OATML

Marco is group leader at Tokomak Energy, an Oxfordshire-based nuclear fusion company.

Albert Jiang is a third-year PhD student at the Cambridge computer laboratory, supervised by Mateja Jamnik, working on AI for mathematics. He is also an AI scientist at Mistral AI.

Jonathan Frankle is Chief Scientist (Neural Networks) at Databricks, where he leads the company's research team toward the goal of developing more efficient strategies for training neural networks.

Nicolas is an Assistant Professor at the University of Toronto, in the Department of Electrical and Computer Engineering and the Department of Computer Science.

Francesco is an Associate Researcher at the CS Department at the University of Oxford, hosted by Marta Kwiatkowska.

Steffen is an ELLIS PhD Student and Incoming Group Leader at Helmholtz Munich.

Ari is currently at a Stealth Start-up and previously Senior Staff Research Scientist at FAIR, on intelligent, unsupervised data curation.

Riashat is a PhD candidate working in Machine Learning and Reinforcement Learning, supervised by Doina Precup, co-supervised by Joelle Pineau and co-advised by Yoshua Bengio at Mila - Quebec AI Institute and McGill University in Montreal, Canada.

Alihan is a PhD student in machine learning at the University of Cambridge supervised by Prof Mihaela van der Schaar.

Dr. Gaidon is the Director of Machine Learning at Toyota Research Institute.

Shreya is an Assistant Professor at the Space Robotics Laboratory, Tohoku University, Japan.

Karolina is a Senior research scientist at Google Brain, adjunct professor in the McGill University School of Computer Science, and an associate industry member of Mila, the Quebec AI Institute.

Uri is an Assistant Professor at the Technion - Israel Institute of Technology.

Owain Evans is a Senior Research Fellow in Artificial Intelligence at the University of Oxford.

Stanislav is a PhD candidate at Stanford University working on 1) understanding deep learning, 2) building the science of it, and 3) applying the insights to challenging problems in physics, with a special focus on quantum computing and astrophysics. He received his BA and MSci degrees from the University of Cambridge, and an MS from Stanford University.

I am a fourth year PhD student at New York University working with Andrew Gordon Wilson. I am primarily interested in better understanding Deep Neural Networks and finding more efficient ways of training them. In particular my interests include Bayesian deep learning, loss surface analysis, uncertainty estimation, optimization and regularization in deep learning. I am also excited about deep semi-supervised learning, generative models, Gassian processes and other topics.

Tamara Broderick is an Associate Professor in the Department of Electrical Engineering and Computer Science t MIT.

Siddharth is a PhD student at the University of Cambridge, supervised by Prof Richard Turner. He is interested in designing algorithms that learn sequentially without revisiting past data, are private over user data, and are uncertainty-aware. His recent work has applied Bayesian methods to neural networks in continual (/lifelong) learning and federated learning. He holds a Microsoft Research EMEA PhD Award, and previously completed his MEng at the University of Cambridge.

Chris is a Professor of Cognitive Neuroscience in the Department of Experimental Psychology at the University of Oxford and a Staff Research Scientist at Deepmind.

Greg Yang is a researcher at Microsoft Research AI in Redmond, Washington. He joined MSR after he obtained Bachelor's in Mathematics and Master's degrees in Computer Science from Harvard University, respectively advised by ST Yau and Alexander Rush. He won the Hoopes prize at Harvard for best undergraduate thesis as well as Honorable Mention for the AMS-MAA-SIAM Morgan Prize, the highest honor in the world for an undergraduate in mathematics. He was an invited speaker at the International Congress of Chinese Mathematicians 2019.

As its width tends to infinity, a deep neural network's behavior under gradient descent can become simplified and predictable (e.g. given by the Neural Tangent Kernel (NTK)), if it is parametrized appropriately (e.g. the NTK parametrization). However, we show that the standard and NTK parametrizations of a neural network do not admit infinite-width limits that can learn representations (i.e. features), which is crucial for pretraining and transfer learning such as with BERT. We propose simple modifications to the standard parametrization to allow for feature learning in the limit. Using the *Tensor Programs* technique, we derive explicit formulas for such limits. On Word2Vec and few-shot learning on Omniglot via MAML, two canonical tasks that rely crucially on feature learning, we compute these limits exactly. We find that they outperform both NTK baselines and finite-width networks, with the latter approaching the infinite-width feature learning performance as width increases. More generally, we classify a natural space of neural network parametrizations that generalizes standard, NTK, and Mean Field parametrizations. We show 1) any parametrization in this space either admits feature learning or has an infinite-width training dynamics given by kernel gradient descent, but not both; 2) any such infinite-width limit can be computed using the Tensor Programs technique.

