[edit]

• The Atomic Human at St Andrews’ Distinguished Lecture Series
• The Atomic Human at Churchill College Science Society
• Decision Making in the Era of Generative AI at Healthy Lifespan Institute Annual Meeting
• Communications and Digital Committee at Houses of Paliament, London, UK
• What is the Future for Probability in the Era of Generative AI? at ELLIS Summer School, Cambridge
• Data First Culture at Advanced Leadership Programme, Judge Business School, Cambridge
• Being Human in the Age of AI at Pint of Science, Cambridge
• Harnessing Data Science for Africa’s Socio-Economic Development at DSA, Kigali, Rwanda
• Use or be Used: Regaining Control of AI at Strachey Lecture, Oxford
• AI needs to serve people, science, and society at Cambridge AI Social, West Hub, Cambridge
• AI needs to serve people, science, and society at 5th Public Sector Innovation Conference
• at
• Caught by Surprise Retrospective: The Mechanistic Fallacy and Modelling How We Think at ELLIS Unconference
• Understanding Artificial Intelligence at New Court, St Swithin’s Lane
• How Engineers Solve Big and Difficult Problems Part 1: The Challenges/Opportunities Presented to Engineers by AI/ML at The Engineer in Society
• Data First Culture at Advanced Leadership Programme, Judge Business School, Cambridge
• AI for Science: An Oral Report from a Recent Dagstuhl Workshop at AI Seminar, Cambridge Computer Laboratory
• Emulation at GPSS, Sheffield
• Organisational Data Science at CDEI Away Day
• Data First Culture at Advanced Leadership Programme, Judge Business School, Cambridge
• Deep Gaussian Processes: A Motivation and Introduction at Sheffield ML Network
• The AI Paradigm Shift: Machine Learning, Automated Decision Making and Modern Society at Four Seasons, Park Lane, London
• Data First Culture at Advanced Leadership Programme, Judge Business School, Cambridge
• Understanding Artificial Intelligence at Bristol Data and AI Showcase
• Deep Gaussian Processes: A Motivation and Introduction at Heilbronn Data Science Seminar, Jean Golding Institute
• The NeurIPS Experiment at Swiss National Science Foundation
• AI and Data Trusts at Advanced Topics of Law and Technology, University of Graz
• My Experience with AI at East Anglian Radiological Society Annual Meeting
• Post-Digital Transformation, Decision Making and Intellectual Debt at Cambridge Senior Management Programme, Judge Business School, University of Cambridge
• Artificial Intelligence: Reclaiming Control at LT1, William Gates Building, Cambridge Festival
• Leveraging Opportunities of the 4th Industrial Revolution to Develop Successful Careers in the field of Healthcare: the pains and gains at MedXMentor Inaugural Kick-off and Launch Event, Mbarara, Uganda
• Organisational Data Science at 10ds Show and Social
• AI4ER Lecture: Projects at Wolfson Lecture Theatre, Madingley Rise Site
• Mind and Machine Intelligence at Comenius Program, Harley Mason Room, Corpus Christi College
• Data First Culture at Advanced Leadership Programme, Jesus College, Cambridge
• Deep Gaussian Processes: A Motivation and Introduction at LG Distinguished Talk
• Data Governance for Ethical AI at Virtual DSN AI Bootcamp
• AI, Data Science and the Covid19 Pandemic at The Stokes Society, Pembroke College
• Access, Assess and Address: A Pipeline for (Automated?) Data Science at ECML Workshop on Automating Data Science
• Emulation at Virtual GPSS
• Machine Learning and the Physical World at Tuebingen ML in Science Conference
• Machine Learning and the Physical World at Trustworthy AI Conference, Moscow
• A Retrospective on the 2014 NeurIPS Experiment at Computer Lab Seminar Series
• AI Can’t Fix This: Happenstance Data, Modelling, and the Covid19 Pandemic at The Flip Side of the Pandemic: Recent Advances in the Mathematics of Information
• Post-Digital Transformation: Intellectual Debt at Cambridge Senior Management Programme, Judge Business School, University of Cambridge
• Machine Learning and the Physical World at Data Centric Engineering
• AI Faith Panel Discussion at Religion Media Centre: Spiritual Succour and Artificial Intelligence
• Auto AI: Resolving Intellectual Debt in Complex Systems at The Turing Presents: AI UK
• Interpretable Models at Virtual Interaction with Machine Learning Course
• Uncertainty, Procrastination and Artificial Intelligence at Queens’ College SCR Talk
• Laplace’s Gremlin: Uncertainty and Artificial Intelligence at Celsius Lecture, Uppsala University, Sweden
• AI and the Future of Work at Hot Spots Movement AI and Future of Work Event
• Introduction to Machine Intelligence at Accelerate Science Winter School
• Data Trusts Salon at Ostrom Workshop
• When Scientists Work with Government at The Leader’s Institute Interview
• AutoAI: Systems, Machine Learning and Mathematics at Isaac Newton Institute Virtual Christmas Dinner
• Accelerate-Spark Information Session at Virtual
