER.io. He has extensive interests in probabilistic inference in machine learning, covering unsupervised, supervised and reinforcement learning. He is particularly interested in design and evaluation of nonparametric methods such as Gaussian processes (GPs) and Dirichlet processes. Exact inference in these models is often intractable, so one needs to resort to approximation methods, such as variational techniques or Markov chain Monte Carlo. He has co-authored a book with Chris Williams, entitled "Gaussian Processes for Machine Learning", MIT Press, 2006. Gaussian processes are a principled, practical, probabilistic approach to learning in kernel machines. This standard reference for GPs is accompanied by open source software tools.
- Founder & CTO, Speechmatics
Tony pioneered recurrent neural networks in speech recognition and has built and scaled multiple speech groups and companies. Tony’s passion is the development and application of machine learning to tasks that traditionally had been considered impossible for computers to solve.
- Principal Solutions Architect, Amazon
Cyrus Vahid is a Principal Solutions Architect at AWS Deep Learning. He has a background in Machine Learning and Artificial Intelligence with a focus on Neural Networks. Before joining AWS Cyrus was Principal Domain Architect at Redhat. He is a software specialist with focus on integration and analytics. Over the past 20 years he has delivering innovative solutions with a track-record of success stories mostly in entrepreneurial environments. For the past 3 years he has been dedicated to Big Data initiatives and solutions.
Dr Ian Wassell joined the University of Cambridge Computer Laboratory as a Senior Lecturer in January 2006. Prior to this, he was with the Department of Engineering for six years. He received the PhD degree from the University of Southampton in 1990 and the BSc., BEng. (Honours) Degrees (First Class) from the University of Loughborough in 1983. He has in excess of 25 years experience in radio communication systems gained via positions in industry and academia and has published more than 200 papers. His research interests include broadband wireless networks, wireless sensor networks, radio propagation, coding, communication signal processing, compressive sampling, and image processing and classification.
- Senior Research Scientist, Google DeepMind
Theo is a senior research scientist at DeepMind. His research interests span probabilistic modeling, deep learning and deep reinforcement learning, and fundamentals of imagination and intuitive reasoning in artificial intelligence. Previously, he worked at Lyrics Labs (later acquired by Analog Devices), applying machine learning techniques to physical world problems. He holds an M.S. and Ph.D from MIT in Operations Research and an M.S. from Ecole Centrale Paris in Applied Mathematics.
Peter is founder of Vision Formers, a specialist consultancy that helps visionary technology businesses get product to market and turn their ideas into reality. Peter has a long track record of conceiving, developing and marketing successful technology-based solutions, deployed at scale, globally. Innovative products Peter has brought to market in digital, cloud, AI, consumer electronics and telecommunications have been used by countless millions of people on a daily basis globally, badged by the world’s leading digital and technology brands. Peter is a board member of CW (Cambridge Wireless), and co-leads its Artificial Intelligence special interest group.
- Professor of Information Engineering, University of Cambridge
Steve Young is Professor of Information Engineering at Cambridge University where he has previously served terms as Head of the School of Technology and Senior Pro-Vice Chancellor. His main research interests lie in the area of statistical spoken language systems including speech recognition, speech synthesis and dialogue management. He is the recipient of a number of awards including an IEEE Signal Processing Society Technical Achievement Award, an ISCA Medal for Scientific Achievement and an IEEE James L Flanagan Speech and Audio Processing Award. He is a Fellow of the Royal Academy of Engineering and the Institute of Electrical and Electronics Engineers (IEEE).
In addition to his academic career, he has also founded three successful startups in the Speech Technology area. Entropic Inc was acquired by Microsoft in 1999, Phonetic Arts was acquired by Google in 2010 and VocalIQ was acquired by Apple in 2015. He is now a Senior Member of Technical Staff in the Apple Siri Development team based in Cambridge, UK, a post held jointly with his University professorship.