Uri Shalit is an Assistant Professor at the Technion - Israel Institute of Technology. His main research interests are the intersection of machine learning and causal inference, and the application of machine learning in healthcare. Previously, he was a postdoctoral researcher in Prof. David Sontag s Clinical Machine Learning Lab in NYU and then MIT. He completed his PhD studies at the School of Computer Science & Engineering at The Hebrew University of Jerusalem, under the guidance of Prof. Gal Chechik and Prof. Daphna Weinshall.

We will discuss two recent projects where ideas from causal inference have inspired us to find new approaches to problems in machine learning. First, we show how using the idea of independence of cause and mechanism (ICM) can be used to help learn predictive models that are stable against a-priori unknown distributional shifts. Then we will present very recent work where we show how a robust notion of model calibration ties into learning models that generalize well out-of-domain.

Dr. Hanuka works at the intersection of accelerator physics and ML, focusing on the fascinating interplay between them how ML can help us build better physical systems, and how physics can help us build better ML algorithms. Adi joined Stanford National Lab after earning her PhD from the Technion in the field of optical particle accelerators.

Machine learning has been used in various ways to improve accelerator operation including advanced optimization of ac_cel_er_a_tor operating configurations, development of virtual diagnostics to measure beam parameters, and prognostics to detect anomalies and predict failures. In this talk I'll review machine learning techniques currently being investigated at particle accelerator facilities, with specific emphasis on physics-informed active-learning research efforts.

Cynthia is a professor of computer science, electrical and computer engineering, and statistical science at Duke University, and directs the Prediction Analysis Lab, whose main focus is in interpretable machine learning. She is also an associate director of the Statistical and Applied Mathematical Sciences Institute (SAMSI). Previously, Prof. Rudin held positions at MIT, Columbia, and NYU. She holds an undergraduate degree from the University at Buffalo, and a PhD from Princeton University. She is a three-time winner of the INFORMS Innovative Applications in Analytics Award, was named as one of the "Top 40 Under 40" by Poets and Quants in 2015, and was named by Businessinsider.com as one of the 12 most impressive professors at MIT in 2015. She is a fellow of the American Statistical Association and a fellow of the Institute of Mathematical Statistics. Some of her (collaborative) projects are: (1) she has developed practical code for optimal decision trees and sparse scoring systems, used for creating models for high stakes decisions. Some of these models are used to manage treatment and monitoring for patients in intensive care units of hospitals. (2) She led the first major effort to maintain a power distribution network with machine learning (in NYC). (3) She developed algorithms for crime series detection, which allow police detectives to find patterns of housebreaks. Her code was developed with detectives in Cambridge MA, and later adopted by the NYPD. (4) She solved several well-known previously open theoretical problems about the convergence of AdaBoost and related boosting methods. (5) She is a co-lead of the Almost-Matching-Exactly lab, which develops matching methods for use in interpretable causal inference.

"I will present a matching framework for causal inference in the potential outcomes setting called Almost Matching Exactly. This framework has several important elements: (1) Its algorithms create matched groups that are interpretable. The goal is to match treatment and control units on as many covariates as possible, or "almost exactly." (2) Its algorithms create accurate estimates of individual treatment effects. This is because we use machine learning on a separate training set to learn which features are important for matching. The key constraint is that units are always matched on a set of covariates that together can predict the outcome well. (3) Our methods are fast and scalable. In summary, these methods rival black box machine learning methods in their estimation accuracy but have the benefit of being interpretable and easier to troubleshoot."

Alexandre Drouin is a Research Scientist at Element AI in Montreal, Canada and an Adjunct Professor of Computer Science at Laval University. He received a PhD in machine learning from Laval University in 2019 for his work on antibiotic resistance prediction in bacterial genomes. His interests include causal inference, deep learning, and bioinformatics.