• Auto AI and Machine Learning Systems Design at UK Systems Research, Virtual Seminar Series
• Policy, Science and the Convening Power of Data at AI and data science in the age of COVID-19
• Science, Evidence and Government; Reflections on the Covid-19 Experience at Bennett Institute for Policy
• Data Sharing and Data Trusts at Wellcome Human Cell Atlas Meeting
• Deploying Machine Learning: Intellectual Debt and AutoAI at Virtual Advances in Data Science Seminar, Manchester
• AI and Data Science at Autumn Data Science School, Cambridge
• Will AI Make the Workplace - Wherever it is - More Equal? at Virtual Cambridge Alumni Event
• Deep GPs at Virtual Gaussian Process Summer School
• FIT Machine Learning Systems at Huawei-Cambridge Workshop
• Introduction to Machine Learning Systems at Virtual DSA
• Open Challenges for Automated Machine Learning: Solving Intellectual Debt with AutoAI at ICML Workshop on Automated Machine Learning
• Future of AI and Machine Learning at Executive MBA Course, Judge Business School, University of Cambridge
• The Great AI Fallacy at Webinar for The Cambridge Network
• Intellectual Debt and the Death of the Programmer at BBC Lectures on ML
• Intellectual Debt and the Death of the Programmer at Distinguished Lecture Series on Information Engineering
• R250: GP Intro at Computer Lab, University of Cambridge
• Communication and Remote Working at FPE Meeting, McGrath Centre, University of Cambridge
• Coconut Science and the Supply Chain of Ideas at Lunchtime Talk, Cambridge Intellect Leadership Programme – Digital Transformation by Design
• Machine Learning and Emergency Medicine at The Royal College for Emergency Medicine, London
• From Innovation to Deployment at The Alan Turing Institute Fellows Welcome Event
• Perspectives on AI at Sheffield ML Meetup, Kollider, Castle House, Castle Street, Sheffield
• Naive Days at University of Sheffield
• Real World Machine Learning Challenges at AstraZeneca Data Science Meetup
• Debating IBM’s Project Debater at Cambridge Union
• Post Digital Transformation at Google Cloud Day, Judge Business School, University of Cambridge
• R250: GP Intro at Computer Lab, University of Cambridge
• What is Artificial Intelligence? at Council on Ethics, The Norwegian Government Pension Fund
• Data First Culture at Advanced Leadership Programme, Judge Business School, University of Cambridge
• Machine Learning Systems Design at Cambridge AI Group Seminar
• AutoAI at 10 Minute Talk, Wednesday Meeting, Department of Computer Science and Technology, University of Cambridge
• From Data Subject to Data Citizen at Global Forum on AI for Humanity, Paris
• From Innovation to Deployment at Data Science Africa, Ashesi University
• What is Machine Learning? at Data Science Africa Summer School, Ashesi, Ghana
• The Future of AI at Autumn Data Science School, Cambridge
• Machine Learning Systems Design at Re-work Deep Learning Summit
• Introduction to Deep Gaussian Processes at Gaussian Process Summer School, University of Sheffield, UK
• Interpretable End-to-End Learning at Sheffield ML Group Research Retreat
• Machine Learning and Data Science at Department of Housing, Commmunities and Local Government
• Machine Learning Systems Design at Data Science Africa, Addis Ababa
• What is Machine Learning? at Data Science Africa Summer School, Addis Ababa, Ethiopia
• Narrowing the Intelligence Gap at CW AI SIG Byte-size Event
• Meta-Modelling and Deploying ML Software at The Mathematics of Deep Learning and Data Science
• Modern Data Oriented Programming at Advances and Challenges in Machine Learning Languages, Centre for Mathematical Sciences, Cambridge
• What is AI and What are the Implications of Advances in AI for Religion? at AI - Ethical and Religious Perspectives, Jesus College, Cambridge
• Towards Machine Learning Systems Design at Mathematical Genomics Away Day
• Digital Disruption at Future of Work, Chatham House
• Data Readiness Levels at Data Bootcamp
• Faith and AI: Introduction to Machine Learning at Faith and AI Event, St George's House, Windsor
• Data Readiness Levels at Data Readiness Levels Workshop, Jesus College, Oxford
• Towards Machine Learning Systems Design at Department of Computing Science, University of Glasgow
• Data Science and Digital Systems at Stu Hunter Resesearch Conference, Milan
• Deep Gaussian Processes at MLSS, Stellenbosch, South Africa
• Gaussian Processes at MLSS, Stellenbosch, South Africa
• Machine Learning and the Physical World at Center for Statistics and Machine Learning, Princeton
• Data Science and Digital Systems at Fifth International Workshop on Sustainable Road Freight, Cambridge
• The Three Ds of Machine Learning at Data Science Africa, Abuja
• Bayesian Methods at DSA, Abuja
• Fairness and Diversity of Decision Making at Center for the Advanced Studies of the Behavioral Sciences, Joint American Academy and Royal Society Workshop
• Machine Learning and the Physical World at Uber AI Labs
• Mind and Machine Intelligence at FinTECHTalents 2018
• You and AI Panel Debate at Manchester Science Festival
• Natural and Artificial Intelligence at Amazon Innovation Day
• fAIth at Landor Associates, London
• Data Science and the Professions at IRSG Ethics, Privacy, Transparency and Bias in Information Retrieval, Sheffield
• Introduction to Gaussian Processes at Gaussian Process Summer School, Sheffield
• Probabilistic Machine Learning at AI Saturdays, Lagos, Nigeria
• at
• Faith and AI at Faith and AI Event, St George’s House, Windsor
• Uncertainty in Loss Functions at IWCV 2018, Modena, Italy
• Outlook for UK AI and Machine Learning at HM Treasury
• Towards Machine Learning Systems Design at Sheffield ML Research Network Launch
• Decision Making and Diversity at CFI Lunchtime Seminar
• Challenges for Data Science in Healthcare at A.I. and Health Regulation Meeting
• Natural and Artificial Intelligence at Amazon Thursday Thoughts
• Data Science: Time for Professionalisation? at LSE Workshop on Data Science Theory and Practice
• Machine Learning and Data Readiness Levels at
• Deep Probabilistic Modelling with with Gaussian Processes at NIPS Tutorial 2017
• Personalized Health: Challenges in Data Science at CWI Lectures in Machine Learning
• Embodiment Factors and Privacy at The Israel Academy of Science and Humanities
• Embodiment Factors and Privacy at The Israel Academy of Science and Humanities
• Data Science: Time for Professionalisation? at Open Data Science Conference, London
• Living Together at TEDx Exeter
• Where Next for AI? at CWTech AI Conference
• Introduction to Gaussian Processes at Gaussian Process Summer School, Sheffield
• Cloaking Functions: Differential Privacy with Gaussian Processes at CD-Make 2017 Keynote, Reggio Calabria, Italy
• What is Machine Learning? at Data Science Africa, Arusha, Tanzania
• Once Upon a Universal Standard Time: Embodiment and AI Narratives at CFI Annual Conference
• Data Analytics Perspectives: Machine Learning at CSaP 2017 Conference, The Royal Society
• Machine Learning, Technology and the Future of Intelligence at Leverhulme CFI
• Probabilistic Dimensionality Reduction at Probabilistic Scientific Computing Workshop at ICERM, Brown
• Peppercorns and Machine Learning System Design at Sheffield ML Research Retreat
• Peppercorns and Machine Learning System Design at Sheffield ML Group Research Retreat
• The Data Science Process at AMLC Data Science Process Workshop
• The Data Science Process at DALI Data Science Process Workshop
• Machine Learning and the Data Science Process at OxWaSP Workshop, Amazon, Berlin
• The rise of the algorithm - artificial intelligence, ethics, trust and tech development at The Guardian Changing Media Summit
• Ethics, Computer Systems and the Professions at UCL Gustave Tuck Theatre, London WC1
• Challenges and Opportunities in Machine Learning and Artificial Intelligence at ARM, Cambridge
• Challenges for Delivering Machine Learning in Health at Deep Learning in Healthcare Summit, London
• Three Challenges in Data Science at Advanced Data Analytics Seminars, Data Science Institute, University of Manchester
• Latent Variable Models with Gaussian Processes at STOR-i Masterclass and University of Lancaster Seminar
• Introduction to Gaussian Processes at STOR-i Masterclass
• Covariance Functions and the Marginal Likelihood at STOR-i Masterclass
• Personalized Health: Challenges in Data Science at SMPGD 2017
• The Data Landscape at Defra Science Advisory Council: Data Sub Group
• Personalized Health: Challenges in Data Science at NIPS Workshop on Machine Learning for Health
• Computational Perspectives: Fairness and Awareness in the Analysis of Data at Fisher Trust, Royal Society and London Mathematical Society Joint Meeting
• Three Challenges for Open Data Science at Open Data Science Conference
• The Data Delusion at Rework Deep Learning Summit, London, UK
• The Challenges of Data Science at European Network for Business and Industrial Statistics (ENBIS) 2016, Sheffield
• Fitting Covariance and Multioutput Gaussian Processes at GPSS, Sheffield
• Introduction to Gaussian Processes at GPSS, Sheffield
• Data Science: Where Computation and Statistics Meet? at Royal Statistical Society Conference, Manchester
• Communicating Machine Learning at Symposium on Communicating Machine Learning, Edinburgh
• Variational Compression and Deep Gaussian Processes at MLSS, Arequipa
• Probabilistic Dimensionality Reduction with Gaussian Processes at MLSS, Arequipa
• Introduction to Gaussian Processes at MLSS, Arequipa
• Introduction to Gaussian Processes II at MLSS, Arequipa
• Privacy and Learning at Workshop on Security, Workroom 2, Diamond Building, Sheffield
• Machine Learning and the Professions at Royal Society, London