Knowledge of the causal structure that underlies a data generating process is essential to answering questions of causal nature. Such questions are abundant in fields that involve decision making such as econometrics, epidemiology, and social sciences. When causal knowledge is unavailable, one can resort to causal discovery algorithms, which attempt to recover causal relationships from data. This talk will present a new algorithm for this task, that combines continuous-constrained optimization with the flexible density estimation capabilities of normalizing flows. In contrast with previous work in this direction, our method combines observational and interventional data to improve the identification of the causal graph. We will present empirical results, along with a theoretical justification of our algorithm.

Been is a staff research scientist at Google Brain. Her research focuses on improving interpretability in machine learning by building interpretability methods for already-trained models or building inherently interpretable models. She gave a talk at the G20 meeting in Argentina in 2019. Her work TCAV received UNESCO Netexplo award, was featured at Google I/O 19' and in Brian Christian's book on "The Alignment Problem". Been has given keynote at ECML 2020, tutorials on interpretability at ICML, University of Toronto, CVPR and at Lawrence Berkeley National Laboratory. She was a co-workshop Chair ICLR 2019, and has been an area chair at conferences including NeurIPS, ICML, ICLR, and AISTATS. She received her PhD. from MIT.

In this talk, I will share some of my reflections on the progress made in the field of interpretable machine learning. We will reflect on where we are going as a field, and what are the things that we need to be aware of to make progress. With that perspective, I will then discuss some of my work on 1) sanity checking popular interpretability methods and 2) developing more lay person-friendly interpretability methods.

Dr. Carl is a senior lecturer at the University of Cambridge. Together with Prof. Neil Lawrence, Dr. Ferenc Huszar and Jessica Montgomery he leads the newly formed machine learning research group in the Cambridge computer lab. He is interested in building models that allows for principled treatment of uncertainty and interpretability.

Deep learning studies functions represented as compositions of other functions. While there is ample evidence that these type of structures are beneficial for algorithmic design there are significant questions if the same is true when used to build statistical models. In this talk I will try to highlight some of the issues that are inherent to compositional functions. I will talk about the identifiablity issues that, while beneficial for predictive algorithms becomes challenging when building models. Rather than a talk providing solutions my aim is to highlight some issues related to models of compositional functions and aim to stimulate a discussion around these topics. I will however provide some initial results on compositional uncertainty to highlight some of the paths that we are currently exploring.

Amy is a final year PhD candidate at McGill University and the Mila Institute, co-supervised by Profs. Joelle Pineau and Doina Precup. Amy is also a researcher at Facebook AI Research. Amy's work focuses on bridging theory and practice through learning approximate state abstractions and learning representations for generalization in reinforcement learning. Amy previously obtained an M.Eng. in EECS and dual B.Sci. degrees in Mathematics and EECS from MIT.

We study how representation learning can accelerate reinforcement learning from rich observations, such as images, without relying either on domain knowledge or pixel-reconstruction. Our goal is to learn representations that both provide for effective downstream control and invariance to task-irrelevant details. Bisimulation metrics quantify behavioral similarity between states in continuous MDPs, which we propose using to learn robust latent representations which encode only the task-relevant information from observations. Our method trains encoders such that distances in latent space equal bisimulation distances in state space. We provide theoretical guarantees for the learned approximate abstraction and extend this notion to families of tasks with varying dynamics.

Stanislav Fort is a PhD candidate at Stanford University working on 1) understanding deep learning, 2) building the science of it, and 3) applying the insights to challenging problems in physics, with a special focus on quantum computing and astrophysics. He received his BA and MSci degrees from the University of Cambridge, and an MS from Stanford University. Stanislav spent a year as a full time AI researcher at Google Research, working primarily with Google Brain, and is about to start as a research scientist intern at DeepMind.

Neural Network Loss Landscapes in High Dimensions: Taylor Expansions, the Neural Tangent Kernel, and the Nonlinear Advantage,"Deep neural networks trained with gradient descent have been extremely successful at learning solutions to a broad suite of difficult problems across a wide range of domains such as vision, gameplay, and natural language, many of which had previously been considered to require intelligence. Despite their tremendous success, we still do not have a detailed, predictive understanding of how these systems work. In this talk, I will focus on recent efforts to understand the structure of deep neural network loss landscapes and how gradient descent navigates them during training. In particular, I will discuss a phenomenological approach to modelling their large-scale structure [1], the role of their nonlinear nature in the early phases of training [2], and its effects on ensembling and calibration. [3,4] [1] Stanislav Fort, and Stanislaw Jastrzebski. Large Scale Structure of Neural Network Loss Landscapes. Advances in Neural Information Processing Systems 32 (NeurIPS 2019). arXiv 1906.04724 [2] Stanislav Fort et al. "Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel". NeurIPS 2020. arXiv 2010.15110 [3] Stanislav Fort, Huiyi Hu, Balaji Lakshminarayanan. "Deep Ensembles: A Loss Landscape Perspective." arXiv 1912.02757 [4] Marton Havasi et al. "Training independent subnetworks for robust prediction". arXiv 2010.06610