• New Directions in Data Science at Data Science in Africa Workshop, UN Global Pulse, Kampala, Uganda
• Introduction to Data Science and Machine Learning at Data Science in Africa Summer School, Makerere University
• System Zero: What Kind of AI Have We Created? at Future of Humanity Institute, Oxford Martin School
• Machine Learning and the Future of Work at Cambridge Centre for Science and Policy
• What Kind of AI Have We Created? at A Pint of Science
• Data Efficiency and Machine Learning at Entropy Day, University of Sheffield
• Introduction to Gaussian Processes II at MLSS, Cadiz
• Introduction to Gaussian Processes at MLSS, Cadiz
• Beyond Backpropagation: Uncertainty Propagation at ICLR 2016, San Jaun, Puerto Rico
• Machine Learning with Gaussian Processes at Amazon Machine Learning Conference, Seattle
• Beyond Backpropagation: Uncertainty Propagation at Microsoft Research, New England, USA
• Variational Inference in Deep GPs at Microsoft Research, New England, USA
• Probabilistic Dimensionality Reduction at Facebook London, UK
• The Data Delusion: Challenges for Democratising Deep Learning at Deep Learning Summit, London, UK
• The Data Delusion at MARS Conference, Parker Palm Springs, Palm Springs, CA
• What Kind of AI have we Created? at Science Week Talk, University of Sheffield
• What Kind of AI have we Created? at Birley Community College, Sheffield
• Future Debates: This House Believes an Artificial Intelligence will Benefit Society at British Science Association Future Debates, Coffee Revolution, University of Sheffield
• Machine Learning with Gaussian Processes at OxWaSP Symposium, University of Warwick
• What kind of AI have we created? at Sheffield Computer Science Welcome Event
• The Open Data Science Initiative at data@sheffield
• Special Topics: Gaussian Processes at University of Sheffield
• The Mechanistic Fallacy and Modelling How We Think at NIPS Workshop on Statistical Methods for Understanding Neural Systems
• Logistic Regression and GLMs at University of Sheffield
• Probabilistic Classification: Naive Bayes at University of Sheffield
• Information Infrastructure for Health at ATI Scoping Workshop on the Data Analytics Pipeline, Edinburgh
• Bayesian Regression at University of Sheffield
• Generalization: Model Validation at University of Sheffield
• What Kind of Artificial Intelligence are we Creating? at Cognitive Science work in Progress Meeting, Sheffield
• Machine Learning Tutorial: Probabilistic Dimensionality Reduction II at Imperial College, U.K.
• What Kind of Artificial Intelligence have we Created? at The Data Hide, The Hide, Scotland Street, Sheffield
• Basis Functions at University of Sheffield
• Personalised Health and Gaussian Processes at Stratified Medical, 40 Churchway, London, NW1
• Linear Algebra and Linear Regression at University of Sheffield
• The Digital Oligarchy: Information, Knowledge and the Internet Era at Right First Time, ControlPoint Event
• Objective Functions: A Simple Example with Matrix Factorisation at University of Sheffield
• Probability and an Introduction to Jupyter, Python and Pandas at University of Sheffield
• Peer Review and The NIPS Experiment at MLPM Summer School, Museum of Science and Industry, Manchester, UK
• Deep Gaussian Processes at HIPS Group, SEAS, Harvard University
• Personalized Health with Gaussian Processes at Microsoft Research, New England
• Latent Force Models: Bridging the Divide between Mechanistic and Data Modelling Paradigms at MPI for Intelligent Systems, Stuttgart
• Gaussian Processes (Part III) at MLSS, Tübingen
• Gaussian Processes (Part II) at MLSS, Tübingen
• Gaussian Processes (Part I) at MLSS, Tübingen
• Panel Discussion at ICML Deep Learning Worshop, Lille, France
• Large Scale Learning in Gaussian Processes at Large-Scale Kernel Learning Workshop @ICML2015
• Deep Gaussian Processes at Deep Learning Workshop @ICML2015
• Personalized Health at Dedan Kimathi University, Nyeri, Kenya
• Regression at Data Science Africa School, Dedan Kimathi University, Nyeri, Kenya
• Introduction to Machine Learning and Data Science at Data Science Africa School, Dedan Kimathi University, Nyeri, Kenya
• Deep Gaussian Processes at 2nd Deep Learning Workshop, Edinburgh
• Deep Gaussian Processes at Computer Science Colloquium, NYU
• Deep Gaussian Processes at KTH Royal Institute of Technology, Sweden
• Deep Gaussian Processes at IDA Machine Learning Seminars, Linkoping, Sweden
• Deep Gaussian Processes at Mascot Num 2015, St Etienne, France
• Modelling in the Context of Massively Missing Data at Max Planck Institute, Tübingen
• The Data Farm at Westwoodside Primary School, Nether Gate, Westwoodside, Doncaster
• Data Science: A New Field or Just a Rebadging Exercise? at School of Mathematical Sciences, University of Nottingham
• Machine Learning Tutorial: Probabilistic Dimensionality Reduction at Imperial College, U.K.
• The Data Farm at Sheffield Festival of Engineering and Science, Jessop West Exhibition Space, Jessop West, University of Sheffield, 1 Upper Hanover Street, Sheffield S3 7RA
• Introduction to Gaussian Processes at MLSS, Sydney
• The NIPS Experiment at RADIANT Meeting, University of Zurich, Switzerland
• Deep Gaussian Processes at Instituto Italiano de Tecnologia, Genova, Italy
• Data Science: A New Field or Just a Rebadging Exercise? at Department of Statistics, University of Warwick
• Statistical Computing: Python at Royal Statistical Society, London, U.K.
• Approximate Inference in Deep GPs at Gatsby Computational Neuroscience Unit, University College London, U.K.
• Deep Gaussian Processes at UCL-Duke University Workshop on Sensing and Analysis of High-Dimensional Data
• Big Data and Open Data Science at UCLID Workshop, University of Lancaster, UK
• Flexible Parametric Representations of Non Parametric Models at Informatics Forum, University of Edinburgh, UK
• Visualizing Biological Data with Gaussian Processes at The Systems Biology Modelling Cycle, EBI, Hinxton, UK
• Gaussian Processes for Dynamic Modelling at The Systems Biology Modelling Cycle, EBI, Hinxton, UK
• What is Machine Learning? A Probabilistic Perspective (Part II) at MLSS, Reykjavik, Iceland
• What is Machine Learning? A Probabilistic Perspective (Part I) at MLSS, Reykjavik, Iceland
• Flexible Parametric Representations of Non Parametric Models at Statistical Machine Learning in Paris, France
• Applications of Gaussian Processes in Computational Biology at Institute Curie, Paris, France
• Modelling with Massively Missing Data at Facebook, Menlo Park, CA
• Flexible Parametric Representations of Non Parametric Models at Gatsby Computational Neuroscience Unit, University College London, U.K.
• Personalized Health with Gaussian Processes at Universidad Nacional de Colombia, Sede Manizales, Colombia
• Deep Gaussian Processes at Oxford University Statistics Department
• New Perspectives on Variational Approximations in Gaussian Processes: Modelling Data at University of Cambridge, Engineering Department
• Latent Variable Models with Gaussian Processes at Gaussian Process Winter School, Sheffield
• Fitting Covariance and Multi-output Gaussian Processes at Gaussian Process Winter School, Sheffield
• Introduction to Gaussian Processes at Gaussian Process Winter School, Sheffield
• Unravelling the Big Data Revolution at Maths Department, Teaching Away Day, University of Leeds
• Unravelling the Data Revolution with Machine Learning at Newcastle, Edinburgh, Cambridge and Sheffield academic Departments of Respiratory Medicine Meeting, Whitely Hall, Sheffield
• Personalized Health with Gaussian Processes at Disease Mapping Workshop, Leahurst
• Deep Health: Machine Learning for Personalized Medicine at E4L Away Day
• Probabilistic Approaches for Computational Biology and Medicine at Machine Learning for Personalized Medicine Summer School
• A Unifying Probabilistic Perspective on Spectral Approaches to Dimensionality Reduction at Microsoft Research, Cambridge
• Deep Gaussian Processes at Microsoft Research, Cambridge
• Deep Gaussian Processes at Natural Computing Applications Forum, University of Oxford
• Deep Health at Manchester and Sheffield Machine Learning Meetings
• Latent Force Models: Introduction at Latent Force Model Workshop, Sheffield
• Unsupervised Learning with Gaussian Processes at Gaussian Process Summer School, Sheffield
• Multioutput Gaussian Processes at Gaussian Process Summer School, Sheffield
• Introduction to Gaussian Processes at Gaussian Process Summer School, Sheffield
• Deep Gaussian Processes at University of Cambridge, Engineering Department
• How the Planets Affect Our Daily Lives: A Brief History of Uncertainty at King Edward's School, Sheffield
• Deep Learning: What is it and What are We doing About it? at University of Sheffield
• How the Planets Affect Our Daily Lives: A Brief History of Uncertainty at St Wilfrid's Primary School, Sheffield
• How the Planets Affect Our Daily Lives: A Brief History of Uncertainty at Birley Community College, Sheffield
• Variational Gaussian Processes at Max Planck Institute, Tübingen, Germany
• Deep Gaussian Processes at Max Planck Institute, Tübingen, Germany
• Deep Gaussian Processes at University College, London
• Deep Gaussian Processes at Aalto University, Finland
• Reproducible Research: Lessons from Machine Learning at RADIANT Kick-off Meeting, Manchester, UK
• Machine Learning and the Life Sciences: from Modelling to Medicine at Department of Infection and Immunity, University of Sheffield
• Life, the Universe and Machine Learning at St George’s Church Lecture Theatre, University of Sheffield
• Model Based Target Identification from Expression Data at UCLA
• A Brief Introduction to Gaussian Processes at UCLA
• Bridging the Gap Between Computational Biology and Systems Biology at Pathologists Society Summer Meeting, Sheffield
• Kernels for Vector Valued Functions at ICML Workshop on ``Object, functional and structured data: towards next generation kernel-based methods''
• Everything You Want to Know About Gaussian Processes: Gaussian Process Regression at CVPR Tutorial, Providence, RI, USA
• Everything You Want to Know About Gaussian Processes: Multioutput Covariances and Mechanistic Models at CVPR Tutorial, Providence, RI, USA
• Gaussian Processes in Computational Biology Tutorial: Session 2 at BioPreDyn Workshop, CRG, Barcelona, Spain
• Gaussian Processes in Computational Biology Tutorial: Multioutput Gaussian Processes and Mechanistic Models at BioPreDyn Workshop, CRG, Barcelona, Spain
• Latent Force Models: Bridging the Divide between Mechanistic and Data Modelling Paradigms at Computer Science Department, University of Liverpool, U.K.