Andrew Gordon Wilson is faculty in the Courant Institute and Center for Data Science at NYU. Before joining NYU, he was an assistant professor at Cornell University from 2016-2019. He was a research fellow in the Machine Learning Department at Carnegie Mellon University from 2014-2016, and completed his PhD at the University of Cambridge in 2014. Andrew's interests include probabilistic modelling, scientific computing, Gaussian processes, Bayesian statistics, and loss surfaces and generalization in deep learning. His webpage is https://cims.nyu.edu/~andrewgw.

To answer scientific questions, and reason about data, we must build models and perform inference within those models. But how should we approach model construction and inference to make the most successful predictions? How do we represent uncertainty and prior knowledge? How flexible should our models be? Should we use a single model, or multiple different models? Should we follow a different procedure depending on how much data are available? In this talk I will present a philosophy for model construction, grounded in probability theory. I will exemplify this approach with methods that exploit loss surface geometry for scalable and practical Bayesian deep learning. I will also discuss recent results on prior specification and tempering in Bayesian deep learning. The talk will primarily be based on two references: (1) https://arxiv.org/abs/2002.08791 (2) https://arxiv.org/abs/2003.02139

Ofir Nachum is a Research Scientist at Google Research in Mountain View, California. His research focuses on deep reinforcement learning, including applications of convex duality as well as hierarchical and offline RL. He received his Bachelor's and Master's from MIT.

We review basic concepts of convex duality and summarize how this duality may be applied to a variety of reinforcement learning (RL) settings, including policy evaluation, policy optimization, and uncertainty estimation. The derivations yield a number of intriguing results and insights, such as new mechanisms for incorporating off-policy learning into RL and straightforward applications of standard concentration inequalities to long-term value estimation. Through a number of such examples, we hope to highlight the utility of convex duality as a new perspective on old problems. At the same time we hope to equip the audience with the appropriate tools to be able to further apply these same ideas in different ways and on new applications.

Maximilian is an Associate Professor of Economics here at Oxford. His research interests include, statistical decision theory (applied to experimental design, machine learning, policy choice, and empirical Bayes methods), statistics as a social process (publication bias etc.), the use of economic theory in econometrics, identification and causality, and economic inequality and (optimal) taxation.

This talk will provide a survey of several papers on the theory and practice of experimental design. I will compare different objectives (estimator precision, outcomes of participants, informing policy choice to maximize average outcomes, and informing policy choice to maximize utilitarian welfare), and​ their implications for experimental design. I will consider heuristic algorithms, will prove approximate optimality results for some of these algorithms, and will discuss several empirical applications.

Rohin is a PhD student at the Center for Human-Compatible AI (CHAI) at UC Berkeley. He is interested in how we can build AI systems that do what we intend them to do, rather than what we literally specify. He writes the Alignment Newsletter, a weekly publication with recent content relevant to AI alignment that has over 1700 subscribers. He has received the Tong Leong Lim Pre-Doctoral Prize, the Berkeley Fellowship, and the NSF Fellowship.

Deep reinforcement learning has emerged as a powerful tool to create AI systems that perform intelligent, complex behaviors -- in environments with good simulators to generate lots of data, and perfect reward functions. However, challenges arise when we try to build AI agents that assist humans. First, since the human is part of the environment, we no longer have a perfect simulator. To address this challenge, we learn a model of the human using imitation learning, and argue that due to the cooperative nature of the task, the imperfections of the learned model do not significantly affect the agent learned through deep RL. Second, the human often cannot specify a perfect reward function that captures good behavior: there are many ways in which behaviors can go wrong, and enumerating all of these is far too difficult. For this, we introduce a preference learning algorithm that can correct many such errors, simply by analyzing the state in which the agent finds itself, without requiring any extra human annotations. While there is much work yet to be done, we believe this is a significant step towards agents that can assist humans in realistic settings.