• What is Machine Learning? at La Palma, Canary Islands
• Nonlinear Probabilistic Dimensionality Reduction at La Palma, Canary Islands
• Spectral Approaches to Dimensionality Reduction at La Palma, Canary Islands
• Dimensionality Reduction: Motivation and Linear Models at La Palma, Canary Islands
• Latent Force Models: Combining the Mechanistic and Data Driven Modelling Paradigms at Rank Prize Workshop, Grasmere, Lake District
• Latent force models: Combining Probabilistic and Mechanistic Modelling at Robotics Research Group Seminar, Department of Engineering Science, University of Oxford
• A Unifying Review of Spectral Methods for Dimensionality Reduction at Robotics Research Group Tutorial, Department of Engineering Science, University of Oxford
• Model Based Target Identification from Expression Data at Cambridge Research Institute, Cancer Research UK
• at BioPreDyn Launch Workshop, CRG, Barcelona
• A Maximum Entropy Perspective on Spectral Dimensionality Reduction at Machine Learning @ CUED, University of Cambridge, U.K.
• Model Based Target Identification from Expression Data at Gene Expression Profiling Workshop, Liverpool, UK
• Between Systems and Data-driven Modeling for Computational Biology: Target Identification with Gaussian Processes at ABCD2011, Ravenna, Italy
• Latent Force Models at Bayes 250 Workshop, University of Edinburgh, U.K.
• Gaussian Processes and Probabilistic Models for Dimensionality Reduction at Schloss Dagstuhl, Germany
• Model Based Target Identification from Expression Data at Krebs Institute Symposium
• A unifying probabilistic perspective on spectral approaches to dimensionality reduction at Hausdorff Research Institute for Mathematics, Workshop on Manifold Learning, University of Bonn
• Advanced Use of Gaussian Processes at University of Siena, Italy
• Introduction to Gaussian Processes at Mathematics and Computer Science, University of Siena, Italy
• Latent Force Models at Mathematics and Computer Science, University of Exeter, U.K.
• Probabilistic Dimensional Reduction with the Gaussian Process Latent Variable Model at Department of Computer Science, University of Loughgborough, U.K.
• A Unifying Probabilistic Perspective on Spectral Approaches to Dimensionality Reduction at ANC/DTC Seminar, School of Informatics, University of Edinburgh, U.K.
• Between Systems and Data-driven Modeling for Computational Biology: Target Identification with Gaussian Processes at SMPGD2010, Paris, France
• A Probabilistic Perspective on Spectral Dimensionality Reduction at Challenges of Data Visualization Workshop, NIPS 2010, Whistler
• A Probabilistic Perspective on Spectral Dimensionality Reduction at AAAI Fall Symposium on Manifold Methods, U.S.A.
• Latent Force Models at Dynamics Research Group Seminar, Mechanical Engineering, University of Sheffield, U.K.
• A Probabilistic Perspective on Spectral Dimensionality Reduction at Department of Statistics, Aalto University, Finland
• Bayesian approaches to Transcription Factor Target Identification at Course in Practical Systems Biology: Visualisation and Reverse engineering gene regulatory networks, EuroMediterranean University Centre of Ronzano, Bologna, Italy
• Making Implementations Available for the Research Community at Validation in Statistics and Machine Learning, Weierstrass Institute, Berlin, Germany
• Latent Force Models at Functional Phylogenies: Evolutionary Inference for Functional Data, University of Oxford, U.K.
• PRIB Tutorial: Gaussian Processes and Gene Regulation at PRIB2010, Radboud University, Nijmegen, Netherlands
• Between Systems and Data-driven Modeling for Computational Biology: Target Identification with Gaussian Processes at IBSB2010, Kyoto University, Japan
• Latent Force Models at Inference Group, Cavendish Laboratory, University of Cambridge
• Transfer Learning and Multiple Output Kernel Functions at NIPS 09 Workshop on Transfer Learning for Structured Data
• Latent Force Models at Disordered Systems Group, Department of Mathematics, King's College London
• Nonlinear Response in Gaussian Process Models of Transcription at Telethon Institute of Genetics and Medicine, Naples, Italy
• Model Based Target Identification from Gene Expression with Gaussian Processes at BioDN@work '09, Computational Biology \& Bioinformatics, University of Naples ``Federico II''
• Latent Force Models at Computer Science Colloquium, NYU, U.S.A.
• Latent Force Models at Google Research, New York, U.S.A.
• Model Based Target Identification from Gene Expression with Gaussian Processes at School of Public Health, Johns Hopkins University, U.S.A.
• Latent Force Modelling with Gaussian Processes at School of Mathematics and Statistics, University of Newcastle, U.K.
• Latent Force Models with Gaussian Processes at Inspire Workshop, Imperial College, U.K.
• Efficient Multiple Output Convolution Processes for Multiple Task Learning at Inf Workshop, University of Warwick, U.K.
• Dealing with High Dimensional Data with Dimensionality Reduction at Interspeech Tutorial, Brighton, U.K.
• Latent Force Models and Multiple Output Gaussian Processes at Statistics and Learning Interface Meeting, University of Manchester, U.K.
• Latent Force Models with Gaussian Processes at Pattern Recognition Applications Group, Department of Electrical and Electronic Engineering, University of Cagliari, Italy
• Non-linear Matrix Facorization with Gaussian Proceses at European Modern Massive Datasets Workshop, Denmark Techinical University, Copenhagen
• An Introduction to Systems Biology from a Machine Learning Perspective II at TISE Summer School, Tampere, Finland
• An Introduction to Systems Biology from a Machine Learning Perspective at TISE Summer School, Tampere, Finland
• Non-linear Matrix Factorization with Gaussian Processes at Learning Workshop, Clearwater, Florida
• Estimation of Multiple Transcription Factor Activities using ODEs and Gaussian Processes at Workshop on Learning and Inference in Computational and Systems Biology (LICSB)
• Python in Machine Learning at MLO Lunch, Kilburn 2.15
• GP-LVM for Data Consolidation at NIPS Workshop on Learning from Multiple Sources
• Latent Force Models with Gaussian Processes at Intelligent Systems Seminars, University of Bristol, U.K.
• Inference in Ordinary Differential Equations with Latent Functions through Gaussian Processes at RSS Manchester Local Group
• Dynamics with Gaussian Processes at Natural Computing Applications Forum, University of Sheffield, U.K.
• Ambiguity Modelling in Latent Spaces at Machine Learning for Multimodal Interaction, Utrecht, The Netherlands
• Latent Force Models with Gaussian Processes at Bayesian Research Kitchen, Grasmere, Lake District, U.K.
• Statistical inference in systems biology through Gaussian processes and ordinary differential equations at LICSB Workshop, University of Warwick, U.K.
• Statistical Inference in Systems Biology through Gaussian Processes and Ordinary Differential Equations at Max Planck Society, Ringberg Castle, Germany
• An Introduction to Systems Biology from a Machine Learning Perspective at Max Planck Society, Ringberg Castle, Germany
• Inferring Latent Functions with Gaussian Processes in Differential Equations at Department of Electronics and Computer Science, University of Southampton, U.K.
• Learning and Inference with Gaussian Processes: An Overview of Bayesian Inference and Gaussian Processes at Data Modelling Series, University of Sheffield, U.K.
• Human Motion Modelling with Gaussian Processes at Netwon Institute, Cambridge, U.K.
• Human Motion Modelling through Dimensional Reduction with Gaussian Processes at Hotel Golf, Bled, Slovenia
• TP1: Leveraging Complex Prior Knowledge in Learning at Hotel Golf, Bled, Slovenia
• Dimensionality Reduction at EPSRC Winter School, University of Sheffield, Sheffield, U.K.
• Exploiting Dimensional Dreduction In Modelling Of High Dimensional Distributions at
• Latent Variables, Differential Equations and Gaussian Processes at Microsoft Research, Cambridge, U.K.
• Modelling Transcriptional Regulation with Gaussian Processes at Parameter Estimation Workshop, Manchester Interdisciplinary Biocentre, University of Manchester, U.K.
• Towards Computational Systems Biology with a Statistical Analysis Pipeline for Microarray Data at Department of Molecular Biology and Biotechnology, University of Sheffield, U.K.
• Latent Variable Modelling with Gaussian Processes at Workshop on Probabilistic formulation of the inverse problem and application to image reconstruction, Neuroscience Research Institute, University of Manchester, U.K.