Zachary is an assistant professor at Carnegie Mellon University appointed in both the Machine Learning Department and Tepper School of Business. His research spans core machine learning methods and their social impact and addresses diverse application areas, including clinical medicine and natural language processing. Current research focuses include robustness under distribution shift, breast cancer screening, the effective and equitable allocation of organs, and the intersection of causal thinking with messy data. He is the founder of the Approximately Correct blog and the creator of Dive Into Deep Learning, an interactive open-source book drafted entirely through Jupyter notebooks. Find on Twitter (@zacharylipton) or GitHub (@zackchase).

We might hope that when faced with unexpected inputs, well-designed software systems would fire off warnings. However, ML systems, which depend strongly on properties of their inputs (e.g. the i.i.d. assumption), tend to fail silently. Faced with distribution shift, we wish (i) to detect and (ii) to quantify the shift, and (iii) to correct our classifiers on the fly—when possible. This talk will describe a line of recent work on tackling distribution shift. First, I will focus on recent work on label shift, a more classic problem, where strong assumptions enable principled methods. Then I will discuss how recent tools from generative adversarial networks have been appropriated (and misappropriated) to tackle dataset shift—characterizing and (partially) repairing a foundational flaw in the method. Finally, I will discuss new work that leverages human-in-the-loop feedback to develop classifiers that take into account causal structure in text classification problem and appear (empirically) to benefit on a battery of out-of-domain evaluations.

Smitha is a 3rd-year PhD student at UC Berkeley. She is mainly interested in problems that have to do with figuring out what the right objective for a system should be, often relying on frameworks of causality and measurement theory in doing so. She is funded by an Open Philanthropy AI Fellowship and a NSF Graduate Research Fellowship.

It is often difficult to hand-specify what the correct reward function is for a task, and thus, researchers have instead aimed to learn reward functions from human behavior or feedback. The types of behavior interpreted as evidence of the reward function have expanded greatly in recent years. For example, researchers have even suggested that the reward specified by a human is merely a source of information about the true reward function. We can only imagine that there are many other types of human behavior that have yet to be formalized as evidence of the reward. How will robots make sense of all these diverse types of behavior? Our insight is that different types of behavior can be interpreted in a single unifying formalism - as a reward-rational (implicit) choice. The formalism offers a unifying lens with which to view existing work, as well as provides a recipe for interpreting new types of behavior. Furthermore, the formalism shows how we can properly interpret combinations of human feedback, in particular, it reveals that in many cases interpreting combinations of feedback requires interpreting the type of feedback the person chooses as a reward-rational choice in itself.

Hisham is a doctoral student at the Australian National University and CSIRO Data61, based in Sydney, Australia. His research includes topics from Optimal Transport, Generative Models, Fairness and Privacy and more generally, answering questions that build on the theoretical understanding of machine learning. Currently, Hisham is interning at Amazon Cambridge, working on the Supply Chain Optimization Team (SCOT).

Since the introduction of Generative Adversarial Networks (GANs) and Variational Autoencoders (VAE), the literature on generative modelling has witnessed an overwhelming resurgence. The impressive, yet elusive empirical performance of GANs has lead to the rise of many GAN-VAE hybrids, with the hopes of GAN level performance and additional benefits of VAE, such as an encoder for feature reduction, which is not offered by GANs. Recently, the Wasserstein Autoencoder (WAE) was proposed, achieving performance similar to that of GANs, yet it is still unclear whether the two are fundamentally different or can be further improved into a unified model. In this work, we study the $f$-GAN and WAE models and make two main discoveries. First, we find that the $f$-GAN and WAE objectives partake in a primal-dual relationship and are equivalent under some assumptions, which then allows us to explicate the success of WAE. Second, the equivalence result allows us to, for the first time, prove generalization bounds for Autoencoder models, which is a pertinent problem when it comes to theoretical analyses of generative models. Furthermore, we show that the WAE objective is related to other statistical quantities such as the $f$-divergence and in particular, upper bounded by the Wasserstein distance, which then allows us to tap into existing efficient (regularized) optimal transport solvers. Our findings thus present the first primal-dual relationship between GANs and Autoencoder models, comment on generalization abilities and make a step towards unifying these models.