• Probabilistic Dimensional Reduction with the Gaussian Process Latent Variable Model at Dept of Signal Theory and Communications, Universidad Carlos III de Madrid, Spain
• Probabilistic Inference for Modelling of Transcription Factor Activity at Dept of Signal Theory and Communications, Universidad Carlos III de Madrid, Spain
• Fast Sparse Gaussian Process Methods: The Informative Vector Machine at Dept of Signal Theory and Communications, Universidad Carlos III de Madrid, Spain
• Hierarchical Gaussian Process Latent Variable Models at ICML, Corvallis, Oregon
• Probabilistic Inference for Modelling of Transcription Factor Activity at Gatsby Computational Neuroscience Unit, University College London, U.K.
• Gaussian Processes for Inference in Biological Interaction Networks at Exploring the Interface Between Mathematics and Bioscience, Manchester Interdisciplinary Biocentre, University of Manchester, U.K.
• Modelling Transcriptional Regulation with Gaussian Processes at Parameter Estimation in Systems Biology, School of Computer Science, University of Manchester, U.K.
• Probabilistic Dimensional Reduction with the Gaussian Process Latent Variable Model at Neural Computing Research Group, Aston University, U.K.
• Probabilistic Dimensional Reduction with the Gaussian Process Latent Variable Model at Google Research, New York, N.Y., U.S.A.
• Probabilistic Dimensional Reduction with the Gaussian Process Latent Variable Model at Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, MA, U.S.A.
• Learning and Inference with Gaussian Processes: An Overview of Gaussian Processes and the GP-LVM at University of Manchester, Machine Learning Course Guest Lecture
• Learning and Inference with Gaussian Processes at Intel Research, Seattle, U.S.A.
• PUMA: Propagation of Uncertainty in Microarray Analysis at Max Planck Institute, Tübingen, Germany
• Probabilistic Dimensional Reduction with the Gaussian Process Latent Variable Model at Erice Workshop on Mathematics and Medical Diagnosis, Sicily, Italy
• Local Distance Preservation in the GP-LVM through Back Constraints at International Conference on Machine Learning, Pittsburgh, U.S.A.
• Learning and Inference with Gaussian Processes at School of Computer Science, University of Manchester, U.K.
• A probabilistic dynamical model for quantitative inference of the regulatory mechanism of transcription at Mathematical and Statistical Aspects of Molecular Biology
• Probabilistic Dimensional Reduction with the Gaussian Process Latent Variable Model at Information Engineering, University of Cambridge, U.K.
• Computer Vision Reading Group: The Gaussian Process Latent Variable Model at Computer Vision Reading Group, Visual Geometry Group, Department of Engineering Science, University of Oxford, U.K.
• High Dimensional Probabilistic Modelling through Manifolds at University of Washington, Seattle, U.S.A.
• High Dimensional Probabilistic Modelling through Manifolds at Microsoft Research, Redmond, U.S.A.
• High Dimensional Probabilistic Modelling through Manifolds at Electronic Arts Speaker Series, University of British Columbia, Canada
• High Dimensional Probabilistic Modelling through Manifolds at Columbia University, New York, U.S.A.
• High Dimensional Probabilistic Modelling through Manifolds at IBM Thomas J Watson Research Center, New York, U.S.A.
• High Dimensional Probabilistic Modelling through Manifolds at Gatsby Computational Neuroscience Unit, University College London, U.K.
• High Dimensional Probabilistic Modelling through Manifolds at IDIAP Research Institute, Martigny, Switzerland
• High Dimensional Probabilistic Modelling through Manifolds at School of Computer and Communication Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
• Probabilistic Non-linear Component Analysis through Gaussian Process Latent Variable Models at Max Planck Institute, Tübingen, Germany
• Probabilistic Non-linear Component Analysis through Gaussian Process Latent Variable Models at Department of Electronics and Computer Science, University of Southampton, U.K.
• Probabilistic Non-linear Component Analysis through Gaussian Process Latent Variable Models at Microsoft Research, Cambridge, U.K.
• Probabilistic Non-linear Component Analysis through Gaussian Process Latent Variable Models at School of Computer Science, University of Manchester, U.K.
• Probabilistic Non-linear Component Analysis through Gaussian Process Latent Variable Models at Institute for Adaptive and Neural Computation, University of Ediburgh, U.K.
• Probabilistic Non-linear Component Analysis through Gaussian Process Latent Variable Models at Robotics Research Group, Department of Engineering Science, University of Oxford, U.K.
• Probabilistic Non-linear Component Analysis through Gaussian Process Latent Variable Models at Sheffield Machine Learning Workshop, Sheffield, U.K.
• Probabilistic Non-linear Component Analysis through Gaussian Process Latent Variable Models at University of California, Berkeley, U.S.A.
• Bayesian Processing of cDNA Microarray Images through the Variational Importance Sampler at Microsoft Research, Redmond, U.S.A.
• Bayesian Processing of cDNA Microarray Images at The University of Sussex, Department of Cognitive Science, Bioinformatics and Vision Seminars
• Bayesian Processing of cDNA Microarray Images at The University of Manchester, Department of Computer Science, Bio-health sciences Seminars
• Particle Filters, Variational methods and Importance Sampling at Machine Learning and Perception Group, Microsoft Research, Cambridge, U.K.

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