George Papamakarios is a research scientist at DeepMind London, working on probabilistic modelling, inference, and model-based reinforcement learning. Prior to joining DeepMind, he did a PhD in machine learning at the University of Edinburgh, and an MSc in advanced computing at Imperial College London. His PhD focused on density estimation and likelihood-free inference with normalizing flows, and introduced various new normalizing-flow models and likelihood-free inference methods.

Adi Shamir is an Israeli cryptographer. He is a co-inventor of the Rivest–Shamir–Adleman (RSA) algorithm, a co-inventor of the Feige–Fiat–Shamir identification scheme, one of the inventors of differential cryptanalysis, and has made numerous contributions to the fields of cryptography and computer science.

Max Jaderberg is a senior research scientist at DeepMind driving the intersection of deep learning, reinforcement learning, and multi-agent systems. His recent work includes creating the first agent to beat human professionals at StarCraft II, and creating algorithms for training teams of agents to play with humans in first-person video games. He previously co-founded Vision Factory, a computer vision startup, which was acquired by Google in 2014, and completed a PhD at the Visual Geometry Group, University of Oxford.

Florian is a PhD student in machine learning advised by Marius Kloft at Humboldt-Universität zu Berlin since 2015. He is interested in Bayesian approximate inference and works with Stephan Mandt (UCI) and Manfred Opper (TU Berlin). He did a scientific internship at Disney Research in Pittsburgh (USA) in 2017 focusing on approximate Bayesian methods applied to NLP problem. In September 2015, he received a Master’s degree (M.Sc.) in mathematics from Humboldt-Universität zu Berlin.

Marc Rußwurm received his Bachelor and Master degrees in Geodesy and Geoinformation at the Technical University of Munich (TUM) in 2014 and 2018 where he focused on Remote Sensing and Earth Observation. In the final years of his studies, he combined methods of computer vision and natural language processing for analysis of multi-temporal satellite images. His work was published in multiple machine learning and remote sensing conferences and journals and his publication at the Earthvision 2017 workshop at the CVPR conference received the best paper award. In 2018 he continued his research as Ph.D. Candidate at the TUM Chair of Remote Sensing Technology and conducted multiple international research experience at the Polish Space Research center, as a participant at the Frontier Development Lab at ESA Rome and Oxford, and at the IRISA Obelix Lab in France.

Joel Z. Leibo is a research scientist at DeepMind, where he has worked since 2013. A computational neuroscientist by training, his recent work focuses on modeling social phenomena like the tragedy of the commons using multi-agent deep reinforcement learning techniques.

Luba Elliott is a curator and researcher specialising in artificial intelligence in the creative industries. She is currently working to educate and engage the broader public about the latest developments in AI art through talks, workshops and exhibitions at venues across the art and technology spectrum including The Photographers’ Gallery, Victoria & Albert Museum, Seoul MediaCity Biennale, Impakt Festival, Leverhulme Centre for the Future of Intelligence, NeurIPS and ECCV. Her Creative AI London meetup community includes 2,300+ members. She has advised organisations including The World Economic Forum, Google and City University on the topic and was featured on the BBC, Forbes and The Guardian. She is a member of the AI council at the British Interactive Media Association. Previously, she worked in startups and venture capital and has a degree in Modern Languages from Cambridge University.

David Balduzzi works on game theory and machine learning at DeepMind. His PhD was in representation theory and algebraic geometry at the university of Chicago. He then worked on computational neuroscience at UW-madison and machine learning at the MPI for intelligent Systems, ETH Zurich and Victoria University in Wellington.

Simon Kohl holds a Masters in Physics from the Karlsruhe Institute of Technology (KIT) where he specialised in statistical analyses of high-energy particle decays. In 2016 he started his PhD in Computer Science jointly with the German Cancer Research Center in Heidelberg and KIT. His focus has been on automatic segmentation of prostate cancer in MRI images using deep nets. This task is interesting and hard as the delineation of prostate cancer is highly ambiguous, which results in very noisy expert annotations. In 2017/2018 he interned with DeepMind where he worked on generative models for semantic segmentation.

Sir Timothy John Berners-Lee OM KBE FRS FREng FRSA FBCS is an English computer scientist best known as the inventor of the World Wide Web. He is a Professorial Fellow of Computer Science at the University of Oxford and a professor at the Massachusetts Institute of Technology (MIT). Berners-Lee proposed an information management system on 12 March 1989, then implemented the first successful communication between a Hypertext Transfer Protocol (HTTP) client and server via the internet in mid-November.